[ { "path": ".dockerignore", "content": "build/\ndata/\nnotebooks/\ntests/\ndataset/" }, { "path": ".gitattributes", "content": "*.csv filter=lfs diff=lfs merge=lfs -text\ntests/validation_data/*.csv -filter=lfs -diff=lfs -merge=lfs -text\n*.catboost filter=lfs diff=lfs merge=lfs -text\n*.h5 filter=lfs diff=lfs merge=lfs -text\n*.cbm filter=lfs diff=lfs merge=lfs -text\n" }, { "path": ".github/workflows/build.yml", "content": "name: Build\n\non: [push]\n\njobs:\n build:\n runs-on: ubuntu-latest\n\n steps:\n - uses: actions/checkout@v1\n\n - name: Install dependencies\n run: |\n conda env create --file environment.yaml\n source /usr/share/miniconda/etc/profile.d/conda.sh\n conda activate ml-ids\n pip install -e .\n\n - name: Static Type Check\n run: |\n source /usr/share/miniconda/etc/profile.d/conda.sh\n conda activate ml-ids\n make typecheck\n\n - name: Code Quality Check\n run: |\n source /usr/share/miniconda/etc/profile.d/conda.sh\n conda activate ml-ids\n make lint-errors\n\n - name: Test with pytest\n run: |\n source /usr/share/miniconda/etc/profile.d/conda.sh\n conda activate ml-ids\n make test\n" }, { "path": ".github/workflows/deployment.yml", "content": "name: Deploy Model on AWS Sagemaker\n\non:\n deployment\n\njobs:\n deploy:\n name: Deploy\n runs-on: ubuntu-latest\n\n steps:\n - name: Checkout\n uses: actions/checkout@v1\n\n - name: Set Tag in Environment\n id: set-aws-tag\n run: |\n if [ -z \"$GITHUB_REF\" ]\n then\n echo \"No Tag given. Workflow may only be run on tagged commits.\"\n exit 1\n fi\n echo \"::set-output name=awstag::$(echo ${GITHUB_REF:10} | sed 's/[^a-zA-Z0-9]/-/g')\"\n\n - name: Set up Python 3.7\n uses: actions/setup-python@v1\n with:\n python-version: 3.7\n\n - name: Install Python dependencies\n run: |\n python -m pip install --upgrade pip\n pip install click==7.0\n pip install boto3==1.10.28\n pip install mlflow==1.4.0\n\n - name: Configure AWS credentials\n uses: aws-actions/configure-aws-credentials@v1\n with:\n aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}\n aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}\n aws-region: eu-west-1\n\n - name: Login to Amazon ECR\n id: login-ecr\n uses: aws-actions/amazon-ecr-login@v1\n\n - name: Deploy model on AWS Sagemaker\n id: deploy-model\n env:\n AWS_TAG: ${{ steps.set-aws-tag.outputs.awstag }}\n run: |\n make sagemaker_deploy JOB_ID=\"ml-ids-sagemaker-$AWS_TAG\"\n" }, { "path": ".github/workflows/train.yml", "content": "name: Train Model on AWS Sagemaker\n\non:\n push:\n tags:\n - 'm*'\n\njobs:\n train:\n name: Deploy\n runs-on: ubuntu-latest\n\n steps:\n - name: Checkout\n uses: actions/checkout@v1\n\n - name: Set Tag in Environment\n id: set-aws-tag\n run: |\n if [ -z \"$GITHUB_REF\" ]\n then\n echo \"No Tag given. Workflow may only be run on tagged commits.\"\n exit 1\n fi\n echo \"::set-output name=awstag::$(echo ${GITHUB_REF:10} | sed 's/[^a-zA-Z0-9]/-/g')\"\n\n - name: Set up Python 3.7\n uses: actions/setup-python@v1\n with:\n python-version: 3.7\n\n - name: Install Python dependencies\n run: |\n python -m pip install --upgrade pip\n pip install click==7.0\n pip install pandas==0.25.2\n pip install sagemaker==1.44.3\n\n - name: Configure AWS credentials\n uses: aws-actions/configure-aws-credentials@v1\n with:\n aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}\n aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}\n aws-region: eu-west-1\n\n - name: Login to Amazon ECR\n id: login-ecr\n uses: aws-actions/amazon-ecr-login@v1\n\n - name: Build, tag, and push image to Amazon ECR\n id: build-image\n env:\n ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}\n ECR_REPOSITORY: ml-ids-train-sagemaker\n IMAGE_TAG: ${{ github.sha }}\n AWS_TAG: ${{ steps.set-aws-tag.outputs.awstag }}\n run: |\n docker build -f models/gradient_boost/envs/sagemaker/container/Dockerfile -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG .\n docker tag $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG $ECR_REGISTRY/$ECR_REPOSITORY:$AWS_TAG\n docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG\n docker push $ECR_REGISTRY/$ECR_REPOSITORY:$AWS_TAG\n echo \"::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG\"\n\n - name: Train the packaged model on AWS Sagemaker\n id: train-model\n env:\n IMAGE_NAME: ${{ steps.build-image.outputs.image }}\n AWS_TAG: ${{ steps.set-aws-tag.outputs.awstag }}\n run: |\n make sagemaker_train_aws \\\n SAGEMAKER_TRAIN_CONFIG_PATH=models/gradient_boost/envs/sagemaker/configs/train-gpu.json \\\n SAGEMAKER_IMAGE_NAME=$IMAGE_NAME \\\n TRAIN_PARAM_PATH=models/gradient_boost/training_params.json \\\n JOB_ID=\"ml-ids-sagemaker-$AWS_TAG\"\n" }, { "path": ".gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndataset/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\npip-wheel-metadata/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py,cover\n.hypothesis/\n.pytest_cache/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n.python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n#Pipfile.lock\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# Catboost\ncatboost_info\n" }, { "path": ".idea/.gitignore", "content": "\n# Default ignored files\n/workspace.xml" }, { "path": ".idea/deployment.xml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": ".idea/inspectionProfiles/profiles_settings.xml", "content": "\n \n \n" }, { "path": ".idea/misc.xml", "content": "\n\n \n \n \n" }, { "path": ".idea/ml-ids.iml", "content": "\n\n \n \n \n \n \n \n \n \n \n \n \n" }, { "path": ".idea/modules.xml", "content": "\n\n \n \n \n \n \n" }, { "path": ".idea/vcs.xml", "content": "\n\n \n \n \n" }, { "path": ".pylintrc", "content": "[MASTER]\n\n# A comma-separated list of package or module names from where C extensions may\n# be loaded. Extensions are loading into the active Python interpreter and may\n# run arbitrary code.\nextension-pkg-whitelist=\n\n# Add files or directories to the blacklist. They should be base names, not\n# paths.\nignore=CVS\n\n# Add files or directories matching the regex patterns to the blacklist. The\n# regex matches against base names, not paths.\nignore-patterns=\n\n# Python code to execute, usually for sys.path manipulation such as\n# pygtk.require().\n#init-hook=\n\n# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the\n# number of processors available to use.\njobs=1\n\n# Control the amount of potential inferred values when inferring a single\n# object. This can help the performance when dealing with large functions or\n# complex, nested conditions.\nlimit-inference-results=100\n\n# List of plugins (as comma separated values of python module names) to load,\n# usually to register additional checkers.\nload-plugins=\n\n# Pickle collected data for later comparisons.\npersistent=yes\n\n# Specify a configuration file.\n#rcfile=\n\n# When enabled, pylint would attempt to guess common misconfiguration and emit\n# user-friendly hints instead of false-positive error messages.\nsuggestion-mode=yes\n\n# Allow loading of arbitrary C extensions. Extensions are imported into the\n# active Python interpreter and may run arbitrary code.\nunsafe-load-any-extension=no\n\n\n[MESSAGES CONTROL]\n\n# Only show warnings with the listed confidence levels. Leave empty to show\n# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED.\nconfidence=\n\n# Disable the message, report, category or checker with the given id(s). You\n# can either give multiple identifiers separated by comma (,) or put this\n# option multiple times (only on the command line, not in the configuration\n# file where it should appear only once). You can also use \"--disable=all\" to\n# disable everything first and then reenable specific checks. For example, if\n# you want to run only the similarities checker, you can use \"--disable=all\n# --enable=similarities\". If you want to run only the classes checker, but have\n# no Warning level messages displayed, use \"--disable=all --enable=classes\n# --disable=W\".\ndisable=print-statement,\n parameter-unpacking,\n unpacking-in-except,\n old-raise-syntax,\n backtick,\n long-suffix,\n old-ne-operator,\n old-octal-literal,\n import-star-module-level,\n non-ascii-bytes-literal,\n raw-checker-failed,\n bad-inline-option,\n locally-disabled,\n file-ignored,\n suppressed-message,\n useless-suppression,\n deprecated-pragma,\n use-symbolic-message-instead,\n apply-builtin,\n basestring-builtin,\n buffer-builtin,\n cmp-builtin,\n coerce-builtin,\n execfile-builtin,\n file-builtin,\n long-builtin,\n raw_input-builtin,\n reduce-builtin,\n standarderror-builtin,\n unicode-builtin,\n xrange-builtin,\n coerce-method,\n delslice-method,\n getslice-method,\n setslice-method,\n no-absolute-import,\n old-division,\n dict-iter-method,\n dict-view-method,\n next-method-called,\n metaclass-assignment,\n indexing-exception,\n raising-string,\n reload-builtin,\n oct-method,\n hex-method,\n nonzero-method,\n cmp-method,\n input-builtin,\n round-builtin,\n intern-builtin,\n unichr-builtin,\n map-builtin-not-iterating,\n zip-builtin-not-iterating,\n range-builtin-not-iterating,\n filter-builtin-not-iterating,\n using-cmp-argument,\n eq-without-hash,\n div-method,\n idiv-method,\n rdiv-method,\n exception-message-attribute,\n invalid-str-codec,\n sys-max-int,\n bad-python3-import,\n deprecated-string-function,\n deprecated-str-translate-call,\n deprecated-itertools-function,\n deprecated-types-field,\n next-method-defined,\n dict-items-not-iterating,\n dict-keys-not-iterating,\n dict-values-not-iterating,\n deprecated-operator-function,\n deprecated-urllib-function,\n xreadlines-attribute,\n deprecated-sys-function,\n exception-escape,\n comprehension-escape,\n R0903,\n\n# Enable the message, report, category or checker with the given id(s). You can\n# either give multiple identifier separated by comma (,) or put this option\n# multiple time (only on the command line, not in the configuration file where\n# it should appear only once). See also the \"--disable\" option for examples.\nenable=c-extension-no-member\n\n\n[REPORTS]\n\n# Python expression which should return a score less than or equal to 10. You\n# have access to the variables 'error', 'warning', 'refactor', and 'convention'\n# which contain the number of messages in each category, as well as 'statement'\n# which is the total number of statements analyzed. This score is used by the\n# global evaluation report (RP0004).\nevaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10)\n\n# Template used to display messages. This is a python new-style format string\n# used to format the message information. See doc for all details.\n#msg-template=\n\n# Set the output format. Available formats are text, parseable, colorized, json\n# and msvs (visual studio). You can also give a reporter class, e.g.\n# mypackage.mymodule.MyReporterClass.\noutput-format=text\n\n# Tells whether to display a full report or only the messages.\nreports=no\n\n# Activate the evaluation score.\nscore=yes\n\n\n[REFACTORING]\n\n# Maximum number of nested blocks for function / method body\nmax-nested-blocks=5\n\n# Complete name of functions that never returns. When checking for\n# inconsistent-return-statements if a never returning function is called then\n# it will be considered as an explicit return statement and no message will be\n# printed.\nnever-returning-functions=sys.exit\n\n\n[TYPECHECK]\n\n# List of decorators that produce context managers, such as\n# contextlib.contextmanager. Add to this list to register other decorators that\n# produce valid context managers.\ncontextmanager-decorators=contextlib.contextmanager\n\n# List of members which are set dynamically and missed by pylint inference\n# system, and so shouldn't trigger E1101 when accessed. Python regular\n# expressions are accepted.\ngenerated-members=Blues\n\n# Tells whether missing members accessed in mixin class should be ignored. A\n# mixin class is detected if its name ends with \"mixin\" (case insensitive).\nignore-mixin-members=yes\n\n# Tells whether to warn about missing members when the owner of the attribute\n# is inferred to be None.\nignore-none=yes\n\n# This flag controls whether pylint should warn about no-member and similar\n# checks whenever an opaque object is returned when inferring. The inference\n# can return multiple potential results while evaluating a Python object, but\n# some branches might not be evaluated, which results in partial inference. In\n# that case, it might be useful to still emit no-member and other checks for\n# the rest of the inferred objects.\nignore-on-opaque-inference=yes\n\n# List of class names for which member attributes should not be checked (useful\n# for classes with dynamically set attributes). This supports the use of\n# qualified names.\nignored-classes=optparse.Values,thread._local,_thread._local\n\n# List of module names for which member attributes should not be checked\n# (useful for modules/projects where namespaces are manipulated during runtime\n# and thus existing member attributes cannot be deduced by static analysis). It\n# supports qualified module names, as well as Unix pattern matching.\nignored-modules=\n\n# Show a hint with possible names when a member name was not found. The aspect\n# of finding the hint is based on edit distance.\nmissing-member-hint=yes\n\n# The minimum edit distance a name should have in order to be considered a\n# similar match for a missing member name.\nmissing-member-hint-distance=1\n\n# The total number of similar names that should be taken in consideration when\n# showing a hint for a missing member.\nmissing-member-max-choices=1\n\n# List of decorators that change the signature of a decorated function.\nsignature-mutators=\n\n\n[MISCELLANEOUS]\n\n# List of note tags to take in consideration, separated by a comma.\nnotes=FIXME,\n XXX,\n TODO\n\n\n[LOGGING]\n\n# Format style used to check logging format string. `old` means using %\n# formatting, `new` is for `{}` formatting,and `fstr` is for f-strings.\nlogging-format-style=old\n\n# Logging modules to check that the string format arguments are in logging\n# function parameter format.\nlogging-modules=logging\n\n\n[STRING]\n\n# This flag controls whether the implicit-str-concat-in-sequence should\n# generate a warning on implicit string concatenation in sequences defined over\n# several lines.\ncheck-str-concat-over-line-jumps=no\n\n\n[BASIC]\n\n# Naming style matching correct argument names.\nargument-naming-style=snake_case\n\n# Regular expression matching correct argument names. Overrides argument-\n# naming-style.\n#argument-rgx=\n\n# Naming style matching correct attribute names.\nattr-naming-style=snake_case\n\n# Regular expression matching correct attribute names. Overrides attr-naming-\n# style.\n#attr-rgx=\n\n# Bad variable names which should always be refused, separated by a comma.\nbad-names=foo,\n bar,\n baz,\n toto,\n tutu,\n tata\n\n# Naming style matching correct class attribute names.\nclass-attribute-naming-style=any\n\n# Regular expression matching correct class attribute names. Overrides class-\n# attribute-naming-style.\n#class-attribute-rgx=\n\n# Naming style matching correct class names.\nclass-naming-style=PascalCase\n\n# Regular expression matching correct class names. Overrides class-naming-\n# style.\n#class-rgx=\n\n# Naming style matching correct constant names.\nconst-naming-style=UPPER_CASE\n\n# Regular expression matching correct constant names. Overrides const-naming-\n# style.\n#const-rgx=\n\n# Minimum line length for functions/classes that require docstrings, shorter\n# ones are exempt.\ndocstring-min-length=-1\n\n# Naming style matching correct function names.\nfunction-naming-style=snake_case\n\n# Regular expression matching correct function names. Overrides function-\n# naming-style.\n#function-rgx=\n\n# Good variable names which should always be accepted, separated by a comma.\ngood-names=i,\n j,\n k,\n f,\n ex,\n df,\n X,\n X_train,\n X_val,\n X_test,\n y,\n Run,\n _\n\n# Include a hint for the correct naming format with invalid-name.\ninclude-naming-hint=no\n\n# Naming style matching correct inline iteration names.\ninlinevar-naming-style=any\n\n# Regular expression matching correct inline iteration names. Overrides\n# inlinevar-naming-style.\n#inlinevar-rgx=\n\n# Naming style matching correct method names.\nmethod-naming-style=snake_case\n\n# Regular expression matching correct method names. Overrides method-naming-\n# style.\n#method-rgx=\n\n# Naming style matching correct module names.\nmodule-naming-style=snake_case\n\n# Regular expression matching correct module names. Overrides module-naming-\n# style.\n#module-rgx=\n\n# Colon-delimited sets of names that determine each other's naming style when\n# the name regexes allow several styles.\nname-group=\n\n# Regular expression which should only match function or class names that do\n# not require a docstring.\nno-docstring-rgx=^_\n\n# List of decorators that produce properties, such as abc.abstractproperty. Add\n# to this list to register other decorators that produce valid properties.\n# These decorators are taken in consideration only for invalid-name.\nproperty-classes=abc.abstractproperty\n\n# Naming style matching correct variable names.\nvariable-naming-style=snake_case\n\n# Regular expression matching correct variable names. Overrides variable-\n# naming-style.\n#variable-rgx=\n\n\n[VARIABLES]\n\n# List of additional names supposed to be defined in builtins. Remember that\n# you should avoid defining new builtins when possible.\nadditional-builtins=\n\n# Tells whether unused global variables should be treated as a violation.\nallow-global-unused-variables=yes\n\n# List of strings which can identify a callback function by name. A callback\n# name must start or end with one of those strings.\ncallbacks=cb_,\n _cb\n\n# A regular expression matching the name of dummy variables (i.e. expected to\n# not be used).\ndummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_\n\n# Argument names that match this expression will be ignored. Default to name\n# with leading underscore.\nignored-argument-names=_.*|^ignored_|^unused_\n\n# Tells whether we should check for unused import in __init__ files.\ninit-import=no\n\n# List of qualified module names which can have objects that can redefine\n# builtins.\nredefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io\n\n\n[SPELLING]\n\n# Limits count of emitted suggestions for spelling mistakes.\nmax-spelling-suggestions=4\n\n# Spelling dictionary name. Available dictionaries: none. To make it work,\n# install the python-enchant package.\nspelling-dict=\n\n# List of comma separated words that should not be checked.\nspelling-ignore-words=\n\n# A path to a file that contains the private dictionary; one word per line.\nspelling-private-dict-file=\n\n# Tells whether to store unknown words to the private dictionary (see the\n# --spelling-private-dict-file option) instead of raising a message.\nspelling-store-unknown-words=no\n\n\n[FORMAT]\n\n# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.\nexpected-line-ending-format=\n\n# Regexp for a line that is allowed to be longer than the limit.\nignore-long-lines=^\\s*(# )??$\n\n# Number of spaces of indent required inside a hanging or continued line.\nindent-after-paren=4\n\n# String used as indentation unit. This is usually \" \" (4 spaces) or \"\\t\" (1\n# tab).\nindent-string=' '\n\n# Maximum number of characters on a single line.\nmax-line-length=120\n\n# Maximum number of lines in a module.\nmax-module-lines=1000\n\n# List of optional constructs for which whitespace checking is disabled. `dict-\n# separator` is used to allow tabulation in dicts, etc.: {1 : 1,\\n222: 2}.\n# `trailing-comma` allows a space between comma and closing bracket: (a, ).\n# `empty-line` allows space-only lines.\nno-space-check=trailing-comma,\n dict-separator\n\n# Allow the body of a class to be on the same line as the declaration if body\n# contains single statement.\nsingle-line-class-stmt=no\n\n# Allow the body of an if to be on the same line as the test if there is no\n# else.\nsingle-line-if-stmt=no\n\n\n[SIMILARITIES]\n\n# Ignore comments when computing similarities.\nignore-comments=yes\n\n# Ignore docstrings when computing similarities.\nignore-docstrings=yes\n\n# Ignore imports when computing similarities.\nignore-imports=no\n\n# Minimum lines number of a similarity.\nmin-similarity-lines=4\n\n\n[CLASSES]\n\n# List of method names used to declare (i.e. assign) instance attributes.\ndefining-attr-methods=__init__,\n __new__,\n setUp,\n __post_init__\n\n# List of member names, which should be excluded from the protected access\n# warning.\nexclude-protected=_asdict,\n _fields,\n _replace,\n _source,\n _make\n\n# List of valid names for the first argument in a class method.\nvalid-classmethod-first-arg=cls\n\n# List of valid names for the first argument in a metaclass class method.\nvalid-metaclass-classmethod-first-arg=cls\n\n\n[DESIGN]\n\n# Maximum number of arguments for function / method.\nmax-args=5\n\n# Maximum number of attributes for a class (see R0902).\nmax-attributes=7\n\n# Maximum number of boolean expressions in an if statement (see R0916).\nmax-bool-expr=5\n\n# Maximum number of branch for function / method body.\nmax-branches=12\n\n# Maximum number of locals for function / method body.\nmax-locals=15\n\n# Maximum number of parents for a class (see R0901).\nmax-parents=7\n\n# Maximum number of public methods for a class (see R0904).\nmax-public-methods=20\n\n# Maximum number of return / yield for function / method body.\nmax-returns=6\n\n# Maximum number of statements in function / method body.\nmax-statements=50\n\n# Minimum number of public methods for a class (see R0903).\nmin-public-methods=2\n\n\n[IMPORTS]\n\n# List of modules that can be imported at any level, not just the top level\n# one.\nallow-any-import-level=\n\n# Allow wildcard imports from modules that define __all__.\nallow-wildcard-with-all=no\n\n# Analyse import fallback blocks. This can be used to support both Python 2 and\n# 3 compatible code, which means that the block might have code that exists\n# only in one or another interpreter, leading to false positives when analysed.\nanalyse-fallback-blocks=no\n\n# Deprecated modules which should not be used, separated by a comma.\ndeprecated-modules=optparse,tkinter.tix\n\n# Create a graph of external dependencies in the given file (report RP0402 must\n# not be disabled).\next-import-graph=\n\n# Create a graph of every (i.e. internal and external) dependencies in the\n# given file (report RP0402 must not be disabled).\nimport-graph=\n\n# Create a graph of internal dependencies in the given file (report RP0402 must\n# not be disabled).\nint-import-graph=\n\n# Force import order to recognize a module as part of the standard\n# compatibility libraries.\nknown-standard-library=\n\n# Force import order to recognize a module as part of a third party library.\nknown-third-party=enchant\n\n# Couples of modules and preferred modules, separated by a comma.\npreferred-modules=\n\n\n[EXCEPTIONS]\n\n# Exceptions that will emit a warning when being caught. Defaults to\n# \"BaseException, Exception\".\novergeneral-exceptions=BaseException,\n Exception" }, { "path": "Makefile", "content": "SAGEMAKER_TRAIN_CONFIG_PATH=models/gradient_boost/envs/sagemaker/configs/train-gpu.json\nSAGEMAKER_DEPLOY_CONFIG_PATH=models/gradient_boost/envs/sagemaker/configs/deploy.json\nTRAIN_PARAM_PATH=models/gradient_boost/training_params.json\nTRAIN_PATH=dataset/train.h5\nVAL_PATH=dataset/val.h5\nTEST_PATH=dataset/test.h5\n\nclean:\n\t-rm -r -f build\n\tmkdir build\n\ntest:\n\tpython -m pytest tests\n\nlint:\n\tpylint ml_ids\n\nlint-errors:\n\tpylint ml_ids -E\n\ntypecheck:\n\tmypy ml_ids\n\nsplit_dataset:\n\tmkdir -p dataset\n\tpython ./ml_ids/data/split_dataset.py \\\n\t\t--dataset-path $(DATASET_PATH) \\\n\t\t--output-path dataset \\\n\t\t--random-seed 42\n\ntrain_local:\n\tpython ./models/gradient_boost/envs/local/train.py \\\n\t\t--train-path $(TRAIN_PATH) \\\n\t\t--val-path $(VAL_PATH) \\\n\t\t--test-path $(TEST_PATH) \\\n\t\t--output-path build/models/gradient_boost \\\n\t\t--param-path $(TRAIN_PARAM_PATH)\n\nsagemaker_build_image:\n\t./models/gradient_boost/envs/sagemaker/scripts/build_image.sh ml-ids-train-sagemaker $(TAG)\n\nsagemaker_push_image:\n\t./models/gradient_boost/envs/sagemaker/scripts/push_image_to_ecr.sh ml-ids-train-sagemaker $(TAG) | grep -Po '(?<=^image-name=).*' > sagemaker-image-name.txt\n\nsagemaker_train_local:\n\tpython ./models/gradient_boost/envs/sagemaker/scripts/train.py \\\n \t\t--config-path $(SAGEMAKER_TRAIN_CONFIG_PATH) \\\n \t\t--param-path $(TRAIN_PARAM_PATH) \\\n \t\t--mode LOCAL \\\n \t\t--image-name \"ml-ids-train-sagemaker:$(TAG)\" \\\n \t\t--job-id \"ml-ids-sagemaker-job\"\n\nsagemaker_train_aws:\n\tpython ./models/gradient_boost/envs/sagemaker/scripts/train.py \\\n \t\t--config-path $(SAGEMAKER_TRAIN_CONFIG_PATH) \\\n \t\t--param-path $(TRAIN_PARAM_PATH) \\\n \t\t--mode AWS \\\n \t\t--image-name $(SAGEMAKER_IMAGE_NAME) \\\n \t\t--job-id $(JOB_ID)\n\nsagemaker_deploy:\n\tpython ./models/gradient_boost/envs/sagemaker/scripts/deploy.py \\\n \t\t--config-path $(SAGEMAKER_DEPLOY_CONFIG_PATH) \\\n \t\t--job-id $(JOB_ID)\n\nsagemaker_undeploy:\n\tpython ./models/gradient_boost/envs/sagemaker/scripts/undeploy.py \\\n\t\t--config-path $(SAGEMAKER_DEPLOY_CONFIG_PATH)" }, { "path": "README.md", "content": "# A machine learning based approach towards building an Intrusion Detection System\n\n## Problem Description\nWith the rising amount of network enabled devices connected to the internet such as mobile phones, IOT appliances or vehicles the concern about the security implications of using these devices is growing. The increase in numbers and types of networked devices inevitably leads to a wider surface of attack whereas the impact of successful attacks is becoming increasingly severe as more critical responsibilities are assumed be these devices.\n\nTo identify and counter network attacks it is common to employ a combination of multiple systems in order to prevent attacks from happening or to detect and stop ongoing attacks if they can not be prevented initially.\nThese systems are usually comprised of an intrusion prevention system such as a firewall as the first layer of security with intrusion detection systems representing the second layer.\nShould the intrusion prevention system be unable to prevent a network attack it is the task of the detection system to identify malicious network traffic in order to stop the ongoing attack and keep the recorded network traffic data for later analysis. This data can subsequently be used to update the prevention system to allow for the detection of the specific network attack in the future. The need for intrusion detection systems is rising as absolute prevention against attacks is not possible due to the rapid emergence of new attack types.\n\nEven though intrusion detection systems are an essential part of network security many detection systems deployed today have a significant weakness as they facilitate signature-based attack classification patterns which are able to detect the most common known attack patterns but have the drawback of being unable to detect novel attack types.\nTo overcome this limitation research in intrusion detection systems is focusing on more dynamic approaches based on machine learning and anomaly detection methods. In these systems the normal network behaviour is learned by processing previously recorded benign data packets which allows the system to identify new attack types by analyzing network traffic for anomalous data flows.\n\nThis project aims to implement a classifier capable of identifying network traffic as either benign or malicious based on machine learning and deep learning methodologies.\n\n## Data\nThe data used to train the classifier is taken from the [CSE-CIC-IDS2018](https://www.unb.ca/cic/datasets/ids-2018.html) dataset provided by the Canadian Institute for Cybersecurity. It was created by capturing all network traffic during ten days of operation inside a controlled network environment on AWS where realistic background traffic and different attack scenarios were conducted.\nAs a result the dataset contains both benign network traffic as well as captures of the most common network attacks.\nThe dataset is comprised of the raw network captures in pcap format as well as csv files created by using [CICFlowMeter-V3](https://www.unb.ca/cic/research/applications.html#CICFlowMeter) containing 80 statistical features of the individual network flows combined with their corresponding labels.\nA network flow is defined as an aggregation of interrelated network packets identified by the following properties:\n* Source IP\n* Destination IP\n* Source port\n* Destination port\n* Protocol\n\nThe dataset contains approximately 16 million individual network flows and covers the following attack scenarios:\n* Brute Force\n* DoS,\n* DDos\n* Heartbleed,\n* Web Attack,\n* Infiltration,\n* Botnet\n\n## Approach\nThe goal of this project is to create a classifier capable of categorising network flows as either benign or malicious.\nThe problem is understood as a supervised learning problem using the labels provided in the dataset which identify the network flows as either benign or malicious. Different approaches of classifying the data will be evaluated to formulate the problem either as a binary classification or a multiclass classification problem differentiating between the individual classes of attacks provided in the dataset in the later case. A relevant subset of the features provided in the dataset will be used as predictors to classify individual network flows.\nMachine learning methods like k-nearest neighbours, random forest or SVM will be applied to the problem and evaluated in the first step in order to assess the feasibility of using traditional machine learning approaches.\nSubsequently deep learning models like convolutional neural networks, autoencoders or recurrent neural networks will be employed to create a competing classifier as recent research has shown that deep learning methods represent a promising application in the field of anomaly detection.\nThe results of both approaches will be compared to select the best performing classifier.\n\n## Deliverables\nThe classifier will be deployed and served via a REST API in conjunction with a simple web application providing a user interface to utilize the API.\n\nThe REST API will provide the following functionality:\n* an endpoint to submit network capture files in pcap format. Individual network flows are extracted from the capture files and analysed for malicious network traffic.\n* (optional) an endpoint to stream continuous network traffic captures which are analysed in near real-time combined with\n* (optional) an endpoint to register a web-socket in order to get notified upon detection of malicious network traffic.\n\nTo further showcase the project, a testbed could be created against which various attack scenarios can be performed. This testbed would be connected to the streaming API for near real-time detection of malicious network traffic.\n\n## Computational resources\nThe requirements regarding the computational resources to train the classifiers are given below:\n\n| Category | Resource |\n| ------------- | ------------- |\n| CPU | Intel Core i7 processor |\n| RAM | 32 GB |\n| GPU | 1 GPU, 8 GB RAM |\n| HDD | 100 GB |\n\n\n## Classifier\n\nThe machine learning estimator created in this project follows a supervised approach and is trained using the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) algorithm. Employing the [CatBoost](https://catboost.ai/) library a binary classifier is created, capable of classifying network flows as either benign or malicious. The chosen parameters of the classifier and its performance metrics can be examined in the following [notebook](https://github.com/cstub/ml-ids/blob/master/notebooks/07_binary_classifier_comparison/binary-classifier-comparison.ipynb). \n\n## Deployment Architecture\n\nThe deployment architecture of the complete ML-IDS system is explained in detail in the [system architecture](https://docs.google.com/document/d/1s_EBMTid4gdrsQU_xOCAYK1BzxkhhnYl6wHFSZo_9Tw/edit?usp=sharing).\n\n## Model Training and Deployment\n\nThe model can be trained and deployed either locally or via [Amazon SageMaker](https://aws.amazon.com/sagemaker/). \nIn each case the [MLflow](https://www.mlflow.org/docs/latest/index.html) framework is utilized to train the model and create the model artifacts.\n\n### Installation\n\nTo install the necessary dependencies checkout the project and create a new Anaconda environment from the environment.yml file.\n\n```\nconda env create -f environment.yml\n```\n\nAfterwards activate the environment and install the project resources.\n\n```\nconda activate ml-ids\n\npip install -e .\n```\n\n### Dataset Creation\n\nTo create the dataset for training use the following command:\n\n```\nmake split_dataset \\\n DATASET_PATH={path-to-source-dataset}\n```\n\nThis command will read the source dataset and split the dataset into separate train/validation/test sets with a sample ratio of 80%/10%/10%. The specified source dataset should be a folder containing multiple `.csv` files. \nYou can use the [CIC-IDS-2018 dataset](https://www.unb.ca/cic/datasets/ids-2018.html) provided via [Google Drive](https://drive.google.com/open?id=1HrTPh0YRSZ4T9DLa_c47lubheKUcPl0r) for this purpose. \nOnce the command completes a new folder `dataset` is created that contains the splitted datasets in `.h5` format.\n\n### Local Mode\n\nTo train the model in local mode, using the default parameters and dataset locations created by `split_dataset`, use the following command:\n\n```\nmake train_local\n```\n\nIf the datasets are stored in a different location or you want to specify different training parameters, you can optionally supply the dataset locations and a training parameter file:\n\n```\nmake train_local \\\n TRAIN_PATH={path-to-train-dataset} \\\n VAL_PATH={path-to-train-dataset} \\\n TEST_PATH={path-to-train-dataset} \\\n TRAIN_PARAM_PATH={path-to-param-file}\n```\n\nUpon completion of the training process the model artifacts can be found in the `build/models/gradient_boost` directory.\n\nTo deploy the model locally the MLflow CLI can be used.\n\n```\nmlflow models serve -m build/models/gradient_boost -p 5000\n```\n\nThe model can also be deployed as a Docker container using the following commands:\n\n```\nmlflow models build-docker -m build/models/gradient_boost -n ml-ids-classifier:1.0\n\ndocker run -p 5001:8080 ml-ids-classifier:1.0\n```\n\n### Amazon SageMaker\n\nTo train the model on Amazon SageMaker the following command sequence is used:\n\n```\n# build a new docker container for model training\nmake sagemaker_build_image \\\n TAG=1.0\n\n# upload the container to AWS ECR\nmake sagemaker_push_image \\\n TAG=1.0\n\n# execute the training container on Amazon SageMaker\nmake sagemaker_train_aws \\\n SAGEMAKER_IMAGE_NAME={ecr-image-name}:1.0 \\\n JOB_ID=ml-ids-job-0001\n```\n\nThis command requires a valid AWS account with the appropriate permissions to be configured locally via the [AWS CLI](https://aws.amazon.com/cli/). Furthermore, [AWS ECR](https://aws.amazon.com/ecr/) and Amazon SageMaker must be configured for the account.\n\nUsing this repository, the manual invocation of the aforementioned commands is not necessary as training on Amazon SageMaker is supported via a [GitHub workflow](https://github.com/cstub/ml-ids/blob/master/.github/workflows/train.yml) that is triggered upon creation of a new tag of the form `m*` (e.g. `m1.0`).\n\nTo deploy a trained model on Amazon SageMaker a [GitHub Deployment request](https://developer.github.com/v3/repos/deployments/) using the GitHub API must be issued, specifying the tag of the model.\n\n```\n{\n \"ref\": \"refs/tags/m1.0\",\n \"payload\": {},\n \"description\": \"Deploy request for model version m1.0\",\n \"auto_merge\": false\n}\n```\n\nThis deployment request triggers a [GitHub workflow](https://github.com/cstub/ml-ids/blob/master/.github/workflows/deployment.yml), deploying the model to SageMaker.\nAfter successful deployment the model is accessible via the SageMaker HTTP API.\n\n## Using the Classifier\n\nThe classifier deployed on Amazon SageMaker is not directly available publicly, but can be accessed using the [ML-IDS REST API](https://github.com/cstub/ml-ids-api). \n\n### REST API\n\nTo invoke the REST API the following command can be used to submit a prediction request for a given network flow:\n\n```\ncurl -X POST \\\n http://ml-ids-cluster-lb-1096011980.eu-west-1.elb.amazonaws.com/api/predictions \\\n -H 'Accept: */*' \\\n -H 'Content-Type: application/json; format=pandas-split' \\\n -H 'Host: ml-ids-cluster-lb-1096011980.eu-west-1.elb.amazonaws.com' \\\n -H 'cache-control: no-cache' \\\n -d '{\"columns\":[\"dst_port\",\"protocol\",\"timestamp\",\"flow_duration\",\"tot_fwd_pkts\",\"tot_bwd_pkts\",\"totlen_fwd_pkts\",\"totlen_bwd_pkts\",\"fwd_pkt_len_max\",\"fwd_pkt_len_min\",\"fwd_pkt_len_mean\",\"fwd_pkt_len_std\",\"bwd_pkt_len_max\",\"bwd_pkt_len_min\",\"bwd_pkt_len_mean\",\"bwd_pkt_len_std\",\"flow_byts_s\",\"flow_pkts_s\",\"flow_iat_mean\",\"flow_iat_std\",\"flow_iat_max\",\"flow_iat_min\",\"fwd_iat_tot\",\"fwd_iat_mean\",\"fwd_iat_std\",\"fwd_iat_max\",\"fwd_iat_min\",\"bwd_iat_tot\",\"bwd_iat_mean\",\"bwd_iat_std\",\"bwd_iat_max\",\"bwd_iat_min\",\"fwd_psh_flags\",\"bwd_psh_flags\",\"fwd_urg_flags\",\"bwd_urg_flags\",\"fwd_header_len\",\"bwd_header_len\",\"fwd_pkts_s\",\"bwd_pkts_s\",\"pkt_len_min\",\"pkt_len_max\",\"pkt_len_mean\",\"pkt_len_std\",\"pkt_len_var\",\"fin_flag_cnt\",\"syn_flag_cnt\",\"rst_flag_cnt\",\"psh_flag_cnt\",\"ack_flag_cnt\",\"urg_flag_cnt\",\"cwe_flag_count\",\"ece_flag_cnt\",\"down_up_ratio\",\"pkt_size_avg\",\"fwd_seg_size_avg\",\"bwd_seg_size_avg\",\"fwd_byts_b_avg\",\"fwd_pkts_b_avg\",\"fwd_blk_rate_avg\",\"bwd_byts_b_avg\",\"bwd_pkts_b_avg\",\"bwd_blk_rate_avg\",\"subflow_fwd_pkts\",\"subflow_fwd_byts\",\"subflow_bwd_pkts\",\"subflow_bwd_byts\",\"init_fwd_win_byts\",\"init_bwd_win_byts\",\"fwd_act_data_pkts\",\"fwd_seg_size_min\",\"active_mean\",\"active_std\",\"active_max\",\"active_min\",\"idle_mean\",\"idle_std\",\"idle_max\",\"idle_min\"],\"data\":[[80,17,\"21\\\\/02\\\\/2018 10:15:06\",119759145,75837,0,2426784,0,32,32,32.0,0.0,0,0,0.0,0.0,20263.87212,633.2460039,1579.1859130859,31767.046875,920247,1,120000000,1579.1859130859,31767.046875,920247,1,0,0.0,0.0,0,0,0,0,0,0,606696,0,633.2460327148,0.0,32,32,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0,32.0004234314,32.0,0.0,0,0,0,0,0,0,75837,2426784,0,0,-1,-1,75836,8,0.0,0.0,0,0,0.0,0.0,0,0]]}'\n```\n\n### ML-IDS API Clients\n\nFor convenience, the Python clients implemented in the [ML-IDS API Clients project](https://github.com/cstub/ml-ids-api-client) can be used to submit new prediction requests to the API and receive real-time notifications on detection of malicious network flows.\n" }, { "path": "data/README.md", "content": "## Data\n\nThe data used to train the classifiers is taken from the [CSE-CIC-IDS2018](https://www.unb.ca/cic/datasets/ids-2018.html) dataset provided by the Canadian Institute for Cybersecurity.\nIt was created by capturing all network traffic during ten days of operation inside a controlled network environment on AWS where realistic background traffic and different attack scenarios were conducted.\n\nThe dataset consists of raw network captures in pcap format as well as processed csv files created by using [CICFlowMeter-V3](https://www.unb.ca/cic/research/applications.html#CICFlowMeter) containing 80 statistical features of the individual network flows combined with their corresponding labels.\n\nDue to size limitations the data provided in this repository represents only a small portion of the dataset in form of processed network flows. The full dataset consisting of the raw network captures and the processed csv files can be retrieved from AWS S3.\n\n## Download\n\nA prerequisite to downloading the full dataset is the installation of the [AWS CLI](https://aws.amazon.com/cli/).\n\nTo download the processed csv files containing the analyzed network flows (~7GB) run the following command:\n```bash\naws s3 sync --no-sign-request --region \"s3://cse-cic-ids2018/Processed Traffic Data for ML Algorithms/\" \n```\nTo download the raw network captures in pcap format (~477GB) run:\n```bash\naws s3 sync --no-sign-request --region \"s3://cse-cic-ids2018/Original Network Traffic and Log data/\" \n```\nTo download the full dataset containing the raw network captures and processed csv files (~484GB) use the following command:\n```bash\naws s3 sync --no-sign-request --region \"s3://cse-cic-ids2018/\" \n```\n\n## Preprocessed Dataset\n\nThe preprocessed dataset used for model training and evaluation can be found at [Google Drive](https://drive.google.com/drive/folders/1AWhRsVShJ_KvYKrV0VlnM1odtJ4Tp-uC?usp=sharing).\n" }, { "path": "data/Wednesday-14-02-2018_TrafficForML_CICFlowMeter.csv", "content": "version https://git-lfs.github.com/spec/v1\noid sha256:acff8bc61376ee031d80878ee6099e0b1a87a1bd711d8068298421418c9f8147\nsize 358223333\n" }, { "path": "environment-notebook.yaml", "content": "name: ml-ids-notebooks\nchannels:\n - anaconda\n - conda-forge\n - defaults\ndependencies:\n - catboost=0.18.1=py37_0\n - click=7.0=py37_0\n - cloudpickle=1.2.2=py_0\n - eli5=0.10.1=py37_1\n - findspark=1.3.0=py_1\n - imbalanced-learn=0.5.0=py_0\n - jupyter=1.0.0=py_2\n - matplotlib=3.1.1=py37_1\n - numpy=1.17.2=py37h95a1406_0\n - pandas=0.25.2=py37hb3f55d8_0\n - pip=19.2.3=py37_0\n - pyspark=2.4.4=py_0\n - pytest=5.2.1=py37_0\n - pytest-runner=5.1=py_0\n - python=3.7.3=h33d41f4_1\n - python-dateutil<2.8.1\n - requests<2.21.0\n - scikit-learn=0.21.3=py37hcdab131_0\n - scikit-plot=0.3.7=py_1\n - scipy=1.3.1=py37h921218d_2\n - seaborn=0.9.0=py_1\n - setuptools=41.6.0=py37_1\n - shap=0.31.0=py37hb3f55d8_0\n - pip:\n - h5py==2.10.0\n - hyperopt==0.2.2\n - keras==2.3.1\n - keras-applications==1.0.8\n - keras-preprocessing==1.1.0\n - tables==3.6.1\n - tensorboard==2.0.0\n - tensorflow-estimator==2.0.0\n - tensorflow-gpu==2.0.0\n" }, { "path": "environment.yaml", "content": "name: ml-ids\nchannels:\n - anaconda\n - conda-forge\n - defaults\ndependencies:\n - catboost=0.18.1=py37_0\n - click=7.0=py37_0\n - cloudpickle=1.2.2=py_0\n - imbalanced-learn=0.5.0=py_0\n - matplotlib=3.1.1=py37_1\n - mypy=0.750\n - numpy=1.17.2=py37h95a1406_0\n - pandas=0.25.2=py37hb3f55d8_0\n - pip=19.2.3=py37_0\n - pylint=2.4.4\n - pytest=5.2.1=py37_0\n - pytest-runner=5.1=py_0\n - python=3.7.3=h33d41f4_1\n - python-dateutil<2.8.1\n - requests<2.21.0\n - scikit-learn=0.21.3=py37hcdab131_0\n - scipy=1.3.1=py37h921218d_2\n - seaborn=0.9.0=py_1\n - setuptools=41.6.0=py37_1\n - pip:\n - mlflow==1.4\n - sagemaker==1.44.3\n - h5py==2.10.0\n - hyperopt==0.2.2\n - keras==2.3.1\n - keras-applications==1.0.8\n - keras-preprocessing==1.1.0\n - tables==3.6.1\n - tensorflow-estimator==2.0.0\n - tensorflow-gpu==2.0.0\n" }, { "path": "ml_ids/__init__.py", "content": "" }, { "path": "ml_ids/conf.py", "content": "\"\"\"\nGlobal configuration variables.\n\"\"\"\nimport os\n\nROOT_DIR = os.sep.join(os.path.dirname(os.path.abspath(__file__)).split(os.sep)[:-1])\n\nTEST_DIR = os.path.join(ROOT_DIR, 'tests')\n\nTEST_DATA_DIR = os.path.join(TEST_DIR, 'validation_data')\n" }, { "path": "ml_ids/data/__init__.py", "content": "" }, { "path": "ml_ids/data/dataset.py", "content": "\"\"\"\nUtilities to manipulate the CIC-IDS-2018 dataset.\n\"\"\"\nfrom typing import List\nimport os\nimport glob\nimport numpy as np\nimport pandas as pd\nimport ml_ids.data.metadata as md\n\n\ndef remove_inf_values(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Replaces values of type `np.inf` and `-np.inf` in a DataFrame with `null` values.\n\n :param df: Input DataFrame.\n :return: The DataFrame without `np.inf` and `-np.inf` values.\n \"\"\"\n inf_columns = [c for c in df.columns if df[df[c] == np.inf][c].count() > 0]\n for col in inf_columns:\n df[col].replace([np.inf, -np.inf], np.nan, inplace=True)\n return df\n\n\ndef remove_negative_values(df: pd.DataFrame, ignore_cols: List[str] = None) -> pd.DataFrame:\n \"\"\"\n Removes negative values in a DataFrame with `null` values.\n\n :param df: Input DataFrame.\n :param ignore_cols: Columns to ignore. Negative values in this columns will be preserved.\n :return: The DataFrame without negative values.\n \"\"\"\n if ignore_cols is None:\n ignore_cols = []\n\n numeric_cols = df.select_dtypes(include=[np.number]).columns.drop(ignore_cols).values\n\n columns = [c for c in numeric_cols if df[df[c] < 0][c].count() > 0]\n for col in columns:\n mask = df[col] < 0\n df.loc[mask, col] = np.nan\n return df\n\n\ndef add_label_category_column(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Adds the column `label_cat` to the DataFrame specifying the category of the label.\n\n :param df: Input DataFrame.\n :return: The DataFrame containing a new column `label_cat`.\n \"\"\"\n df[md.COLUMN_LABEL_CAT] = df.label.apply(lambda l: md.LABEL_CAT_MAPPING[l])\n return df\n\n\ndef add_label_is_attack_columns(df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"\n Adds the column `label_is_attack` to the DataFrame containing a binary indicator specifying if a row is of category\n `benign = 0` or `attack = 1`.\n\n :param df: Input DataFrame.\n :return: The DataFrame containing a new column `label_is_attack`.\n \"\"\"\n df[md.COLUMN_LABEL_IS_ATTACK] = df.label.apply(lambda l: 0 if l == md.LABEL_BENIGN else 1)\n return df\n\n\ndef load_dataset_generic(load_df_fn,\n dataset_path: str,\n use_cols: List[str] = None,\n omit_cols: List[str] = None,\n preserve_neg_value_cols: list = None,\n transform_data: bool = True) -> pd.DataFrame:\n \"\"\"\n Loads the dataset from the given path using the supplied function.\n All invalid values (`np.inf`, `-np.inf`, negative) are removed and replaced with `null` for easy imputation.\n Negative values of columns specified in `preserve_neg_value_cols` will be preserved.\n\n :param load_df_fn: Function used to load the dataset.\n :param dataset_path: Path of the base directory containing all files of the dataset.\n :param use_cols: Columns to load.\n :param omit_cols: Columns to omit.\n :param nrows: Number of rows to load per file.\n :param transform_data: Indicates if data should be manipulated (removal of invalid and negative values).\n :param preserve_neg_value_cols: Columns in which negative values are preserved.\n :return: The dataset as a DataFrame.\n \"\"\"\n cols = None\n if use_cols:\n cols = use_cols\n if omit_cols:\n cols = [c for c in md.COLUMN_DTYPES.keys() if c not in omit_cols]\n\n df = load_df_fn(dataset_path, cols)\n\n if transform_data:\n df = remove_inf_values(df)\n df = remove_negative_values(df, preserve_neg_value_cols)\n\n if md.COLUMN_LABEL in df.columns:\n df = add_label_category_column(df)\n df = add_label_is_attack_columns(df)\n\n return df\n\n\ndef load_dataset(dataset_path: str,\n use_cols: List[str] = None,\n omit_cols: List[str] = None,\n nrows: int = None,\n transform_data: bool = True,\n preserve_neg_value_cols: list = None) -> pd.DataFrame:\n \"\"\"\n Loads the dataset in CSV format from the given path.\n All invalid values (`np.inf`, `-np.inf`, negative) are removed and replaced with `null` for easy imputation.\n Negative values of columns specified in `preserve_neg_value_cols` will be preserved.\n\n :param dataset_path: Path of the base directory containing all files of the dataset.\n :param use_cols: Columns to load.\n :param omit_cols: Columns to omit.\n :param nrows: Number of rows to load per file.\n :param transform_data: Indicates if data should be manipulated (removal of invalid and negative values).\n :param preserve_neg_value_cols: Columns in which negative values are preserved.\n :return: The dataset as a DataFrame.\n \"\"\"\n\n def load_csv(path, cols):\n files = glob.glob(os.path.join(path, '*.csv'))\n return pd.concat([pd.read_csv(f, dtype=md.COLUMN_DTYPES, usecols=cols, nrows=nrows) for f in files])\n\n return load_dataset_generic(load_df_fn=load_csv,\n dataset_path=dataset_path,\n use_cols=use_cols,\n omit_cols=omit_cols,\n preserve_neg_value_cols=preserve_neg_value_cols,\n transform_data=transform_data)\n\n\ndef load_dataset_hdf(dataset_path: str,\n use_cols: List[str] = None,\n omit_cols: List[str] = None,\n preserve_neg_value_cols: list = None,\n transform_data: bool = True,\n key: str = None) -> pd.DataFrame:\n \"\"\"\n Loads the dataset stored as a HDF file from the given path.\n All invalid values (`np.inf`, `-np.inf`, negative) are removed and replaced with `null` for easy imputation.\n Negative values of columns specified in `preserve_neg_value_cols` will be preserved.\n\n :param dataset_path: Path of the base directory containing all files of the dataset.\n :param use_cols: Columns to load.\n :param omit_cols: Columns to omit.\n :param preserve_neg_value_cols: Columns in which negative values are preserved.\n :param transform_data: Indicates if data should be manipulated (removal of invalid and negative values).\n :param key: Group identifier in the HDF store.\n :return: The dataset as a DataFrame.\n \"\"\"\n\n def load_hdf(path, cols):\n return pd.read_hdf(path, key=key, columns=cols)\n\n return load_dataset_generic(load_df_fn=load_hdf,\n dataset_path=dataset_path,\n use_cols=use_cols,\n omit_cols=omit_cols,\n preserve_neg_value_cols=preserve_neg_value_cols,\n transform_data=transform_data)\n" }, { "path": "ml_ids/data/metadata.py", "content": "\"\"\"\nMetadata of the CIC-IDS-2018 dataset.\n\"\"\"\nCOLUMN_DTYPES = {\n 'dst_port': 'uint32',\n 'protocol': 'uint8',\n 'timestamp': 'object',\n 'flow_duration': 'int64',\n 'tot_fwd_pkts': 'uint32',\n 'tot_bwd_pkts': 'uint32',\n 'totlen_fwd_pkts': 'uint32',\n 'totlen_bwd_pkts': 'uint32',\n 'fwd_pkt_len_max': 'uint16',\n 'fwd_pkt_len_min': 'uint16',\n 'fwd_pkt_len_mean': 'float32',\n 'fwd_pkt_len_std': 'float32',\n 'bwd_pkt_len_max': 'uint16',\n 'bwd_pkt_len_min': 'uint16',\n 'bwd_pkt_len_mean': 'float32',\n 'bwd_pkt_len_std': 'float32',\n 'flow_byts_s': 'float64',\n 'flow_pkts_s': 'float64',\n 'flow_iat_mean': 'float32',\n 'flow_iat_std': 'float32',\n 'flow_iat_max': 'int64',\n 'flow_iat_min': 'int64',\n 'fwd_iat_tot': 'int64',\n 'fwd_iat_mean': 'float32',\n 'fwd_iat_std': 'float32',\n 'fwd_iat_max': 'int64',\n 'fwd_iat_min': 'int64',\n 'bwd_iat_tot': 'uint32',\n 'bwd_iat_mean': 'float32',\n 'bwd_iat_std': 'float32',\n 'bwd_iat_max': 'uint32',\n 'bwd_iat_min': 'uint32',\n 'fwd_psh_flags': 'uint8',\n 'bwd_psh_flags': 'uint8',\n 'fwd_urg_flags': 'uint8',\n 'bwd_urg_flags': 'uint8',\n 'fwd_header_len': 'uint32',\n 'bwd_header_len': 'uint32',\n 'fwd_pkts_s': 'float32',\n 'bwd_pkts_s': 'float32',\n 'pkt_len_min': 'uint16',\n 'pkt_len_max': 'uint16',\n 'pkt_len_mean': 'float32',\n 'pkt_len_std': 'float32',\n 'pkt_len_var': 'float32',\n 'fin_flag_cnt': 'uint8',\n 'syn_flag_cnt': 'uint8',\n 'rst_flag_cnt': 'uint8',\n 'psh_flag_cnt': 'uint8',\n 'ack_flag_cnt': 'uint8',\n 'urg_flag_cnt': 'uint8',\n 'cwe_flag_count': 'uint8',\n 'ece_flag_cnt': 'uint8',\n 'down_up_ratio': 'uint16',\n 'pkt_size_avg': 'float32',\n 'fwd_seg_size_avg': 'float32',\n 'bwd_seg_size_avg': 'float32',\n 'fwd_byts_b_avg': 'uint8',\n 'fwd_pkts_b_avg': 'uint8',\n 'fwd_blk_rate_avg': 'uint8',\n 'bwd_byts_b_avg': 'uint8',\n 'bwd_pkts_b_avg': 'uint8',\n 'bwd_blk_rate_avg': 'uint8',\n 'subflow_fwd_pkts': 'uint32',\n 'subflow_fwd_byts': 'uint32',\n 'subflow_bwd_pkts': 'uint32',\n 'subflow_bwd_byts': 'uint32',\n 'init_fwd_win_byts': 'int32',\n 'init_bwd_win_byts': 'int32',\n 'fwd_act_data_pkts': 'uint32',\n 'fwd_seg_size_min': 'uint8',\n 'active_mean': 'float32',\n 'active_std': 'float32',\n 'active_max': 'uint32',\n 'active_min': 'uint32',\n 'idle_mean': 'float32',\n 'idle_std': 'float32',\n 'idle_max': 'uint64',\n 'idle_min': 'uint64',\n 'label': 'category'\n}\n\nLABEL_BENIGN = 'Benign'\n\nLABEL_CAT_MAPPING = {\n 'Benign': 0,\n 'Bot': 1,\n 'Brute Force -Web': 2,\n 'Brute Force -XSS': 3,\n 'DoS attacks-GoldenEye': 4,\n 'DoS attacks-Hulk': 5,\n 'DoS attacks-SlowHTTPTest': 6,\n 'DoS attacks-Slowloris': 7,\n 'DDOS attack-HOIC': 8,\n 'DDOS attack-LOIC-UDP': 9,\n 'DDoS attacks-LOIC-HTTP': 10,\n 'FTP-BruteForce': 11,\n 'Infilteration': 12,\n 'SQL Injection': 13,\n 'SSH-Bruteforce': 14,\n 'DDOS LOIT': 15,\n 'Heartbleed': 16,\n 'PortScan': 17\n}\n\nFEATURES_NO_VARIANCE = [\n \"bwd_blk_rate_avg\",\n \"bwd_byts_b_avg\",\n \"bwd_pkts_b_avg\",\n \"bwd_psh_flags\",\n \"bwd_urg_flags\",\n \"fwd_blk_rate_avg\",\n \"fwd_byts_b_avg\",\n \"fwd_pkts_b_avg\"\n]\n\nFEATURES_TO_IGNORE = [\n 'timestamp',\n 'dst_port',\n 'protocol'\n]\n\nFEATURES_PRESERVE_NEG_COLUMNS = [\n 'init_fwd_win_byts',\n 'init_bwd_win_byts'\n]\n\nCOLUMN_LABEL = 'label'\nCOLUMN_LABEL_CAT = 'label_cat'\nCOLUMN_LABEL_IS_ATTACK = 'label_is_attack'\n" }, { "path": "ml_ids/data/split_dataset.py", "content": "\"\"\"\nCLI to split a single dataset into train/val/test sub-datasets.\n\"\"\"\nimport os\nimport sys\nimport logging\nimport click\nimport pandas as pd\nimport ml_ids.data.metadata as md\nfrom ml_ids.data.dataset import load_dataset\nfrom ml_ids.model_selection import train_val_test_split\n\nlogging.basicConfig(\n format='[%(asctime)s|%(module)s.py|%(levelname)s] %(message)s',\n datefmt='%H:%M:%S',\n level=logging.INFO,\n stream=sys.stdout\n)\n\n\n@click.command()\n@click.option('--dataset-path', type=click.Path(exists=True), required=True,\n help='Path to the input dataset in .csv format. Can be a folder containing multiple files.')\n@click.option('--output-path', type=click.Path(exists=True), required=True,\n help='Path to store the output datasets.')\n@click.option('--val-size', type=click.FloatRange(0, 1), default=0.1,\n help='Fraction of the data used for the validation set.')\n@click.option('--test-size', type=click.FloatRange(0, 1), default=0.1,\n help='Fraction of the data used for the test set.')\n@click.option('--nrows', type=int,\n help='Number of rows to load per input file.')\n@click.option('--random-seed', type=int,\n help='Random seed.')\ndef split_dataset(dataset_path, output_path, val_size, test_size, nrows, random_seed):\n \"\"\"\n Runs the CLI.\n \"\"\"\n logging.info('Loading dataset from \"%s\"...', dataset_path)\n\n dataset = load_dataset(dataset_path=dataset_path, transform_data=False, nrows=nrows)\n\n train, val, test = train_val_test_split(dataset,\n val_size=val_size,\n test_size=test_size,\n stratify_col=md.COLUMN_LABEL_CAT,\n random_state=random_seed)\n\n train = remove_extra_labels(train)\n val = remove_extra_labels(val)\n test = remove_extra_labels(test)\n\n save_dataset(train, output_path, 'train')\n save_dataset(val, output_path, 'val')\n save_dataset(test, output_path, 'test')\n logging.info('Processing complete.')\n\n\ndef remove_extra_labels(dataset: pd.DataFrame):\n \"\"\"\n Removes unused target labels.\n :param dataset: Input dataset as Pandas DataFrame.\n :return: Dataset without unused target labels.\n \"\"\"\n return dataset.drop(columns=[md.COLUMN_LABEL_CAT, md.COLUMN_LABEL_IS_ATTACK])\n\n\ndef save_dataset(dataset: pd.DataFrame, path: str, ds_type: str):\n \"\"\"\n Stores the given dataset in hdf format on the specified path.\n\n :param dataset: Dataset as Pandas DataFrame.\n :param path: Target path to store the dataset.\n :param ds_type: Dataset type.\n :return: None\n \"\"\"\n file_path = os.path.join(path, '{}.h5'.format(ds_type))\n\n logging.info('Storing dataset \"%s\" of size %d to \"%s\"', ds_type, len(dataset), file_path)\n\n dataset.to_hdf(file_path, 'ids_data', format='t', complevel=5, complib='zlib')\n\n\nif __name__ == '__main__':\n # pylint: disable=no-value-for-parameter\n split_dataset()\n" }, { "path": "ml_ids/keras/__init__.py", "content": "" }, { "path": "ml_ids/keras/callbacks.py", "content": "\"\"\"\nCustom callbacks for Keras models.\n\"\"\"\n# pylint: disable=import-error\nfrom tensorflow import keras\nfrom tensorflow.keras import callbacks\n\nK = keras.backend\n\n\nclass OneCycleScheduler(callbacks.Callback):\n \"\"\"\n Keras callback implementing a one-cycle learning-rate scheduler.\n Provided by https://github.com/ageron/handson-ml2/blob/master/11_training_deep_neural_networks.ipynb.\n \"\"\"\n def __init__(self, iterations, max_rate, start_rate=None,\n last_iterations=None, last_rate=None):\n self.iterations = iterations\n self.max_rate = max_rate\n self.start_rate = start_rate or max_rate / 10\n self.last_iterations = last_iterations or iterations // 10 + 1\n self.half_iteration = (iterations - self.last_iterations) // 2\n self.last_rate = last_rate or self.start_rate / 1000\n self.iteration = 0\n\n def _interpolate(self, iter1, iter2, rate1, rate2):\n return ((rate2 - rate1) * (iter2 - self.iteration)\n / (iter2 - iter1) + rate1)\n\n def on_batch_begin(self, batch, logs):\n if self.iteration < self.half_iteration:\n rate = self._interpolate(0, self.half_iteration, self.start_rate, self.max_rate)\n elif self.iteration < 2 * self.half_iteration:\n rate = self._interpolate(self.half_iteration, 2 * self.half_iteration,\n self.max_rate, self.start_rate)\n else:\n rate = self._interpolate(2 * self.half_iteration, self.iterations,\n self.start_rate, self.last_rate)\n rate = max(rate, self.last_rate)\n self.iteration += 1\n K.set_value(self.model.optimizer.lr, rate)\n" }, { "path": "ml_ids/keras/evaluation.py", "content": "\"\"\"\nUtility functions to evaluate Keras models.\n\"\"\"\nPREDICT_BATCH_SIZE = 16384\n\n\ndef evaluate_model(model, X_train, y_train, X_val, y_val, metric_title):\n \"\"\"\n Prints the performance metrics of a Keras model by invoking the `evaluate` function of the model on the training\n and validation dataset.\n\n :param model: Keras model.\n :param X_train: Predictor variables of the training dataset.\n :param y_train: Target labels of the training dataset.\n :param X_val: Predictor variables of the validation dataset.\n :param y_val: Target labels of the validation dataset.\n :param metric_title: Title of the metrics.\n :return: None\n \"\"\"\n print('Evaluation:')\n print('===========')\n print(' {}'.format(metric_title))\n print('Train: {}'.format(model.evaluate(X_train, y_train, batch_size=PREDICT_BATCH_SIZE, verbose=0)))\n print('Val: {}'.format(model.evaluate(X_val, y_val, batch_size=PREDICT_BATCH_SIZE, verbose=0)))\n" }, { "path": "ml_ids/keras/metrics.py", "content": "\"\"\"\nUtilities to create custom metrics for Keras models.\n\"\"\"\n# pylint: disable=import-error\nimport gc\nimport numpy as np\nfrom tensorflow import keras\nfrom tensorflow.keras import callbacks\nfrom sklearn.metrics import average_precision_score\n\nK = keras.backend\n\n\nclass AveragePrecisionScoreMetric(callbacks.Callback):\n \"\"\"\n Keras callback calculating the average precision score for a given validation dataset using the\n `average_precision_score` metric from Scikit-learn.\n \"\"\"\n def __init__(self, X_val, y_val, batch_size=4096):\n super(AveragePrecisionScoreMetric, self).__init__()\n self.X_val = X_val\n self.y_val = y_val\n self.batch_size = batch_size\n\n def get_precision_score(self):\n \"\"\"\n Calculates the average precision score using scikit-learn.\n \"\"\"\n preds = self.model.predict(self.X_val, batch_size=self.batch_size)\n # reduces memory consumption caused by a memory leak in `model.predict()` of Tensorflow 2\n # https://github.com/tensorflow/tensorflow/issues/33009\n gc.collect()\n mse = np.mean(np.power(self.X_val - preds, 2), axis=1)\n return average_precision_score(self.y_val, mse)\n\n def on_epoch_end(self, epoch, logs):\n \"\"\"\n Invoked after each training epoch.\n \"\"\"\n auprc = self.get_precision_score()\n logs['val_auprc'] = auprc\n print(' - val_auprc: {0:.4f}'.format(auprc))\n" }, { "path": "ml_ids/keras/model_selection.py", "content": "\"\"\"\nUtility functions for model selection of Keras models.\n\"\"\"\nimport gc\nfrom typing import Tuple\nimport numpy as np\nfrom sklearn.model_selection import StratifiedKFold\nfrom tensorflow import keras\n\n\ndef cross_val_train(fit_fn,\n X: np.ndarray,\n y: np.ndarray,\n target_transform_fn=id,\n target_stratify_fn=id,\n n_splits: int = 3,\n fit_args: dict = None,\n random_state: int = None) -> Tuple[np.ndarray, np.ndarray, list]:\n \"\"\"\n Performs stratified cross-validation for a Keras model using the provided fit function.\n\n :param fit_fn: The function used to fit a model with a given split of the train and test set. Must return a fitted\n Keras model with its history.\n :param X: Predictor variables.\n :param y: Labels.\n :param target_transform_fn: Function to transform the target labels (e.g. one-hot encoding).\n :param target_stratify_fn: Function to extract the target label to stratify by.\n :param n_splits: Number of cross-validation splits.\n :param fit_args: Arguments to pass to the fit function.\n :param random_state: Random state.\n :return: A triple containing the cross-validation predictions, the true values and a list of history-objects.\n \"\"\"\n if fit_args is None:\n fit_args = {}\n\n kfold = StratifiedKFold(n_splits=n_splits, random_state=random_state)\n\n cv_predictions = None\n cv_y_true = None\n hists = []\n fold = 1\n\n for train_index, val_index in kfold.split(X, target_stratify_fn(y)):\n print('\\nFold {}/{}:'.format(fold, n_splits))\n print('==========')\n\n X_train, X_val = X[train_index], X[val_index]\n y_train, y_val = y[train_index], y[val_index]\n\n y_train_ = target_transform_fn(y_train)\n y_val_ = target_transform_fn(y_val)\n\n keras.backend.clear_session()\n gc.collect()\n\n model, hist = fit_fn(X_train, y_train_, X_val, y_val_, fit_args, (fold == 1))\n\n if isinstance(hist, list):\n hists.extend(hist)\n else:\n hists.append(hist)\n\n if cv_predictions is not None:\n cv_predictions = np.append(cv_predictions, model.predict(X_val), axis=0)\n else:\n cv_predictions = model.predict(X_val)\n\n if cv_y_true is not None:\n cv_y_true = np.append(cv_y_true, y_val, axis=0)\n else:\n cv_y_true = y_val\n\n fold = fold + 1\n\n return cv_predictions, cv_y_true, hists\n" }, { "path": "ml_ids/keras/prediction.py", "content": "\"\"\"\nUtility functions to create predictions using Keras models.\n\"\"\"\nPREDICT_BATCH_SIZE = 16384\n\n\ndef predict(model, X, decision_boundary=0.5):\n \"\"\"\n Performs predictions for a binary classification task given a Keras model and a decision boundary.\n If the probability of a sample belonging to the positive class exceeds the decision boundary the positive label\n is assigned to the sample, otherwise the negative label is used.\n\n :param model: Keras model.\n :param X: Dataset containing samples.\n :param decision_boundary: Decision boundary used to assign predictions to the positive class.\n :return: numpy array containing the binary predictions as one of the values {0, 1}.\n \"\"\"\n pred = model.predict(X, batch_size=PREDICT_BATCH_SIZE)\n return (pred >= decision_boundary).astype('int').reshape(-1)\n\n\ndef predict_proba(model, X):\n \"\"\"\n Performs predictions for a binary classification task given a Keras model.\n This function returns the class probability of the positive class.\n\n :param model: Keras model.\n :param X: Dataset containing samples.\n :return: numpy array containing the class probabilities of the positive class.\n \"\"\"\n return model.predict(X, batch_size=PREDICT_BATCH_SIZE).reshape(-1)\n" }, { "path": "ml_ids/libs/dfencoder/dataframe.py", "content": "# Copyright (c) 2019, Michael Klear.\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are\n# met:\n#\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n#\n# * Redistributions in binary form must reproduce the above\n# copyright notice, this list of conditions and the following\n# disclaimer in the documentation and/or other materials provided\n# with the distribution.\n#\n# * Neither the name of the dfencoder Developers nor the names of any\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n# \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\n# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nimport pandas as pd\nimport numpy as np\n\n\nclass EncoderDataFrame(pd.DataFrame):\n def __init__(self, *args, **kwargs):\n super(EncoderDataFrame, self).__init__(*args, **kwargs)\n\n def swap(self, likelihood=.15):\n \"\"\"\n Performs random swapping of data.\n Each value has a likelihood of *argument likelihood*\n of being randomly replaced with a value from a different\n row.\n Returns a copy of the dataframe with equal size.\n \"\"\"\n\n # select values to swap\n tot_rows = self.__len__()\n n_rows = int(round(tot_rows * likelihood))\n n_cols = len(self.columns)\n\n def gen_indices():\n column = np.repeat(np.arange(n_cols).reshape(1, -1), repeats=n_rows, axis=0)\n row = np.random.randint(0, tot_rows, size=(n_rows, n_cols))\n return row, column\n\n row, column = gen_indices()\n new_mat = self.values\n to_place = new_mat[row, column]\n\n row, column = gen_indices()\n new_mat[row, column] = to_place\n\n dtypes = {col: typ for col, typ in zip(self.columns, self.dtypes)}\n result = EncoderDataFrame(columns=self.columns, data=new_mat)\n result = result.astype(dtypes, copy=False)\n\n return result\n" }, { "path": "ml_ids/model_selection.py", "content": "\"\"\"\nUtilities for machine learning model selection.\n\"\"\"\nfrom typing import Tuple, List\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import precision_recall_curve\n\n\ndef train_val_test_split(df: pd.DataFrame,\n val_size: float = 0.1,\n test_size: float = 0.1,\n stratify_col: str = None,\n random_state: int = None) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n \"\"\"\n Splits the given DataFrame into three parts used for:\n - training\n - validation\n - test\n\n :param df: Input DataFrame.\n :param val_size: Size of validation set.\n :param test_size: Size of test set.\n :param stratify_col: Column to stratify.\n :param random_state: Random state.\n :return: A triple containing (`train`, `val`, `test`) sets.\n \"\"\"\n assert (val_size + test_size) < 1, 'Sum of validation and test size must not be > 1.'\n\n df_stratify = df[stratify_col] if stratify_col else None\n df_train, df_hold = train_test_split(df,\n test_size=(val_size + test_size),\n stratify=df_stratify,\n random_state=random_state)\n\n df_hold_stratify = df_hold[stratify_col] if stratify_col else None\n df_val, df_test = train_test_split(df_hold,\n test_size=test_size / (val_size + test_size),\n stratify=df_hold_stratify,\n random_state=random_state)\n\n return df_train, df_val, df_test\n\n\ndef split_x_y(df: pd.DataFrame, y_cols: List[str] = None) -> Tuple[pd.DataFrame, pd.DataFrame]:\n \"\"\"\n Splits the given DataFrame into a DataFrame `X` containing the predictor variables and a DataFrame 'y' containing\n the labels y.\n\n :param df: Input DataFrame.\n :param y_cols: Columns to use in the labels DataFrame `y`.\n :return: A tuple containing the DataFrames (`X`, `y`).\n \"\"\"\n if y_cols is None:\n y_cols = ['label', 'label_cat', 'label_is_attack']\n return df.drop(columns=y_cols), df[y_cols]\n\n\ndef best_precision_for_target_recall(y_true, y_pred_score, target_recall):\n \"\"\"\n Determines the decision boundary for the best precision given a specified target recall by using\n the precision-recall curve.\n\n :param y_true: True labels.\n :param y_pred_score: Predicted labels.\n :param target_recall: Target recall.\n :return: Decision boundary.\n \"\"\"\n _, recalls, thresholds = precision_recall_curve(y_true, y_pred_score)\n return thresholds[np.argmin(recalls >= target_recall)]\n" }, { "path": "ml_ids/models/__init__.py", "content": "" }, { "path": "ml_ids/models/gradient_boost/__init__.py", "content": "" }, { "path": "ml_ids/models/gradient_boost/mlflow_wrapper.py", "content": "\"\"\"\nWrapper to enable usage of a CatBoost estimator with MLflow.\n\"\"\"\nimport pickle\nimport mlflow.pyfunc\nfrom catboost import CatBoostClassifier\nfrom ml_ids.data.dataset import remove_negative_values, remove_inf_values\n\n\nclass CatBoostWrapper(mlflow.pyfunc.PythonModel):\n \"\"\"\n MLflow wrapper for CatBoost estimators.\n \"\"\"\n\n def load_context(self, context):\n # pylint: disable=attribute-defined-outside-init\n with open(context.artifacts['pipeline'], 'rb') as f:\n self.pipeline = pickle.load(f)\n\n with open(context.artifacts['col_config'], 'rb') as f:\n column_config = pickle.load(f)\n\n self.clf = CatBoostClassifier()\n self.clf.load_model(context.artifacts['cbm_model'])\n self.col_names = column_config['col_names']\n self.preserve_cols = column_config['preserve_neg_vals']\n\n def preprocess(self, data):\n \"\"\"\n Applies the pre-processing pipeline to the features given in the input dataset.\n\n :param data: Input dataset.\n :return: Transformed dataset.\n \"\"\"\n data = data[self.col_names]\n data = remove_inf_values(data)\n data = remove_negative_values(data, ignore_cols=self.preserve_cols)\n return self.pipeline.transform(data)\n\n def predict(self, context, model_input):\n X = self.preprocess(model_input)\n return self.clf.predict(X)\n" }, { "path": "ml_ids/models/gradient_boost/train.py", "content": "\"\"\"\nUtilities to train a machine learning estimator based on the Gradient Boosting algorithm using the CatBoost library.\n\"\"\"\nimport logging\nfrom collections import namedtuple\nimport pandas as pd\nfrom catboost import CatBoostClassifier, Pool\nfrom sklearn.preprocessing import FunctionTransformer\n\nfrom ml_ids.transform.preprocessing import create_pipeline\nfrom ml_ids.transform.sampling import upsample_minority_classes\nfrom ml_ids.model_selection import split_x_y\n\nLOGGER = logging.getLogger(__name__)\n\nGradientBoostHyperParams = namedtuple('GradientBoostHyperParams',\n ['nr_iterations', 'tree_depth', 'l2_reg', 'border_count', 'random_strength',\n 'task_type'])\n\n\ndef fit_pipeline(train_dataset):\n \"\"\"\n Creates and fits the scikit-learn pre-processing pipeline.\n\n :param train_dataset: Training dataset.\n :return: Tuple of (fitted scikit-learn pipeline, column names).\n \"\"\"\n cols_to_impute = train_dataset.columns[train_dataset.isna().any()].tolist()\n\n X_train, _ = split_x_y(train_dataset)\n\n pipeline, get_col_names = create_pipeline(X_train,\n imputer_strategy='median',\n imputer_cols=cols_to_impute,\n scaler=FunctionTransformer,\n scaler_args={'validate': False})\n pipeline.fit(X_train)\n return pipeline, get_col_names()\n\n\ndef preprocess_val_dataset(pipeline, val_dataset):\n \"\"\"\n Pre-processes the validation dataset.\n\n :param pipeline: Scikit-learn pipeline.\n :param val_dataset: Validation dataset.\n :return: Tuple of (transformed features, labels)\n \"\"\"\n X_val, y_val = split_x_y(val_dataset)\n X_val = pipeline.transform(X_val)\n\n return X_val, y_val.label_is_attack\n\n\ndef preprocess_train_dataset(pipeline, train_dataset, nr_attack_samples, random_state):\n \"\"\"\n Pre-processes the training dataset.\n\n :param pipeline: Scikit-learn pipeline.\n :param train_dataset: Training dataset.\n :param nr_attack_samples: Minimum number of attack samples per category. If the actual number of samples in the\n dataset is lower than this number the SMOTE algorithm will be used to upsample this category to have the requested\n number of samples.\n :return: Tuple of (transformed features, labels)\n \"\"\"\n X_train, y_train = split_x_y(train_dataset)\n X_train = pipeline.transform(X_train)\n\n X_train, y_train = upsample_minority_classes(X_train, y_train,\n min_samples=nr_attack_samples,\n random_state=random_state)\n\n return X_train, (y_train != 0).astype('int')\n\n\ndef calculate_class_weights(y_train):\n \"\"\"\n Calculates the class weights of the unique classes in the training labels.\n\n :param y_train: Training labels.\n :return: Array of class weights.\n \"\"\"\n minority_class_weight = len(y_train[y_train == 0]) / len(y_train[y_train == 1])\n return [1, minority_class_weight]\n\n\ndef train_gb_classifier(train_pool,\n val_pool,\n class_weights,\n nr_iterations,\n tree_depth,\n l2_reg,\n border_count,\n random_strength,\n task_type,\n random_state=None):\n \"\"\"\n Trains an estimator based on the Gradient Boosting algorithm using the CatBoost library.\n\n :param train_pool: Training dataset.\n :param val_pool: Validation dataset.\n :param class_weights: Class weights of the target labels.\n :param nr_iterations: The maximum number of trees that can be built when solving machine learning problems.\n :param tree_depth: Depth of a single tree.\n :param l2_reg: Coefficient at the L2 regularization term of the cost function.\n :param border_count: The number of splits for numerical features.\n :param random_strength: The amount of randomness to use for scoring splits when the tree structure is selected.\n :param task_type: The processing unit type to use for training (CPU | GPU).\n :param random_state: State to initialize the random number generator.\n :return: Trained CatBoost classifier.\n \"\"\"\n clf = CatBoostClassifier(loss_function='Logloss',\n iterations=nr_iterations,\n depth=tree_depth,\n l2_leaf_reg=l2_reg,\n border_count=border_count,\n random_strength=random_strength,\n task_type=task_type,\n class_weights=class_weights,\n verbose=1,\n random_seed=random_state)\n\n clf.fit(train_pool, eval_set=val_pool)\n return clf\n\n\ndef train_model(train_dataset: pd.DataFrame,\n val_dataset: pd.DataFrame,\n hyper_params: GradientBoostHyperParams,\n nr_attack_samples: int,\n random_seed: int = None):\n \"\"\"\n Trains an estimator based on the Gradient Boosting algorithm using the CatBoost library.\n\n :param train_dataset: Training dataset.\n :param val_dataset: Validation dataset.\n :param hyper_params: Hyper-parameters applied to the Gradient Boosting algorithm.\n :param nr_attack_samples: Minimum number of attack samples per category. If the actual number of samples in the\n dataset is lower than this number the SMOTE algorithm will be used to upsample this category to have the requested\n number of samples.\n :param random_seed: Seed to initialize the random number generator.\n :return: Tuple of (CatBoost classifier, pre-processing pipeline, column names)\n \"\"\"\n LOGGER.info('Training model with parameters [samples-per-attack-category=%s, hyperparams=%s]',\n nr_attack_samples,\n hyper_params)\n\n pipeline, col_names = fit_pipeline(train_dataset)\n\n X_train, y_train = preprocess_train_dataset(pipeline, train_dataset, nr_attack_samples, random_seed)\n train_pool = Pool(X_train, y_train)\n\n if val_dataset is not None:\n X_val, y_val = preprocess_val_dataset(pipeline, val_dataset)\n val_pool = Pool(X_val, y_val)\n else:\n val_pool = None\n\n clf = train_gb_classifier(train_pool=train_pool,\n val_pool=val_pool,\n class_weights=calculate_class_weights(y_train),\n nr_iterations=hyper_params.nr_iterations,\n tree_depth=hyper_params.tree_depth,\n l2_reg=hyper_params.l2_reg,\n border_count=hyper_params.border_count,\n random_strength=hyper_params.random_strength,\n task_type=hyper_params.task_type,\n random_state=random_seed)\n\n return clf, pipeline, col_names\n" }, { "path": "ml_ids/prediction.py", "content": "\"\"\"\nUtilities to create predictions given a Scikit-learn estimator and a dataset containing input features.\n\"\"\"\n\n\ndef predict_proba_positive(clf, X):\n \"\"\"\n Performs predictions for a binary classification task given a scikit-learn model.\n This function returns the class probability of the positive class.\n\n :param clf: Scikit-learn estimator.\n :param X: Dataset containing the samples.\n :return: numpy array containing the class probabilities of the positive class.\n \"\"\"\n return clf.predict_proba(X)[:, 1].reshape(-1)\n\n\ndef predict_decision_boundary(clf, X, decision_boundary=0.5):\n \"\"\"\n Performs predictions for a binary classification task given a scikit-learn model and a decision boundary.\n If the probability of a sample belonging to the positive class exceeds the decision boundary the positive label\n is assigned to the sample, otherwise the negative label is used.\n\n :param clf: Scikit-learn estimator.\n :param X: Dataset containing samples.\n :param decision_boundary: Decision boundary used to assign predictions to the positive class.\n :return: numpy array containing the binary predictions as one of the values {0, 1}.\n \"\"\"\n pred = predict_proba_positive(clf, X)\n return (pred >= decision_boundary).astype('int')\n" }, { "path": "ml_ids/tf_utils.py", "content": "\"\"\"\nUtility functions for TensorFlow.\n\"\"\"\nimport tensorflow as tf\n\n\ndef enable_gpu_memory_growth():\n \"\"\"\n Enables the experimental setting `allow_memory_growth` for GPU devices\n\n :return: None\n \"\"\"\n physical_devices = tf.config.experimental.list_physical_devices('GPU')\n tf.config.experimental.set_memory_growth(physical_devices[0], True)\n" }, { "path": "ml_ids/transform/__init__.py", "content": "" }, { "path": "ml_ids/transform/preprocessing.py", "content": "\"\"\"\nUtilities for data pre-processing.\n\"\"\"\nfrom typing import List\nimport numpy as np\nimport pandas as pd\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.exceptions import NotFittedError\nfrom sklearn.base import BaseEstimator\n\n\ndef remove_outliers(df: pd.DataFrame, zscore: int = 3) -> pd.DataFrame:\n \"\"\"\n Removes all rows from the given DataFrame containing outliers in any of the columns.\n\n :param df: Input DataFrame.\n :param zscore: z-score to use when calculating outliers.\n :return: The DataFrame with all outliers removed.\n \"\"\"\n scores = (df - df.mean()) / df.std(ddof=0).values\n return df[(np.abs(scores) < zscore).all(axis=1)]\n\n\ndef create_pipeline(df: pd.DataFrame,\n imputer_strategy: str = 'mean',\n imputer_cols: List[str] = None,\n scaler: BaseEstimator = StandardScaler,\n scaler_args: dict = None,\n cat_cols: List[str] = None,\n copy: bool = True):\n \"\"\"\n Creates a pipeline performing the following steps:\n - value imputation\n - value scaling\n - one-hot-encoding of categorical values.\n\n :param df: Input DataFrame.\n :param imputer_strategy: Imputer strategy applied to missing values.\n Allowed values are ['mean', 'median', 'most_frequent', 'constant'].\n :param imputer_cols: Columns to impute. If no columns are specified all columns will be imputed.\n :param scaler: Scikit-learn scaler to be applied to all values.\n :param scaler_args: Additional arguments forwarded to the specified scaler.\n :param cat_cols: Categorical columns to be one-hot-encoded.\n :param copy: If True, a copy of the input will be created.\n :return: A tuple containing the pipeline and a function returning the columns names after the pipeline has been\n fitted.\n \"\"\"\n\n def create_get_feature_names(p, imp, scl, cat):\n def get_feature_names():\n if not hasattr(p, 'transformers_'):\n raise AssertionError('Pipeline is not yet fitted.')\n\n try:\n cat_names = p.transformers_[2][1].get_feature_names(cat)\n except NotFittedError:\n cat_names = []\n return np.append(imp, np.append(scl, cat_names))\n\n return get_feature_names\n\n if scaler_args is None:\n scaler_args = {}\n\n cat_features = cat_cols if cat_cols else []\n num_features = [c for c in df.select_dtypes(include=[np.number]).columns.values if c not in cat_features]\n imp_features: List[str] = []\n\n if imputer_strategy is not None:\n imp_features = imputer_cols if imputer_cols else num_features\n\n scale_features = [f for f in num_features if f not in imp_features]\n\n imp_pipeline = Pipeline([\n ('imputer', SimpleImputer(missing_values=np.nan, strategy=imputer_strategy, copy=copy)),\n ('imp_scaler', scaler(**scaler_args))\n ])\n\n pipeline = ColumnTransformer([\n ('imp', imp_pipeline, imp_features),\n ('scl', scaler(**scaler_args), scale_features),\n ('one_hot', OneHotEncoder(categories='auto'), cat_features)\n ])\n\n return pipeline, create_get_feature_names(pipeline, imp_features, scale_features, cat_features)\n" }, { "path": "ml_ids/transform/sampling.py", "content": "\"\"\"\nUtilities to modify the amount of samples of specific categories in a datasets.\n\"\"\"\nimport numpy as np\nimport pandas as pd\nfrom imblearn.over_sampling import SMOTE, SMOTENC\nfrom typing import Tuple, List\n\n\ndef upsample_minority_classes(X: np.ndarray,\n y: pd.DataFrame,\n min_samples: int,\n random_state: int = None,\n cat_cols: List[int] = None,\n n_jobs: int = 24) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"\n Synthetic up-sampling of minority classes using `imblearn.over_sampling.SMOTE`.\n\n :param X: Predictor variables.\n :param y: Labels.\n :param min_samples: Minimum samples of each class.\n :param random_state: Random state.\n :param cat_cols: Column indices of categorical features.\n :param n_jobs: Number of threads to use.\n :return: A tuple containing the up-sampled X and y values.\n \"\"\"\n counts = y.label_cat.value_counts()\n sample_dict = {}\n\n for i in np.unique(y.label_cat):\n sample_dict[i] = max(counts[i], min_samples)\n\n if cat_cols:\n smote = SMOTENC(sampling_strategy=sample_dict,\n categorical_features=cat_cols,\n n_jobs=n_jobs,\n random_state=random_state)\n else:\n smote = SMOTE(sampling_strategy=sample_dict, n_jobs=n_jobs, random_state=random_state)\n\n x_s, y_s = smote.fit_resample(X, y.label_cat)\n return x_s, y_s\n\n\ndef create_sample_dict(df: pd.DataFrame,\n default_nr_samples: int,\n samples_per_label: dict = None) -> dict:\n \"\"\"\n Creates a dictionary containing the number of samples per label.\n\n :param df: Input DataFrame.\n :param default_nr_samples: Default number of samples per label.\n :param samples_per_label: Number of samples for specific labels.\n :return: Dictionary containing the number of samples per label.\n \"\"\"\n if samples_per_label is None:\n samples_per_label = {}\n\n sample_dict = df.label_cat.value_counts().to_dict()\n\n for label in sample_dict.keys():\n requested_samples = samples_per_label[label] if label in samples_per_label else default_nr_samples\n existing_samples = sample_dict[label] if label in sample_dict else 0\n sample_dict[label] = min(requested_samples, existing_samples)\n\n return sample_dict\n\n\ndef downsample(df: pd.DataFrame,\n default_nr_samples: int,\n samples_per_label: dict = None,\n random_state: int = None) -> pd.DataFrame:\n \"\"\"\n Downsamples the given DataFrame to contain at most `default_nr_samples` per instance of label.\n\n :param df: Input DataFrame.\n :param default_nr_samples: Default number of samples per label.\n :param samples_per_label: Number of samples for specific labels.\n :param random_state: Random state.\n :return: The downsampled DataFrame.\n \"\"\"\n if samples_per_label is None:\n samples_per_label = {}\n\n sample_dict = create_sample_dict(df, default_nr_samples, samples_per_label)\n return pd.concat([df[df.label_cat == l].sample(n=n, random_state=random_state) for l, n in sample_dict.items()])\n" }, { "path": "ml_ids/visualization.py", "content": "\"\"\"\nVisualization utilities for IPython Notebooks.\n\"\"\"\n# pylint: disable=import-error\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator\nfrom sklearn.metrics import confusion_matrix, classification_report, average_precision_score, precision_recall_curve\nfrom IPython.display import display\n\n\ndef plot_hist(hist,\n metrics=None,\n y_lim=None,\n size=(8, 5),\n ax=None):\n \"\"\"\n Plot a Keras history object.\n\n :param hist: The Keras history.\n :param metrics: A list of histories to plot.\n :param y_lim: Limits the y-axis.\n :param size: Size of the plot.\n :param ax: Axis to apply the plot.\n \"\"\"\n if metrics is None:\n metrics = ['loss', 'val_loss']\n\n fig_size = size if not ax else None\n\n df = pd.DataFrame(hist.history)[metrics]\n df.plot(figsize=fig_size, ax=ax)\n\n gca = ax if ax else plt.gca()\n gca.xaxis.set_major_locator(MaxNLocator(integer=True))\n\n if y_lim:\n gca.set_ylim(y_lim)\n\n if ax:\n ax.grid(True)\n else:\n plt.grid(True)\n plt.show()\n\n\ndef plot_confusion_matrix(y_true,\n y_pred,\n classes=None,\n size=(10, 10),\n normalize=False,\n title=None,\n print_raw=False,\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n\n :param y_true: True labels.\n :param y_pred: Predicted labels.\n :param classes: List of class names.\n :param size: Size of the plot.\n :param normalize: If True values of the confusion matrix will be normalized.\n :param title: Title of the plot.\n :param print_raw: If True the raw confusion matrix is printed.\n :param cmap: Color map\n \"\"\"\n if not title:\n if normalize:\n title = 'Normalized confusion matrix'\n else:\n title = 'Confusion matrix, without normalization'\n\n # Compute confusion matrix\n cm = confusion_matrix(y_true, y_pred)\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\n if print_raw:\n print(cm)\n\n fig, ax = plt.subplots(figsize=size)\n im = ax.matshow(cm, interpolation='nearest', cmap=cmap)\n ax.figure.colorbar(im, ax=ax)\n ax.set(title=title,\n ylabel='True label',\n xlabel='Predicted label')\n\n if classes is not None:\n x_labels = classes\n y_labels = classes\n\n ax.set(xticks=np.arange(cm.shape[1]),\n yticks=np.arange(cm.shape[0]),\n xticklabels=x_labels,\n yticklabels=y_labels)\n\n plt.margins(2)\n ax.tick_params(axis=\"x\", bottom=True, labelbottom=True, top=False, labeltop=False, rotation=45)\n\n # Rotate the tick labels and set their alignment.\n plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n rotation_mode=\"anchor\")\n\n # Loop over data dimensions and create text annotations.\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i in range(cm.shape[0]):\n for j in range(cm.shape[1]):\n ax.text(j, i, format(cm[i, j], fmt),\n ha=\"center\", va=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n fig.tight_layout()\n return ax\n\n\ndef identity(x):\n \"\"\"\n Identity function.\n \"\"\"\n return x\n\n\ndef plot_threshold(pred_train, pred_val, threshold, size=(15, 5), transform=identity):\n \"\"\"\n Plots the reconstruction errors of training and test samples and displays the classification threshold.\n\n :param pred_train: Predictions of training samples.\n :param pred_val: Predictions of validation samples.\n :param threshold: Classification threshold.\n :param size: Size of the plot.\n :param transform: Value transformation.\n \"\"\"\n _, ax = plt.subplots(figsize=size)\n sns.distplot(transform(pred_train.rec_error.values), hist=False, ax=ax, label='Train Benign')\n sns.distplot(transform(pred_val[pred_val.y_true == 0].rec_error.values), hist=False, ax=ax,\n label='Validation Benign')\n sns.distplot(transform(pred_val[pred_val.y_true == 1].rec_error.values), hist=False, ax=ax,\n label='Validation Attack')\n ax.axvline(transform(threshold), color='red', linestyle='--')\n ax.legend()\n\n\ndef get_misclassifications(y, y_true, pred):\n \"\"\"\n Calculates the misclassification rate for each label.\n\n :param y: Pandas DataFrame containing the target labels.\n :param y_true: True labels.\n :param pred: Predicted labels.\n :return: Pandas DataFrame containing the misclassification per label.\n \"\"\"\n misclassifications = y[y_true != pred]\n\n mc_df = pd.merge(pd.DataFrame({'misclassified': misclassifications.label.value_counts()}),\n pd.DataFrame({'total': y.label.value_counts()}),\n how='left', left_index=True, right_index=True)\n mc_df['percent_misclassified'] = mc_df.apply(lambda x: x[0] / x[1], axis=1)\n return mc_df.sort_values('percent_misclassified', ascending=False)\n\n\ndef print_binary_performance(y, y_true, pred, print_misclassifications=True, digits=3):\n \"\"\"\n Prints the performance of a binary classifier using\n - the classification report,\n - the confusion matrix and\n - the misclassification report.\n\n :param y: Pandas DataFrame containing the target labels (binary, categories).\n :param y_true: True labels.\n :param pred: Predicted labels.\n :param print_misclassifications: Binary indicator instructing that the misclassification report should be printed.\n :param digits: Number of digits used to print the classification report.\n :return: None\n \"\"\"\n print('Classification Report:')\n print('======================')\n print(classification_report(y_true, pred, digits=digits))\n\n print('Confusion Matrix:')\n print('=================')\n plot_confusion_matrix(y_true, pred, np.array(['Benign', 'Attack']), size=(5, 5))\n plt.show()\n\n if print_misclassifications:\n print('Misclassifications by attack category:')\n print('======================================')\n mc_df = get_misclassifications(y, y_true, pred)\n display(mc_df)\n\n\ndef plot_pr_curve(y_true, y_score, size=(8, 5), average='weighted'):\n \"\"\"\n Plots the precision-recall curve for a single estimator.\n\n :param y_true: True labels.\n :param y_score: Predicted probabilities.\n :param size: Size of the plot.\n :param average: Average parameter used for the calculation of the average precision score.\n :return: None\n \"\"\"\n precisions, recalls, _ = precision_recall_curve(y_true, y_score)\n pr_auc = average_precision_score(y_true, y_score, average=average)\n\n plt.figure(figsize=size)\n plt.plot(recalls, precisions, label='auc={}'.format(pr_auc))\n plt.title('Precision / Recall Curve')\n plt.xlabel('Recall')\n plt.ylabel('Precision')\n plt.legend(loc='lower left')\n plt.show()\n\n print('Average PR Score {}'.format(pr_auc))\n\n\ndef plot_pr_curves(y_true, y_score_dict, size=(8, 5), average='weighted'):\n \"\"\"\n Plots the precision-recall curve for a multiple estimators.\n\n :param y_true: True labels.\n :param y_score_dict: Dictionary containing the estimator name as keys and the predicted label probabilities\n as values.\n :param size: Size of the plot.\n :param average: Average parameter used for the calculation of the average precision score.\n :return: None\n \"\"\"\n plt.figure(figsize=size)\n\n for name, y_score in y_score_dict.items():\n precisions, recalls, _ = precision_recall_curve(y_true, y_score)\n pr_auc = average_precision_score(y_true, y_score, average=average)\n plt.plot(recalls, precisions, label='{} (AUC={})'.format(name, pr_auc))\n\n plt.title('Precision / Recall Curve')\n plt.xlabel('Recall')\n plt.ylabel('Precision')\n plt.legend(loc='lower left')\n plt.show()\n\n\ndef plot_pr_threshold_curves(y_true, y_pred_score, size=(20, 8)):\n \"\"\"\n Plots the precision-recall values for different probability thresholds.\n\n :param y_true: True labels.\n :param y_pred_score: Predicted probabilities.\n :param size: Size of the plot.\n :return: None\n \"\"\"\n precisions, recalls, thresholds = precision_recall_curve(y_true, y_pred_score)\n\n # plot precision / recall for different thresholds\n plt.figure(figsize=size)\n plt.plot(thresholds, precisions[:-1], label=\"Precision\")\n plt.plot(thresholds, recalls[:-1], label=\"Recall\")\n plt.title('Precision / Recall of different thresholds')\n plt.xlabel('Threshold')\n plt.ylabel('Precision / Recall')\n plt.legend(loc='lower right')\n plt.show()\n" }, { "path": "models/gradient_boost/envs/local/train.py", "content": "import json\nimport click\nimport mlflow\nimport shutil\nimport os\n\n\ndef merge(dict1, dict2):\n \"\"\"\n Merges two dictionaries by creating copies of the dictionaries.\n :param dict1: First dictionary to merge\n :param dict2: Second dictionary to merge\n :return: Merged dictionary\n \"\"\"\n d = dict(dict1)\n d.update(dict2)\n return d\n\n\n@click.command()\n@click.option('--train-path', type=click.Path(exists=True), required=True,\n help='Path to the train dataset in .h5 format.')\n@click.option('--val-path', type=click.Path(exists=True), required=True,\n help='Path to the train dataset in .h5 format.')\n@click.option('--test-path', type=click.Path(exists=True), required=True,\n help='Path to the train dataset in .h5 format.')\n@click.option('--output-path', type=click.Path(), required=True,\n help='Path to store the output.')\n@click.option('--param-path', type=click.Path(exists=True), required=True,\n help='Path to the training parameters.')\ndef train(train_path, val_path, test_path, output_path, param_path):\n with open(param_path, 'r') as f:\n params = json.load(f)\n\n shutil.rmtree(output_path, ignore_errors=True)\n os.makedirs(output_path, exist_ok=True)\n\n run_params = merge(params, {\n 'train_path': train_path,\n 'val_path': val_path,\n 'test_path': test_path,\n 'output_path': output_path,\n 'artifact_path': output_path,\n })\n\n mlflow.run('models/gradient_boost/project',\n parameters=run_params)\n\n\nif __name__ == '__main__':\n train()\n" }, { "path": "models/gradient_boost/envs/sagemaker/configs/deploy.json", "content": "{\n \"deploy\": {\n \"app_name\": \"ml-ids-classifier\",\n \"instance_type\": \"ml.t2.medium\",\n \"instance_count\": 1,\n \"region\": \"eu-west-1\"\n },\n \"role\": \"arn:aws:iam::763816190631:role/service-role/AmazonSageMaker-ExecutionRole-20191125T215860\",\n \"model_bucket\": \"s3://sagemaker-eu-west-1-763816190631\",\n \"model_artifact\": \"model.tar.gz\",\n \"model_name\": \"ml-ids-gb_mlflow_pyfunc\"\n}" }, { "path": "models/gradient_boost/envs/sagemaker/configs/train-cpu.json", "content": "{\n \"train\": {\n \"instance_type\": \"ml.m5.large\",\n \"instance_count\": 1,\n \"task_type\": \"CPU\"\n },\n \"role\": \"arn:aws:iam::763816190631:role/service-role/AmazonSageMaker-ExecutionRole-20191125T215860\",\n \"data\": {\n \"train\": \"s3://ml-ids-2018-sm/training\",\n \"val\": \"s3://ml-ids-2018-sm/validation\",\n \"test\": \"s3://ml-ids-2018-sm/testing\"\n },\n \"model_bucket\": \"s3://sagemaker-eu-west-1-763816190631\"\n}" }, { "path": "models/gradient_boost/envs/sagemaker/configs/train-gpu.json", "content": "{\n \"train\": {\n \"instance_type\": \"ml.p2.xlarge\",\n \"instance_count\": 1,\n \"task_type\": \"GPU\"\n },\n \"role\": \"arn:aws:iam::763816190631:role/service-role/AmazonSageMaker-ExecutionRole-20191125T215860\",\n \"data\": {\n \"train\": \"s3://ml-ids-2018-full/training\",\n \"val\": \"s3://ml-ids-2018-full/validation\",\n \"test\": \"s3://ml-ids-2018-full/testing\"\n },\n \"model_bucket\": \"s3://sagemaker-eu-west-1-763816190631\"\n}" }, { "path": "models/gradient_boost/envs/sagemaker/container/Dockerfile", "content": "FROM nvidia/cuda:10.1-base\n\n# Install Miniconda 3\nENV LANG=C.UTF-8 LC_ALL=C.UTF-8\nENV PATH /opt/conda/bin:$PATH\n\nRUN apt-get update --fix-missing && \\\n apt-get install -y wget bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 git mercurial subversion && \\\n apt-get clean\n\nRUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.7.12-Linux-x86_64.sh -O ~/miniconda.sh && \\\n /bin/bash ~/miniconda.sh -b -p /opt/conda && \\\n rm ~/miniconda.sh && \\\n /opt/conda/bin/conda clean -tipsy && \\\n ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \\\n echo \". /opt/conda/etc/profile.d/conda.sh\" >> ~/.bashrc && \\\n echo \"conda activate base\" >> ~/.bashrc && \\\n find /opt/conda/ -follow -type f -name '*.a' -delete && \\\n find /opt/conda/ -follow -type f -name '*.js.map' -delete && \\\n /opt/conda/bin/conda clean -afy\n\n# Copy Conda environment file\nCOPY models/gradient_boost/project/conda.yaml /opt/ml/code/conda.yaml\n\n# Install Conda environment\nRUN conda env create -f /opt/ml/code/conda.yaml\n\n# Copy project files\nADD ml_ids /opt/ml/code/ml_ids\nADD models/gradient_boost/project /opt/ml/code/models/gradient_boost/project\nCOPY setup.cfg /opt/ml/code/setup.cfg\nCOPY setup.py /opt/ml/code/setup.py\n\n# Activate conda env\nRUN echo \"source activate ml-ids-gradient-boost-catboost\" > ~/.bashrc\nENV PATH /opt/conda/envs/ml-ids-gradient-boost-catboost/bin:$PATH\n\n# Copy train script and make it executable\nCOPY models/gradient_boost/envs/sagemaker/container/train.py /opt/ml/code/train\nRUN chmod +x /opt/ml/code/train\nENV PATH=\"/opt/ml/code:${PATH}\"\n\nWORKDIR /opt/ml/code" }, { "path": "models/gradient_boost/envs/sagemaker/container/train.py", "content": "#!/usr/bin/env python\n\nimport sys\nimport os\nimport json\nimport traceback\nimport uuid\nimport mlflow\n\nprefix = '/opt/ml/'\n\noutput_path = os.path.join(prefix, 'output')\nmodel_path = os.path.join(prefix, 'model')\nparam_path = os.path.join(prefix, 'input/config/hyperparameters.json')\n\ninput_path = prefix + 'input/data'\ntraining_path = os.path.join(input_path, 'training')\nvalidation_path = os.path.join(input_path, 'validation')\ntesting_path = os.path.join(input_path, 'testing')\n\nmlflow_project_uri = os.path.join(prefix, 'code/models/gradient_boost/project')\nmlflow_out_path = os.path.join('/tmp', str(uuid.uuid4()))\n\n\ndef merge(dict1, dict2):\n d = dict(dict1)\n d.update(dict2)\n return d\n\n\nif __name__ == '__main__':\n print('Starting the training')\n\n try:\n with open(param_path, 'r') as tc:\n training_params = json.load(tc)\n\n training_file_path = os.path.join(training_path, 'train.h5')\n validation_file_path = os.path.join(validation_path, 'val.h5')\n testing_file_path = os.path.join(testing_path, 'test.h5')\n\n mlflow_params = merge(training_params, {\n 'train_path': training_file_path,\n 'val_path': validation_file_path,\n 'test_path': testing_file_path,\n 'output_path': mlflow_out_path,\n 'artifact_path': model_path\n })\n\n os.makedirs(mlflow_out_path, exist_ok=True)\n\n mlflow.run(mlflow_project_uri, parameters=mlflow_params, use_conda=False)\n print('Training complete.')\n\n sys.exit(0)\n except Exception as e:\n # Write out an error file. This will be returned as the failureReason in the\n # DescribeTrainingJob result.\n trc = traceback.format_exc()\n with open(os.path.join(output_path, 'failure'), 'w') as s:\n s.write('Exception during training: ' + str(e) + '\\n' + trc)\n # Printing this causes the exception to be in the training job logs, as well.\n print('Exception during training: ' + str(e) + '\\n' + trc, file=sys.stderr)\n # A non-zero exit code causes the training job to be marked as Failed.\n sys.exit(255)\n" }, { "path": "models/gradient_boost/envs/sagemaker/scripts/build_image.sh", "content": "#!/usr/bin/env bash\n\nimage_name=$1\nimage_version=$2\n\nif [ \"$image_name\" == \"\" ]\nthen\n echo \"Usage: $0 \"\n exit 1\nfi\n\nif [ \"$image_version\" == \"\" ]\nthen\n echo \"Usage: $1 \"\n exit 1\nfi\n\nfullname=\"${image_name}:${image_version}\"\n\necho \"Building image '${fullname}'\"\n\ndocker build -f models/gradient_boost/envs/sagemaker/container/Dockerfile -t ${fullname} ." }, { "path": "models/gradient_boost/envs/sagemaker/scripts/deploy.py", "content": "import click\nimport json\nimport boto3\nimport tarfile\nimport re\nimport logging\nfrom mlflow import sagemaker\n\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger(__name__)\n\n\ndef unpack(file):\n \"\"\"\n Unpacks compressed files of format `tar` and `tar.gz`.\n :param file: Filename.\n :return: None\n \"\"\"\n if file.endswith(\"tar.gz\"):\n tar = tarfile.open(file, \"r:gz\")\n tar.extractall()\n tar.close()\n elif file.endswith(\"tar\"):\n tar = tarfile.open(file, \"r:\")\n tar.extractall()\n tar.close()\n\n\n@click.command()\n@click.option('--config-path', type=click.Path(exists=True), required=True,\n help='Path to the config.')\n@click.option('--job-id', type=str, required=True,\n help='Unique ID of the training job. Model is retrieved from a subdirectory with this name.')\ndef deploy(config_path, job_id):\n with open(config_path, 'r') as f:\n config = json.load(f)\n\n app_name = config['deploy']['app_name']\n instance_type = config['deploy']['instance_type']\n instance_count = config['deploy']['instance_count']\n region = config['deploy']['region']\n role = config['role']\n model_name = config['model_name']\n model_bucket = re.sub('s3://', '', config['model_bucket'])\n model_artifact = config['model_artifact']\n model_path = '{}/output/{}'.format(job_id, model_artifact)\n\n logger.info('Deploying model with parameters '\n '[app-name=\"{}\", instance-type=\"{}\", instance-count={}, region=\"{}\", model-path=\"{}\"]'\n .format(app_name, instance_type, instance_count, region, model_path))\n\n s3 = boto3.client('s3')\n s3.download_file(model_bucket, model_path, model_artifact)\n\n unpack(model_artifact)\n\n sagemaker.deploy(app_name=app_name,\n model_uri=model_name,\n execution_role_arn=role,\n region_name=region,\n mode='replace',\n instance_type=instance_type,\n instance_count=instance_count)\n\n\nif __name__ == '__main__':\n deploy()\n" }, { "path": "models/gradient_boost/envs/sagemaker/scripts/push_image_to_ecr.sh", "content": "#!/usr/bin/env bash\n\nimage_name=$1\nimage_version=$2\n\nif [ \"$image_name\" == \"\" ]\nthen\n echo \"Usage: $0 \"\n exit 1\nfi\n\nif [ \"$image_version\" == \"\" ]\nthen\n echo \"Usage: $1 \"\n exit 1\nfi\n\n# Get the account number associated with the current IAM credentials\naccount=$(aws sts get-caller-identity --query Account --output text)\n\nif [ $? -ne 0 ]\nthen\n exit 255\nfi\n\n# Get the region defined in the current configuration (default to eu-west-1 if none defined)\nregion=$(aws configure get region)\nregion=${region:-eu-west-1}\n\nfullname=\"${account}.dkr.ecr.${region}.amazonaws.com/${image_name}:${image_version}\"\n\n# If the repository doesn't exist in ECR, create it.\n\naws ecr describe-repositories --repository-names \"${image_name}\" > /dev/null 2>&1\n\nif [ $? -ne 0 ]\nthen\n aws ecr create-repository --repository-name \"${image_name}\" > /dev/null\nfi\n\n# Get the login command from ECR and execute it directly\n$(aws ecr get-login --region ${region} --no-include-email)\n\n# Build the docker image locally with the image name and then push it to ECR\n# with the full name.\n\ndocker tag \"${image_name}:${image_version}\" ${fullname}\ndocker push ${fullname}\n\necho \"image-name=${fullname}\"" }, { "path": "models/gradient_boost/envs/sagemaker/scripts/train.py", "content": "import json\nimport click\nimport logging\nfrom sagemaker.estimator import Estimator\n\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger(__name__)\n\n\ndef create_performance_metric_regex(id):\n \"\"\"\n Creates the regex for a single performance metric.\n Format: metric_name: 0.12345\n :param id: Metric identifier.\n :return: Regex\n \"\"\"\n return rf'{id}:\\s*([\\d.]*)'\n\n\ndef create_metric_def(name, regex):\n \"\"\"\n Creates a metric definition for a single metric.\n :param name: Metric name.\n :param regex: Metric regex.\n :return: Metric definition as a `dict`.\n \"\"\"\n return {'Name': name, 'Regex': regex}\n\n\ndef get_metric_definitions():\n \"\"\"\n Creates the definitions for all metrics to monitor.\n :return: Metric definitions as a `list`.\n \"\"\"\n return [create_metric_def('train:loss', create_performance_metric_regex('learn')),\n create_metric_def('val:loss', create_performance_metric_regex('test')),\n create_metric_def('val:loss:best', r'bestTest\\s=\\s([\\d.]*)'),\n create_metric_def('test:pr_auc', create_performance_metric_regex('pr_auc')),\n create_metric_def('test:precision', create_performance_metric_regex('precision')),\n create_metric_def('test:recall', create_performance_metric_regex('recall')),\n create_metric_def('test:f1', create_performance_metric_regex('f1'))]\n\n\n@click.command()\n@click.option('--config-path', type=click.Path(exists=True), required=True,\n help='Path to the config.')\n@click.option('--param-path', type=click.Path(exists=True), required=True,\n help='Path to the training parameters.')\n@click.option('--image-name', type=str, required=True,\n help='Name of the training image')\n@click.option('--mode', type=click.Choice(['LOCAL', 'AWS'], case_sensitive=False), default='LOCAL',\n help='Training mode.')\n@click.option('--job-id', type=str, required=True,\n help='Unique ID of the training job. Model outputs will be stored in a subdirectory with this name.')\ndef train(config_path, param_path, image_name, mode, job_id):\n with open(config_path, 'r') as f:\n config = json.load(f)\n\n with open(param_path, 'r') as f:\n params = json.load(f)\n\n if mode == 'LOCAL':\n train_instance_type = 'local'\n params['task_type'] = 'CPU'\n else:\n train_instance_type = config['train']['instance_type']\n params['task_type'] = config['train']['task_type']\n\n train_instance_count = config['train']['instance_count']\n role = config['role']\n model_bucket = config['model_bucket']\n\n logger.info('Start training with parameters '\n '[job-id=\"{}\", image=\"{}\", mode=\"{}\", instance_type=\"{}\", instance_count={}, params={}]'\n .format(job_id, image_name, mode, train_instance_type, train_instance_count, params))\n\n estimator = Estimator(image_name=image_name,\n role=role,\n train_instance_count=train_instance_count,\n train_instance_type=train_instance_type,\n hyperparameters=params,\n output_path=model_bucket,\n metric_definitions=get_metric_definitions(),\n train_max_run=(2 * 60 * 60))\n\n estimator.fit(job_name=job_id,\n inputs={\n 'training': config['data']['train'],\n 'validation': config['data']['val'],\n 'testing': config['data']['test']\n })\n\n\nif __name__ == '__main__':\n train()\n" }, { "path": "models/gradient_boost/envs/sagemaker/scripts/undeploy.py", "content": "import click\nimport json\nfrom mlflow import sagemaker\n\n\n@click.command()\n@click.option('--config-path', type=click.Path(exists=True), required=True,\n help='Path to the config.')\ndef undeploy(config_path):\n with open(config_path, 'r') as f:\n config = json.load(f)\n\n app_name = config['deploy']['app_name']\n region = config['deploy']['region']\n\n sagemaker.delete(app_name=app_name, region_name=region)\n\n\nif __name__ == '__main__':\n undeploy()\n" }, { "path": "models/gradient_boost/project/MLproject", "content": "name: gradient_boost_model\n\nconda_env: conda.yaml\n\nentry_points:\n main:\n parameters:\n train_path: path\n val_path: path\n test_path: path\n output_path: path\n artifact_path: path\n use_val_set: {type: bool, default: True}\n nr_iterations: {type: int, default: 1000}\n tree_depth: {type: int, default: 6}\n l2_reg: {type: float, default: 3.0}\n border_count: {type: int, default: 254}\n random_strength: {type: int, default: 1}\n task_type: {type: str, default: 'GPU'}\n nr_samples_attack_category: {type: int, default: 1000}\n random_seed: {type: int, default: -1}\n command: \"pip install -e ../../../. &&\n python train.py --train-path {train_path}\n --val-path {val_path}\n --test-path {test_path}\n --output-path {output_path}\n --artifact-path {artifact_path}\n --use-val-set {use_val_set}\n --random-seed {random_seed}\n --nr-iterations {nr_iterations}\n --tree-depth {tree_depth}\n --l2-reg {l2_reg}\n --border-count {border_count}\n --random-strength {random_strength}\n --task-type {task_type}\n --nr-samples-attack-category {nr_samples_attack_category}\"" }, { "path": "models/gradient_boost/project/conda.yaml", "content": "name: ml-ids-gradient-boost-catboost\nchannels:\n - anaconda\n - conda-forge\n - defaults\ndependencies:\n - python=3.7\n - pip=19.2.3=py37_0\n - pandas=0.25.2=py37hb3f55d8_0\n - catboost=0.18.1=py37_0\n - imbalanced-learn=0.5.0=py_0\n - scikit-learn=0.21.3=py37hcdab131_0\n - scipy=1.3.1=py37h921218d_2\n - click=7.0=py37_0\n - cloudpickle=1.2.2=py_0\n - pip:\n - tables==3.6.1\n - keras==2.2.4\n - mlflow==1.4" }, { "path": "models/gradient_boost/project/train.py", "content": "import click\nimport logging\nimport mlflow\nimport mlflow.pyfunc\nimport pickle\nimport os\nimport shutil\nfrom catboost import Pool\nfrom ml_ids.data.dataset import load_dataset_hdf\nfrom ml_ids.data.metadata import FEATURES_NO_VARIANCE, FEATURES_TO_IGNORE, FEATURES_PRESERVE_NEG_COLUMNS\nfrom ml_ids.prediction import predict_proba_positive\nfrom ml_ids.model_selection import split_x_y\nfrom ml_ids.models.gradient_boost.train import train_model, GradientBoostHyperParams\nfrom ml_ids.models.gradient_boost.mlflow_wrapper import CatBoostWrapper\nfrom sklearn.metrics import average_precision_score, precision_score, recall_score, f1_score\nimport logging\n\nlogging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger(__name__)\n\n\ndef load_dataset(path):\n \"\"\"\n Loads a single dataset in `hdf` format.\n :param path: Dataset path.\n :return: Pandas DataFrame.\n \"\"\"\n return load_dataset_hdf(dataset_path=path,\n omit_cols=FEATURES_NO_VARIANCE + FEATURES_TO_IGNORE,\n preserve_neg_value_cols=FEATURES_PRESERVE_NEG_COLUMNS)\n\n\ndef load_train_val_test_dataset(train_path, val_path, test_path):\n \"\"\"\n Loads the train, validation and test datasets.\n :param train_path: Path to the train dataset.\n :param val_path: Path to the validation dataset.\n :param test_path: Path to the test dataset.\n :return: the `Tuple(train, val, test)` containing Pandas DataFrames.\n \"\"\"\n return load_dataset(train_path), load_dataset(val_path), load_dataset(test_path)\n\n\ndef measure_performance(clf, pipeline, dataset):\n \"\"\"\n Measures performance metrics on the given dataset.\n :param clf: Classifier to test.\n :param pipeline: Preprocessing pipeline.\n :param dataset: Dataset.\n :return: the `Tuple(pr_auc, precision, recall, f1)`.\n \"\"\"\n X, y = split_x_y(dataset)\n X = pipeline.transform(X)\n\n pool = Pool(X)\n y_true = y.label_is_attack\n\n pred_proba = predict_proba_positive(clf, pool)\n pred = clf.predict(pool)\n\n pr_auc = average_precision_score(y_true, pred_proba)\n precision = precision_score(y_true, pred)\n recall = recall_score(y_true, pred)\n f1 = f1_score(y_true, pred)\n return pr_auc, precision, recall, f1\n\n\ndef save_artifacts(cbm_model_path, classifier, pipeline_path, pipeline, col_config_path, column_config):\n \"\"\"\n Save training artifacts to disk.\n :param cbm_model_path: Path on disk where the classifier should be stored.\n :param classifier: Classifier to store.\n :param pipeline_path: Path on disk where the pipeline should be stored.\n :param pipeline: Pipeline to store.\n :param col_config_path: Path on disk where the config should be stored.\n :param column_config: Column config to store.\n :return: None\n \"\"\"\n classifier.save_model(cbm_model_path)\n with open(pipeline_path, 'wb') as f:\n pickle.dump(pipeline, f)\n with open(col_config_path, 'wb') as f:\n pickle.dump(column_config, f)\n\n\n@click.command()\n@click.option('--train-path', type=click.Path(exists=True), required=True,\n help='Path to the train dataset in .h5 format.')\n@click.option('--val-path', type=click.Path(exists=True), required=True,\n help='Path to the train dataset in .h5 format.')\n@click.option('--test-path', type=click.Path(exists=True), required=True,\n help='Path to the train dataset in .h5 format.')\n@click.option('--output-path', type=click.Path(exists=True), required=True,\n help='Path to store the output.')\n@click.option('--artifact-path', type=click.Path(exists=True), required=True,\n help='Path to store the artifacts.')\n@click.option('--use-val-set', type=bool, default=True,\n help='Determines if the evaluation dataset should be used for early stopping of the training process.'\n 'If set to False the evaluation dataset will be appended to the train dataset.')\n@click.option('--random-seed', type=int, default=None,\n help='Random seed.')\n@click.option('--nr-iterations', type=int, required=True)\n@click.option('--tree-depth', type=int, required=True)\n@click.option('--l2-reg', type=float, required=True)\n@click.option('--border-count', type=int, required=True)\n@click.option('--random-strength', type=int, required=True)\n@click.option('--task-type', type=click.Choice(['CPU', 'GPU'], case_sensitive=False), required=True)\n@click.option('--nr-samples-attack-category', type=int, required=True)\ndef train(train_path,\n val_path,\n test_path,\n output_path,\n artifact_path,\n use_val_set,\n random_seed,\n nr_iterations,\n tree_depth,\n l2_reg,\n border_count,\n random_strength,\n task_type,\n nr_samples_attack_category):\n shutil.rmtree(output_path, ignore_errors=True)\n os.makedirs(output_path, exist_ok=True)\n\n cbm_model_path = os.path.join(output_path, 'gradient_boost_model.cbm')\n pipeline_path = os.path.join(output_path, 'preprocessing_pipeline.pkl')\n col_config_path = os.path.join(output_path, 'column_config.pkl')\n mlflow_model_path = os.path.join(artifact_path, 'ml-ids-gb_mlflow_pyfunc')\n\n random_seed = None if random_seed == -1 else random_seed\n\n logger.info('Loading datasets...')\n train_dataset, val_dataset, test_dataset = load_train_val_test_dataset(train_path, val_path, test_path)\n\n if not use_val_set:\n logger.info('Evaluation dataset will not be used for early stopping. Merging with training dataset.')\n train_dataset = train_dataset.append(val_dataset)\n val_dataset = None\n else:\n logger.info('Evaluation dataset will be used for early stopping.')\n\n hyper_params = GradientBoostHyperParams(nr_iterations=nr_iterations,\n tree_depth=tree_depth,\n l2_reg=l2_reg,\n border_count=border_count,\n random_strength=random_strength,\n task_type=task_type)\n\n with mlflow.start_run():\n logger.info('Starting training...')\n clf, pipeline, column_names = train_model(train_dataset,\n val_dataset,\n hyper_params=hyper_params,\n nr_attack_samples=nr_samples_attack_category,\n random_seed=random_seed)\n\n pr_auc, precision, recall, f1 = measure_performance(clf, pipeline, test_dataset)\n logger.info('Estimator performance:')\n logger.info('pr_auc: %f', pr_auc)\n logger.info('precision: %f', precision)\n logger.info('recall: %f', recall)\n logger.info('f1: %f', f1)\n\n save_artifacts(cbm_model_path,\n clf,\n pipeline_path,\n pipeline,\n col_config_path,\n {\n 'col_names': column_names,\n 'preserve_neg_vals': FEATURES_PRESERVE_NEG_COLUMNS\n })\n\n mlflow.pyfunc.save_model(\n path=mlflow_model_path,\n python_model=CatBoostWrapper(),\n artifacts={\n 'cbm_model': cbm_model_path,\n 'pipeline': pipeline_path,\n 'col_config': col_config_path\n },\n conda_env='conda.yaml',\n code_path=['../../../ml_ids'])\n\n logger.info('Training completed.')\n\n\nif __name__ == '__main__':\n train()\n" }, { "path": "models/gradient_boost/training_params.json", "content": "{\n \"task_type\": \"GPU\",\n \"use_val_set\": true,\n \"nr_iterations\": 2000,\n \"tree_depth\": 10,\n \"l2_reg\": 4.813919374945952,\n \"border_count\": 254,\n \"random_strength\": 5,\n \"nr_samples_attack_category\": 100000\n}" }, { "path": "models/gradient_boost/training_params_quick_run.json", "content": "{\n \"task_type\": \"GPU\",\n \"nr_iterations\": 10,\n \"nr_samples_attack_category\": 1000,\n \"random_seed\": 42\n}" }, { "path": "notebooks/01_data-cleanup/data_cleanup.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Data Cleanup\\n\",\n \"\\n\",\n \"Before the CSE-CIC-IDS2018 dataset can be used for analysis and training the dataset has to be cleaned. In its raw format the dataset consists of ten individual csv files, each containing the recorded network traffic of a single day of operation, named after the day the traffic was recorded on.\\n\",\n \"\\n\",\n \"To conduct an initial analysis of the dataset a single file is loaded and dissected.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# set base path to the directory containing the csv files of the dataset\\n\",\n \"dataset_base_path = r'/path/to/dataset'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1. Removing invalid rows\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/anaconda3/envs/spark/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3057: DtypeWarning: Columns (0,1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78) have mixed types. Specify dtype option on import or set low_memory=False.\\n\",\n \" interactivity=interactivity, compiler=compiler, result=result)\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import os\\n\",\n \"\\n\",\n \"file_path = os.path.join(dataset_base_path, 'Thursday-01-03-2018_TrafficForML_CICFlowMeter.csv')\\n\",\n \"\\n\",\n \"df = pd.read_csv(file_path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Analyzing the output of `read_csv` shows that for most of the columns pandas could not infer a datatype.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 331125 entries, 0 to 331124\\n\",\n \"Data columns (total 80 columns):\\n\",\n \"Dst Port 331125 non-null object\\n\",\n \"Protocol 331125 non-null object\\n\",\n \"Timestamp 331125 non-null object\\n\",\n \"Flow Duration 331125 non-null object\\n\",\n \"Tot Fwd Pkts 331125 non-null object\\n\",\n \"Tot Bwd Pkts 331125 non-null object\\n\",\n \"TotLen Fwd Pkts 331125 non-null object\\n\",\n \"TotLen Bwd Pkts 331125 non-null object\\n\",\n \"Fwd Pkt Len Max 331125 non-null object\\n\",\n \"Fwd Pkt Len Min 331125 non-null object\\n\",\n \"Fwd Pkt Len Mean 331125 non-null object\\n\",\n \"Fwd Pkt Len Std 331125 non-null object\\n\",\n \"Bwd Pkt Len Max 331125 non-null object\\n\",\n \"Bwd Pkt Len Min 331125 non-null object\\n\",\n \"Bwd Pkt Len Mean 331125 non-null object\\n\",\n \"Bwd Pkt Len Std 331125 non-null object\\n\",\n \"Flow Byts/s 329291 non-null object\\n\",\n \"Flow Pkts/s 331125 non-null object\\n\",\n \"Flow IAT Mean 331125 non-null object\\n\",\n \"Flow IAT Std 331125 non-null object\\n\",\n \"Flow IAT Max 331125 non-null object\\n\",\n \"Flow IAT Min 331125 non-null object\\n\",\n \"Fwd IAT Tot 331125 non-null object\\n\",\n \"Fwd IAT Mean 331125 non-null object\\n\",\n \"Fwd IAT Std 331125 non-null object\\n\",\n \"Fwd IAT Max 331125 non-null object\\n\",\n \"Fwd IAT Min 331125 non-null object\\n\",\n \"Bwd IAT Tot 331125 non-null object\\n\",\n \"Bwd IAT Mean 331125 non-null object\\n\",\n \"Bwd IAT Std 331125 non-null object\\n\",\n \"Bwd IAT Max 331125 non-null object\\n\",\n \"Bwd IAT Min 331125 non-null object\\n\",\n \"Fwd PSH Flags 331125 non-null object\\n\",\n \"Bwd PSH Flags 331125 non-null object\\n\",\n \"Fwd URG Flags 331125 non-null object\\n\",\n \"Bwd URG Flags 331125 non-null object\\n\",\n \"Fwd Header Len 331125 non-null object\\n\",\n \"Bwd Header Len 331125 non-null object\\n\",\n \"Fwd Pkts/s 331125 non-null object\\n\",\n \"Bwd Pkts/s 331125 non-null object\\n\",\n \"Pkt Len Min 331125 non-null object\\n\",\n \"Pkt Len Max 331125 non-null object\\n\",\n \"Pkt Len Mean 331125 non-null object\\n\",\n \"Pkt Len Std 331125 non-null object\\n\",\n \"Pkt Len Var 331125 non-null object\\n\",\n \"FIN Flag Cnt 331125 non-null object\\n\",\n \"SYN Flag Cnt 331125 non-null object\\n\",\n \"RST Flag Cnt 331125 non-null object\\n\",\n \"PSH Flag Cnt 331125 non-null object\\n\",\n \"ACK Flag Cnt 331125 non-null object\\n\",\n \"URG Flag Cnt 331125 non-null object\\n\",\n \"CWE Flag Count 331125 non-null object\\n\",\n \"ECE Flag Cnt 331125 non-null object\\n\",\n \"Down/Up Ratio 331125 non-null object\\n\",\n \"Pkt Size Avg 331125 non-null object\\n\",\n \"Fwd Seg Size Avg 331125 non-null object\\n\",\n \"Bwd Seg Size Avg 331125 non-null object\\n\",\n \"Fwd Byts/b Avg 331125 non-null object\\n\",\n \"Fwd Pkts/b Avg 331125 non-null object\\n\",\n \"Fwd Blk Rate Avg 331125 non-null object\\n\",\n \"Bwd Byts/b Avg 331125 non-null object\\n\",\n \"Bwd Pkts/b Avg 331125 non-null object\\n\",\n \"Bwd Blk Rate Avg 331125 non-null object\\n\",\n \"Subflow Fwd Pkts 331125 non-null object\\n\",\n \"Subflow Fwd Byts 331125 non-null object\\n\",\n \"Subflow Bwd Pkts 331125 non-null object\\n\",\n \"Subflow Bwd Byts 331125 non-null object\\n\",\n \"Init Fwd Win Byts 331125 non-null object\\n\",\n \"Init Bwd Win Byts 331125 non-null object\\n\",\n \"Fwd Act Data Pkts 331125 non-null object\\n\",\n \"Fwd Seg Size Min 331125 non-null object\\n\",\n \"Active Mean 331125 non-null object\\n\",\n \"Active Std 331125 non-null object\\n\",\n \"Active Max 331125 non-null object\\n\",\n \"Active Min 331125 non-null object\\n\",\n \"Idle Mean 331125 non-null object\\n\",\n \"Idle Std 331125 non-null object\\n\",\n \"Idle Max 331125 non-null object\\n\",\n \"Idle Min 331125 non-null object\\n\",\n \"Label 331125 non-null object\\n\",\n \"dtypes: object(80)\\n\",\n \"memory usage: 202.1+ MB\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.info()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Querying the `info()` method of the dataframe shows that pandas inferred all columns as `object` columns as opposed to numerical columns which would be appropriate for most of them.\\n\",\n \"In order to understand why the columns are interpreted as `object`s low cardinality columns are analyzed to show individual values.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"6 170066\\n\",\n \"17 95674\\n\",\n \"6 42833\\n\",\n \"17 15378\\n\",\n \"0 4596\\n\",\n \"0 2553\\n\",\n \"Protocol 25\\n\",\n \"Name: Protocol, dtype: int64\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['Protocol'].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 268629\\n\",\n \"0 60520\\n\",\n \"1 1707\\n\",\n \"1 244\\n\",\n \"FIN Flag Cnt 25\\n\",\n \"Name: FIN Flag Cnt, dtype: int64\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['FIN Flag Cnt'].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The unique values indicate the existence of the column name as values in the dataset. \\n\",\n \"A visual examination of the input file confirms that the headers are present multiple times within the file, interweaved with the raw data rows. This suggests that a single file was created by concatenating mulitple csv files duplicating the headers in the process.\\n\",\n \"To fix this issue all columns containing the headers are removed from the dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = df[~df['Dst Port'].str.contains('Dst Port', na=False)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the next steps the dataframe is exported to a temporary csv file in order to read it again with the correct column datatypes. \\n\",\n \"Furthermore the column names are converted to lowercase with non-word characters removed for easier access of the columns.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import re\\n\",\n \"\\n\",\n \"tmp_path = os.path.join(dataset_base_path, 'tmp')\\n\",\n \"\\n\",\n \"if not os.path.exists(tmp_path):\\n\",\n \" os.mkdir(tmp_path)\\n\",\n \"\\n\",\n \"column_name_regex = re.compile(r\\\"\\\\W\\\", re.IGNORECASE)\\n\",\n \"df.columns = [column_name_regex.sub('_', c.lower()) for c in df.columns]\\n\",\n \"\\n\",\n \"tmp_file_path = os.path.join(tmp_path, 'Thursday-01-03-2018_TrafficForML_CICFlowMeter_duplicate_headers_removed.csv')\\n\",\n \"\\n\",\n \"df.to_csv(tmp_file_path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2. Removing invalid values\\n\",\n \"\\n\",\n \"Now the temporary file is loaded with the following datatype definitions. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"types = {\\n\",\n \" 'dst_port': 'uint32',\\n\",\n \" 'protocol': 'uint8',\\n\",\n \" 'timestamp': 'object',\\n\",\n \" 'flow_duration': 'int64',\\n\",\n \" 'tot_fwd_pkts': 'uint32',\\n\",\n \" 'tot_bwd_pkts': 'uint32',\\n\",\n \" 'totlen_fwd_pkts': 'uint32',\\n\",\n \" 'totlen_bwd_pkts': 'uint32',\\n\",\n \" 'fwd_pkt_len_max': 'uint16',\\n\",\n \" 'fwd_pkt_len_min': 'uint16',\\n\",\n \" 'fwd_pkt_len_mean': 'float32',\\n\",\n \" 'fwd_pkt_len_std': 'float32',\\n\",\n \" 'bwd_pkt_len_max': 'uint16',\\n\",\n \" 'bwd_pkt_len_min': 'uint16',\\n\",\n \" 'bwd_pkt_len_mean': 'float32',\\n\",\n \" 'bwd_pkt_len_std': 'float32',\\n\",\n \" 'flow_byts_s': 'float64',\\n\",\n \" 'flow_pkts_s': 'float64',\\n\",\n \" 'flow_iat_mean': 'float32',\\n\",\n \" 'flow_iat_std': 'float32',\\n\",\n \" 'flow_iat_max': 'int64',\\n\",\n \" 'flow_iat_min': 'int64',\\n\",\n \" 'fwd_iat_tot': 'int64',\\n\",\n \" 'fwd_iat_mean': 'float32',\\n\",\n \" 'fwd_iat_std': 'float32',\\n\",\n \" 'fwd_iat_max': 'int64',\\n\",\n \" 'fwd_iat_min': 'int64',\\n\",\n \" 'bwd_iat_tot': 'uint32',\\n\",\n \" 'bwd_iat_mean': 'float32',\\n\",\n \" 'bwd_iat_std': 'float32',\\n\",\n \" 'bwd_iat_max': 'uint32',\\n\",\n \" 'bwd_iat_min': 'uint32',\\n\",\n \" 'fwd_psh_flags': 'uint8',\\n\",\n \" 'bwd_psh_flags': 'uint8',\\n\",\n \" 'fwd_urg_flags': 'uint8',\\n\",\n \" 'bwd_urg_flags': 'uint8',\\n\",\n \" 'fwd_header_len': 'uint32',\\n\",\n \" 'bwd_header_len': 'uint32',\\n\",\n \" 'flow_byts_s': 'float32',\\n\",\n \" 'bwd_pkts_s': 'float32',\\n\",\n \" 'pkt_len_min': 'uint16',\\n\",\n \" 'pkt_len_max': 'uint16',\\n\",\n \" 'pkt_len_mean': 'float32',\\n\",\n \" 'pkt_len_std': 'float32',\\n\",\n \" 'pkt_len_var': 'float32',\\n\",\n \" 'fin_flag_cnt': 'uint8',\\n\",\n \" 'syn_flag_cnt': 'uint8',\\n\",\n \" 'rst_flag_cnt': 'uint8',\\n\",\n \" 'psh_flag_cnt': 'uint8',\\n\",\n \" 'ack_flag_cnt': 'uint8',\\n\",\n \" 'urg_flag_cnt': 'uint8',\\n\",\n \" 'cwe_flag_count': 'uint8',\\n\",\n \" 'ece_flag_cnt': 'uint8',\\n\",\n \" 'down_up_ratio': 'uint16',\\n\",\n \" 'pkt_size_avg': 'float32',\\n\",\n \" 'fwd_seg_size_avg': 'float32',\\n\",\n \" 'bwd_seg_size_avg': 'float32',\\n\",\n \" 'fwd_byts_b_avg': 'uint8',\\n\",\n \" 'fwd_pkts_b_avg': 'uint8',\\n\",\n \" 'fwd_blk_rate_avg': 'uint8',\\n\",\n \" 'bwd_byts_b_avg': 'uint8',\\n\",\n \" 'bwd_pkts_b_avg': 'uint8',\\n\",\n \" 'bwd_blk_rate_avg': 'uint8',\\n\",\n \" 'subflow_fwd_pkts': 'uint32',\\n\",\n \" 'subflow_fwd_byts': 'uint32',\\n\",\n \" 'subflow_bwd_pkts': 'uint32',\\n\",\n \" 'subflow_bwd_byts': 'uint32',\\n\",\n \" 'init_fwd_win_byts': 'int32',\\n\",\n \" 'init_bwd_win_byts': 'int32',\\n\",\n \" 'fwd_act_data_pkts': 'uint32',\\n\",\n \" 'fwd_seg_size_min': 'uint8',\\n\",\n \" 'active_mean': 'float32',\\n\",\n \" 'active_std': 'float32',\\n\",\n \" 'active_max': 'uint32',\\n\",\n \" 'active_min': 'uint32',\\n\",\n \" 'idle_mean': 'float32',\\n\",\n \" 'idle_std': 'float32',\\n\",\n \" 'idle_max': 'uint64',\\n\",\n \" 'idle_min': 'uint64',\\n\",\n \" 'label': 'category'\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"cannot safely convert passed user dtype of float32 for object dtyped data in column 17\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32mpandas/_libs/parsers.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;31mTypeError\\u001b[0m: Cannot cast array from dtype('O') to dtype('float32') according to the rule 'safe'\",\n \"\\nDuring handling of the above exception, another exception occurred:\\n\",\n \"\\u001b[0;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mdf\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mpd\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread_csv\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mtmp_file_path\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mdtype\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mtypes\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/anaconda3/envs/spark/lib/python3.6/site-packages/pandas/io/parsers.py\\u001b[0m in \\u001b[0;36mparser_f\\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\\u001b[0m\\n\\u001b[1;32m 700\\u001b[0m skip_blank_lines=skip_blank_lines)\\n\\u001b[1;32m 701\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 702\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0m_read\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mfilepath_or_buffer\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mkwds\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 703\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 704\\u001b[0m \\u001b[0mparser_f\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__name__\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mname\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/anaconda3/envs/spark/lib/python3.6/site-packages/pandas/io/parsers.py\\u001b[0m in \\u001b[0;36m_read\\u001b[0;34m(filepath_or_buffer, kwds)\\u001b[0m\\n\\u001b[1;32m 433\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 434\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m--> 435\\u001b[0;31m \\u001b[0mdata\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mparser\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mnrows\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 436\\u001b[0m \\u001b[0;32mfinally\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 437\\u001b[0m \\u001b[0mparser\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mclose\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/anaconda3/envs/spark/lib/python3.6/site-packages/pandas/io/parsers.py\\u001b[0m in \\u001b[0;36mread\\u001b[0;34m(self, nrows)\\u001b[0m\\n\\u001b[1;32m 1137\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mread\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnrows\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1138\\u001b[0m \\u001b[0mnrows\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0m_validate_integer\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'nrows'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnrows\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1139\\u001b[0;31m \\u001b[0mret\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_engine\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mnrows\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1140\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1141\\u001b[0m \\u001b[0;31m# May alter columns / col_dict\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/anaconda3/envs/spark/lib/python3.6/site-packages/pandas/io/parsers.py\\u001b[0m in \\u001b[0;36mread\\u001b[0;34m(self, nrows)\\u001b[0m\\n\\u001b[1;32m 1993\\u001b[0m \\u001b[0;32mdef\\u001b[0m \\u001b[0mread\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mself\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnrows\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mNone\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1994\\u001b[0m \\u001b[0;32mtry\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 1995\\u001b[0;31m \\u001b[0mdata\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_reader\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mnrows\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1996\\u001b[0m \\u001b[0;32mexcept\\u001b[0m \\u001b[0mStopIteration\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1997\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_first_chunk\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32mpandas/_libs/parsers.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.parsers.TextReader.read\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;32mpandas/_libs/parsers.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;32mpandas/_libs/parsers.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.parsers.TextReader._read_rows\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;32mpandas/_libs/parsers.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.parsers.TextReader._convert_column_data\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;32mpandas/_libs/parsers.pyx\\u001b[0m in \\u001b[0;36mpandas._libs.parsers.TextReader._convert_tokens\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;31mValueError\\u001b[0m: cannot safely convert passed user dtype of float32 for object dtyped data in column 17\"\n ]\n }\n ],\n \"source\": [\n \"df = pd.read_csv(tmp_file_path, dtype=types)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The error indicates that column 17 (Flow Byts/s) cannot be parsed. Another visual examination of the file reveals the existence of the string `Infinity` in multiple rows of this column.\\n\",\n \"The `read_csv()` method of pandas is not able to correctly parse this value as it only recognizes the strings `inf/-inf` as a valid representation of infinity.\\n\",\n \"\\n\",\n \"To fix this problem all occurrences of `Infinity` are replaced by the string `inf`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/anaconda3/envs/spark/lib/python3.6/site-packages/IPython/core/interactiveshell.py:3057: DtypeWarning: Columns (17,18) have mixed types. Specify dtype option on import or set low_memory=False.\\n\",\n \" interactivity=interactivity, compiler=compiler, result=result)\\n\"\n ]\n }\n ],\n \"source\": [\n \"df = pd.read_csv(tmp_file_path)\\n\",\n \"\\n\",\n \"df_infinity_fixed = df.replace('Infinity', 'inf')\\n\",\n \"\\n\",\n \"tmp_file_path_inf = os.path.join(tmp_path, 'Thursday-01-03-2018_TrafficForML_CICFlowMeter_infinity_fixed.csv')\\n\",\n \"\\n\",\n \"df_infinity_fixed.to_csv(tmp_file_path_inf)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After fixing the infinity values the file can successfully be loaded with the given datatypes.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df = pd.read_csv(tmp_file_path_inf, dtype=types)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 331100 entries, 0 to 331099\\n\",\n \"Data columns (total 82 columns):\\n\",\n \"Unnamed: 0 331100 non-null int64\\n\",\n \"Unnamed: 0.1 331100 non-null int64\\n\",\n \"dst_port 331100 non-null uint32\\n\",\n \"protocol 331100 non-null uint8\\n\",\n \"timestamp 331100 non-null object\\n\",\n \"flow_duration 331100 non-null int64\\n\",\n \"tot_fwd_pkts 331100 non-null uint32\\n\",\n \"tot_bwd_pkts 331100 non-null uint32\\n\",\n \"totlen_fwd_pkts 331100 non-null uint32\\n\",\n \"totlen_bwd_pkts 331100 non-null uint32\\n\",\n \"fwd_pkt_len_max 331100 non-null uint16\\n\",\n \"fwd_pkt_len_min 331100 non-null uint16\\n\",\n \"fwd_pkt_len_mean 331100 non-null float32\\n\",\n \"fwd_pkt_len_std 331100 non-null float32\\n\",\n \"bwd_pkt_len_max 331100 non-null uint16\\n\",\n \"bwd_pkt_len_min 331100 non-null uint16\\n\",\n \"bwd_pkt_len_mean 331100 non-null float32\\n\",\n \"bwd_pkt_len_std 331100 non-null float32\\n\",\n \"flow_byts_s 329266 non-null float32\\n\",\n \"flow_pkts_s 331100 non-null float64\\n\",\n \"flow_iat_mean 331100 non-null float32\\n\",\n \"flow_iat_std 331100 non-null float32\\n\",\n \"flow_iat_max 331100 non-null int64\\n\",\n \"flow_iat_min 331100 non-null int64\\n\",\n \"fwd_iat_tot 331100 non-null int64\\n\",\n \"fwd_iat_mean 331100 non-null float32\\n\",\n \"fwd_iat_std 331100 non-null float32\\n\",\n \"fwd_iat_max 331100 non-null int64\\n\",\n \"fwd_iat_min 331100 non-null int64\\n\",\n \"bwd_iat_tot 331100 non-null uint32\\n\",\n \"bwd_iat_mean 331100 non-null float32\\n\",\n \"bwd_iat_std 331100 non-null float32\\n\",\n \"bwd_iat_max 331100 non-null uint32\\n\",\n \"bwd_iat_min 331100 non-null uint32\\n\",\n \"fwd_psh_flags 331100 non-null uint8\\n\",\n \"bwd_psh_flags 331100 non-null uint8\\n\",\n \"fwd_urg_flags 331100 non-null uint8\\n\",\n \"bwd_urg_flags 331100 non-null uint8\\n\",\n \"fwd_header_len 331100 non-null uint32\\n\",\n \"bwd_header_len 331100 non-null uint32\\n\",\n \"fwd_pkts_s 331100 non-null float64\\n\",\n \"bwd_pkts_s 331100 non-null float32\\n\",\n \"pkt_len_min 331100 non-null uint16\\n\",\n \"pkt_len_max 331100 non-null uint16\\n\",\n \"pkt_len_mean 331100 non-null float32\\n\",\n \"pkt_len_std 331100 non-null float32\\n\",\n \"pkt_len_var 331100 non-null float32\\n\",\n \"fin_flag_cnt 331100 non-null uint8\\n\",\n \"syn_flag_cnt 331100 non-null uint8\\n\",\n \"rst_flag_cnt 331100 non-null uint8\\n\",\n \"psh_flag_cnt 331100 non-null uint8\\n\",\n \"ack_flag_cnt 331100 non-null uint8\\n\",\n \"urg_flag_cnt 331100 non-null uint8\\n\",\n \"cwe_flag_count 331100 non-null uint8\\n\",\n \"ece_flag_cnt 331100 non-null uint8\\n\",\n \"down_up_ratio 331100 non-null uint16\\n\",\n \"pkt_size_avg 331100 non-null float32\\n\",\n \"fwd_seg_size_avg 331100 non-null float32\\n\",\n \"bwd_seg_size_avg 331100 non-null float32\\n\",\n \"fwd_byts_b_avg 331100 non-null uint8\\n\",\n \"fwd_pkts_b_avg 331100 non-null uint8\\n\",\n \"fwd_blk_rate_avg 331100 non-null uint8\\n\",\n \"bwd_byts_b_avg 331100 non-null uint8\\n\",\n \"bwd_pkts_b_avg 331100 non-null uint8\\n\",\n \"bwd_blk_rate_avg 331100 non-null uint8\\n\",\n \"subflow_fwd_pkts 331100 non-null uint32\\n\",\n \"subflow_fwd_byts 331100 non-null uint32\\n\",\n \"subflow_bwd_pkts 331100 non-null uint32\\n\",\n \"subflow_bwd_byts 331100 non-null uint32\\n\",\n \"init_fwd_win_byts 331100 non-null int32\\n\",\n \"init_bwd_win_byts 331100 non-null int32\\n\",\n \"fwd_act_data_pkts 331100 non-null uint32\\n\",\n \"fwd_seg_size_min 331100 non-null uint8\\n\",\n \"active_mean 331100 non-null float32\\n\",\n \"active_std 331100 non-null float32\\n\",\n \"active_max 331100 non-null uint32\\n\",\n \"active_min 331100 non-null uint32\\n\",\n \"idle_mean 331100 non-null float32\\n\",\n \"idle_std 331100 non-null float32\\n\",\n \"idle_max 331100 non-null uint64\\n\",\n \"idle_min 331100 non-null uint64\\n\",\n \"label 331100 non-null category\\n\",\n \"dtypes: category(1), float32(22), float64(2), int32(2), int64(8), object(1), uint16(7), uint32(17), uint64(2), uint8(20)\\n\",\n \"memory usage: 95.7+ MB\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Infinity values of flow_byts_s: 1085\\n\",\n \"Null values of flow_byts_s: 1834\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f\\\"Infinity values of flow_byts_s: {df[df['flow_byts_s'] == np.inf]['dst_port'].count()}\\\")\\n\",\n \"print(f\\\"Null values of flow_byts_s: {df[df['flow_byts_s'].isnull()]['dst_port'].count()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3. Cleanup Script\\n\",\n \"\\n\",\n \"In summary the following clean-up steps must be applied to all files of the dataset:\\n\",\n \"1. Removal of duplicate headers contained as rows of the dataset.\\n\",\n \"2. Substitution of occurrences of `Infinity` with `inf`\\n\",\n \"3. Renaming the column names to remove whitespaces and non-word characters\\n\",\n \"\\n\",\n \"The following script processes all files of the dataset and stores the output files using a name describing the attack types of the flows contained in the files rather than the date of the flows.\\n\",\n \"\\n\",\n \"Remark: The file `Thuesday-20-02-2018_TrafficForML_CICFlowMeter.csv` contains four columns (`Flow ID`, `Src IP`, `Dst IP`, `Src Port`) not present in any of the other files. As those columns are not required they are dropped upon loading the file.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import os\\n\",\n \"import re\\n\",\n \"\\n\",\n \"csv_files = {\\n\",\n \" 'Wednesday-28-02-2018_TrafficForML_CICFlowMeter.csv': 'infiltration_28-02-2018.csv',\\n\",\n \" 'Thursday-01-03-2018_TrafficForML_CICFlowMeter.csv': 'infiltration_01-03-2018.csv',\\n\",\n \" 'Friday-02-03-2018_TrafficForML_CICFlowMeter.csv': 'bot_02-03-2018.csv',\\n\",\n \" 'Thursday-22-02-2018_TrafficForML_CICFlowMeter.csv': 'bruteforce-web-xss_sql-injection_22-02-2018.csv',\\n\",\n \" 'Thursday-15-02-2018_TrafficForML_CICFlowMeter.csv': 'dos-goldeneye-slowloris_15-02-2018.csv',\\n\",\n \" 'Thuesday-20-02-2018_TrafficForML_CICFlowMeter.csv': 'ddos-loic-http-loic-udp_20-02-2018.csv',\\n\",\n \" 'Wednesday-21-02-2018_TrafficForML_CICFlowMeter.csv': 'ddos-loic-udp_hoic_21-02-2018.csv',\\n\",\n \" 'Wednesday-14-02-2018_TrafficForML_CICFlowMeter.csv': 'bruteforce-ftp-ssh_14-02-2018.csv',\\n\",\n \" 'Friday-16-02-2018_TrafficForML_CICFlowMeter.csv': 'dos-slowhttp-hulk_16-02-2018.csv',\\n\",\n \" 'Friday-23-02-2018_TrafficForML_CICFlowMeter.csv': 'bruteforce-web-xss_sql-injection_23-02-2018.csv'\\n\",\n \"}\\n\",\n \"\\n\",\n \"column_name_regex = re.compile(r\\\"\\\\W\\\", re.IGNORECASE)\\n\",\n \"processed_dir = 'processed'\\n\",\n \"processed_path = os.path.join(dataset_base_path, processed_dir)\\n\",\n \"\\n\",\n \"def remove_headers(f): \\n\",\n \" return f[~f['Dst Port'].str.contains('Dst Port', na=False)]\\n\",\n \"\\n\",\n \"def replace_infinity(f):\\n\",\n \" return f.replace('Infinity', 'inf', inplace=True)\\n\",\n \"\\n\",\n \"def remove_non_word_chars_from_column_names(f):\\n\",\n \" return [column_name_regex.sub('_', c.lower()) for c in df.columns]\\n\",\n \" \\n\",\n \"if not os.path.exists(processed_path):\\n\",\n \" os.mkdir(processed_path) \\n\",\n \" \\n\",\n \"for f, out in csv_files.items():\\n\",\n \" file_path = os.path.join(dataset_base_path, f)\\n\",\n \" output_path = os.path.join(dataset_base_path, processed_dir, out)\\n\",\n \" \\n\",\n \" df = pd.read_csv(file_path, dtype=str).drop(columns=['Flow ID', 'Src IP', 'Dst IP', 'Src Port'], errors='ignore')\\n\",\n \" df = remove_headers(df)\\n\",\n \" replace_infinity(df)\\n\",\n \" df.columns = remove_non_word_chars_from_column_names(df)\\n\",\n \" df.to_csv(output_path, index=False)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/02_exploratory-data-analysis/exploratory_data_analysis.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exploratory Data Analysis\\n\",\n \"\\n\",\n \"This notebook conducts an exploratory data analysis of the CSE-CIC-IDS2018 dataset. The notebook aims to answer the following questions:\\n\",\n \"1. How many benign and malicious network flows are contained in the dataset?\\n\",\n \"2. How many network flows are contained per attack type?\\n\",\n \"3. Is there a strong correlation between certain features?\\n\",\n \"4. Which features show a correlation with the binary class of a network flow?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Dataset Preparation\\n\",\n \"\\n\",\n \"As a first step the dataset must be loaded and prepared for analysis. The following code imports all necessary libraries and loads the full dataset into a Pandas dataframe.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"import glob\\n\",\n \"import os\\n\",\n \"from scipy import stats\\n\",\n \"from scipy.stats import ks_2samp\\n\",\n \"\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# set base path to the directory containing the csv files of the dataset\\n\",\n \"dataset_base_path = r'/path/to/dataset'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"types = {\\n\",\n \" 'dst_port': 'uint32',\\n\",\n \" 'protocol': 'uint8',\\n\",\n \" 'timestamp': 'object',\\n\",\n \" 'flow_duration': 'int64',\\n\",\n \" 'tot_fwd_pkts': 'uint32',\\n\",\n \" 'tot_bwd_pkts': 'uint32',\\n\",\n \" 'totlen_fwd_pkts': 'uint32',\\n\",\n \" 'totlen_bwd_pkts': 'uint32',\\n\",\n \" 'fwd_pkt_len_max': 'uint16',\\n\",\n \" 'fwd_pkt_len_min': 'uint16',\\n\",\n \" 'fwd_pkt_len_mean': 'float32',\\n\",\n \" 'fwd_pkt_len_std': 'float32',\\n\",\n \" 'bwd_pkt_len_max': 'uint16',\\n\",\n \" 'bwd_pkt_len_min': 'uint16',\\n\",\n \" 'bwd_pkt_len_mean': 'float32',\\n\",\n \" 'bwd_pkt_len_std': 'float32',\\n\",\n \" 'flow_byts_s': 'float64',\\n\",\n \" 'flow_pkts_s': 'float64',\\n\",\n \" 'flow_iat_mean': 'float32',\\n\",\n \" 'flow_iat_std': 'float32',\\n\",\n \" 'flow_iat_max': 'int64',\\n\",\n \" 'flow_iat_min': 'int64',\\n\",\n \" 'fwd_iat_tot': 'int64',\\n\",\n \" 'fwd_iat_mean': 'float32',\\n\",\n \" 'fwd_iat_std': 'float32',\\n\",\n \" 'fwd_iat_max': 'int64',\\n\",\n \" 'fwd_iat_min': 'int64',\\n\",\n \" 'bwd_iat_tot': 'uint32',\\n\",\n \" 'bwd_iat_mean': 'float32',\\n\",\n \" 'bwd_iat_std': 'float32',\\n\",\n \" 'bwd_iat_max': 'uint32',\\n\",\n \" 'bwd_iat_min': 'uint32',\\n\",\n \" 'fwd_psh_flags': 'uint8',\\n\",\n \" 'bwd_psh_flags': 'uint8',\\n\",\n \" 'fwd_urg_flags': 'uint8',\\n\",\n \" 'bwd_urg_flags': 'uint8',\\n\",\n \" 'fwd_header_len': 'uint32',\\n\",\n \" 'bwd_header_len': 'uint32',\\n\",\n \" 'fwd_pkts_s': 'float32',\\n\",\n \" 'bwd_pkts_s': 'float32',\\n\",\n \" 'pkt_len_min': 'uint16',\\n\",\n \" 'pkt_len_max': 'uint16',\\n\",\n \" 'pkt_len_mean': 'float32',\\n\",\n \" 'pkt_len_std': 'float32',\\n\",\n \" 'pkt_len_var': 'float32',\\n\",\n \" 'fin_flag_cnt': 'uint8',\\n\",\n \" 'syn_flag_cnt': 'uint8',\\n\",\n \" 'rst_flag_cnt': 'uint8',\\n\",\n \" 'psh_flag_cnt': 'uint8',\\n\",\n \" 'ack_flag_cnt': 'uint8',\\n\",\n \" 'urg_flag_cnt': 'uint8',\\n\",\n \" 'cwe_flag_count': 'uint8',\\n\",\n \" 'ece_flag_cnt': 'uint8',\\n\",\n \" 'down_up_ratio': 'uint16',\\n\",\n \" 'pkt_size_avg': 'float32',\\n\",\n \" 'fwd_seg_size_avg': 'float32',\\n\",\n \" 'bwd_seg_size_avg': 'float32',\\n\",\n \" 'fwd_byts_b_avg': 'uint8',\\n\",\n \" 'fwd_pkts_b_avg': 'uint8',\\n\",\n \" 'fwd_blk_rate_avg': 'uint8',\\n\",\n \" 'bwd_byts_b_avg': 'uint8',\\n\",\n \" 'bwd_pkts_b_avg': 'uint8',\\n\",\n \" 'bwd_blk_rate_avg': 'uint8',\\n\",\n \" 'subflow_fwd_pkts': 'uint32',\\n\",\n \" 'subflow_fwd_byts': 'uint32',\\n\",\n \" 'subflow_bwd_pkts': 'uint32',\\n\",\n \" 'subflow_bwd_byts': 'uint32',\\n\",\n \" 'init_fwd_win_byts': 'int32',\\n\",\n \" 'init_bwd_win_byts': 'int32',\\n\",\n \" 'fwd_act_data_pkts': 'uint32',\\n\",\n \" 'fwd_seg_size_min': 'uint8',\\n\",\n \" 'active_mean': 'float32',\\n\",\n \" 'active_std': 'float32',\\n\",\n \" 'active_max': 'uint32',\\n\",\n \" 'active_min': 'uint32',\\n\",\n \" 'idle_mean': 'float32',\\n\",\n \" 'idle_std': 'float32',\\n\",\n \" 'idle_max': 'uint64',\\n\",\n \" 'idle_min': 'uint64',\\n\",\n \" 'label': 'category'\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"csv_files = glob.glob(os.path.join(dataset_base_path, '*.csv'))\\n\",\n \"\\n\",\n \"df = pd.concat((pd.read_csv(f, dtype=types) for f in csv_files))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Filling missing values\\n\",\n \"\\n\",\n \"The dataset contains `inf` values which should be substituted as they cannot be used effectively in calculations. For this purpose all columns containing `inf` values are identified.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Columns containing infinity values ['flow_byts_s', 'flow_pkts_s']\\n\"\n ]\n }\n ],\n \"source\": [\n \"inf_columns = [c for c in df.columns if df[df[c] == np.inf][c].count() > 0]\\n\",\n \"\\n\",\n \"print(f'Columns containing infinity values {inf_columns}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The two columns containing `inf` values are `flow_byts_s` and `flow_pkts_s`. \\n\",\n \"The `inf` values are replaced with `nan` values, subsequently imputing all `nan` values of those columns with the mean of the columns.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for col in inf_columns:\\n\",\n \" df[col].replace([np.inf, -np.inf], np.nan, inplace=True)\\n\",\n \" mean = df[col].mean()\\n\",\n \" df[col].fillna(mean, inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Label creation\\n\",\n \"\\n\",\n \"To analyze the dataset in terms of binary classification (benign/attack) and multi-classification (benign/attack-type) addtional label columns are created:\\n\",\n \"1. `label_is_attack` specifies if a network flow represents a benign or an malicious flow.\\n\",\n \"2. `label_is_attack_[attack_type]` specifies if a network flow represents a certain type of attack.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df['label'] = df.label.astype('category')\\n\",\n \"df['label_code'] = df['label'].cat.codes\\n\",\n \"df['label_is_attack'] = df.label.apply(lambda x: 0 if x == 'Benign' else 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"attack_types = [a for a in df.label.value_counts().index.tolist() if a != 'Benign']\\n\",\n \"\\n\",\n \"for a in attack_types:\\n\",\n \" l = 'label_is_attack_' + a.replace('-', ' ').replace(' ', '_').lower()\\n\",\n \" df[l] = df.label.apply(lambda x: 1 if x == a else 0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Dataset Overview\\n\",\n \"\\n\",\n \"The basic information of the dataset reveals that the whole dataset consists of *16232943* network flows and that all `null` values were eliminated.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Int64Index: 16232943 entries, 0 to 1048574\\n\",\n \"Data columns (total 96 columns):\\n\",\n \"dst_port 16232943 non-null uint32\\n\",\n \"protocol 16232943 non-null uint8\\n\",\n \"timestamp 16232943 non-null object\\n\",\n \"flow_duration 16232943 non-null int64\\n\",\n \"tot_fwd_pkts 16232943 non-null uint32\\n\",\n \"tot_bwd_pkts 16232943 non-null uint32\\n\",\n \"totlen_fwd_pkts 16232943 non-null uint32\\n\",\n \"totlen_bwd_pkts 16232943 non-null uint32\\n\",\n \"fwd_pkt_len_max 16232943 non-null uint16\\n\",\n \"fwd_pkt_len_min 16232943 non-null uint16\\n\",\n \"fwd_pkt_len_mean 16232943 non-null float32\\n\",\n \"fwd_pkt_len_std 16232943 non-null float32\\n\",\n \"bwd_pkt_len_max 16232943 non-null uint16\\n\",\n \"bwd_pkt_len_min 16232943 non-null uint16\\n\",\n \"bwd_pkt_len_mean 16232943 non-null float32\\n\",\n \"bwd_pkt_len_std 16232943 non-null float32\\n\",\n \"flow_byts_s 16232943 non-null float64\\n\",\n \"flow_pkts_s 16232943 non-null float64\\n\",\n \"flow_iat_mean 16232943 non-null float32\\n\",\n \"flow_iat_std 16232943 non-null float32\\n\",\n \"flow_iat_max 16232943 non-null int64\\n\",\n \"flow_iat_min 16232943 non-null int64\\n\",\n \"fwd_iat_tot 16232943 non-null int64\\n\",\n \"fwd_iat_mean 16232943 non-null float32\\n\",\n \"fwd_iat_std 16232943 non-null float32\\n\",\n \"fwd_iat_max 16232943 non-null int64\\n\",\n \"fwd_iat_min 16232943 non-null int64\\n\",\n \"bwd_iat_tot 16232943 non-null uint32\\n\",\n \"bwd_iat_mean 16232943 non-null float32\\n\",\n \"bwd_iat_std 16232943 non-null float32\\n\",\n \"bwd_iat_max 16232943 non-null uint32\\n\",\n \"bwd_iat_min 16232943 non-null uint32\\n\",\n \"fwd_psh_flags 16232943 non-null uint8\\n\",\n \"bwd_psh_flags 16232943 non-null uint8\\n\",\n \"fwd_urg_flags 16232943 non-null uint8\\n\",\n \"bwd_urg_flags 16232943 non-null uint8\\n\",\n \"fwd_header_len 16232943 non-null uint32\\n\",\n \"bwd_header_len 16232943 non-null uint32\\n\",\n \"fwd_pkts_s 16232943 non-null float32\\n\",\n \"bwd_pkts_s 16232943 non-null float32\\n\",\n \"pkt_len_min 16232943 non-null uint16\\n\",\n \"pkt_len_max 16232943 non-null uint16\\n\",\n \"pkt_len_mean 16232943 non-null float32\\n\",\n \"pkt_len_std 16232943 non-null float32\\n\",\n \"pkt_len_var 16232943 non-null float32\\n\",\n \"fin_flag_cnt 16232943 non-null uint8\\n\",\n \"syn_flag_cnt 16232943 non-null uint8\\n\",\n \"rst_flag_cnt 16232943 non-null uint8\\n\",\n \"psh_flag_cnt 16232943 non-null uint8\\n\",\n \"ack_flag_cnt 16232943 non-null uint8\\n\",\n \"urg_flag_cnt 16232943 non-null uint8\\n\",\n \"cwe_flag_count 16232943 non-null uint8\\n\",\n \"ece_flag_cnt 16232943 non-null uint8\\n\",\n \"down_up_ratio 16232943 non-null uint16\\n\",\n \"pkt_size_avg 16232943 non-null float32\\n\",\n \"fwd_seg_size_avg 16232943 non-null float32\\n\",\n \"bwd_seg_size_avg 16232943 non-null float32\\n\",\n \"fwd_byts_b_avg 16232943 non-null uint8\\n\",\n \"fwd_pkts_b_avg 16232943 non-null uint8\\n\",\n \"fwd_blk_rate_avg 16232943 non-null uint8\\n\",\n \"bwd_byts_b_avg 16232943 non-null uint8\\n\",\n \"bwd_pkts_b_avg 16232943 non-null uint8\\n\",\n \"bwd_blk_rate_avg 16232943 non-null uint8\\n\",\n \"subflow_fwd_pkts 16232943 non-null uint32\\n\",\n \"subflow_fwd_byts 16232943 non-null uint32\\n\",\n \"subflow_bwd_pkts 16232943 non-null uint32\\n\",\n \"subflow_bwd_byts 16232943 non-null uint32\\n\",\n \"init_fwd_win_byts 16232943 non-null int32\\n\",\n \"init_bwd_win_byts 16232943 non-null int32\\n\",\n \"fwd_act_data_pkts 16232943 non-null uint32\\n\",\n \"fwd_seg_size_min 16232943 non-null uint8\\n\",\n \"active_mean 16232943 non-null float32\\n\",\n \"active_std 16232943 non-null float32\\n\",\n \"active_max 16232943 non-null uint32\\n\",\n \"active_min 16232943 non-null uint32\\n\",\n \"idle_mean 16232943 non-null float32\\n\",\n \"idle_std 16232943 non-null float32\\n\",\n \"idle_max 16232943 non-null uint64\\n\",\n \"idle_min 16232943 non-null uint64\\n\",\n \"label 16232943 non-null category\\n\",\n \"label_code 16232943 non-null int8\\n\",\n \"label_is_attack 16232943 non-null int64\\n\",\n \"label_is_attack_ddos_attack_hoic 16232943 non-null int64\\n\",\n \"label_is_attack_ddos_attacks_loic_http 16232943 non-null int64\\n\",\n \"label_is_attack_dos_attacks_hulk 16232943 non-null int64\\n\",\n \"label_is_attack_bot 16232943 non-null int64\\n\",\n \"label_is_attack_ftp_bruteforce 16232943 non-null int64\\n\",\n \"label_is_attack_ssh_bruteforce 16232943 non-null int64\\n\",\n \"label_is_attack_infilteration 16232943 non-null int64\\n\",\n \"label_is_attack_dos_attacks_slowhttptest 16232943 non-null int64\\n\",\n \"label_is_attack_dos_attacks_goldeneye 16232943 non-null int64\\n\",\n \"label_is_attack_dos_attacks_slowloris 16232943 non-null int64\\n\",\n \"label_is_attack_ddos_attack_loic_udp 16232943 non-null int64\\n\",\n \"label_is_attack_brute_force__web 16232943 non-null int64\\n\",\n \"label_is_attack_brute_force__xss 16232943 non-null int64\\n\",\n \"label_is_attack_sql_injection 16232943 non-null int64\\n\",\n \"dtypes: category(1), float32(22), float64(2), int32(2), int64(21), int8(1), object(1), uint16(7), uint32(17), uint64(2), uint8(20)\\n\",\n \"memory usage: 6.3+ GB\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.info(verbose=True, null_counts=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1. Number of benign network flows in relation to malicious flows\\n\",\n \"\\n\",\n \"Plotting the numbers of benign network flows versus malicious network flows shows that the dataset is heavily skewed in favor of benign network flows which account for ~83% of all data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, ax = plt.subplots(figsize=(7, 5))\\n\",\n \"sns.countplot(x='label_is_attack', data=df, order = df['label_is_attack'].value_counts().index, ax=ax)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
label_is_attackpercentage
0134847080.8307
127482350.1693
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" label_is_attack percentage\\n\",\n \"0 13484708 0.8307\\n\",\n \"1 2748235 0.1693\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_is_attack = df.groupby('label_is_attack').agg({'label_is_attack':'first', 'label_is_attack':'count'}).rename_axis(None).sort_values('label_is_attack', ascending=False)\\n\",\n \"df_is_attack['percentage'] = df_is_attack / df_is_attack.loc[:'label_is_attack'].sum()\\n\",\n \"df_is_attack\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2. Number of flows per attack type\\n\",\n \"\\n\",\n \"The following graph shows the number of flows accounting for the different attack types. \\n\",\n \"The graph reveals an under-representation of attack types in the following categories compared with the other attack types:\\n\",\n \"* DoS attacks-GoldenEye\\n\",\n \"* DoS attacks-Slowloris\\n\",\n \"* DDOS attack-LOIC-UDP\\n\",\n \"* Brute Force-Web\\n\",\n \"* Brute Force-XSS\\n\",\n \"* SQL Injection \\t\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, ax = plt.subplots(figsize=(20, 7))\\n\",\n \"ax.set(xscale='log')\\n\",\n \"sns.countplot(y='label', data=df, order = df['label'].value_counts().index, ax=ax)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
label
Benign13484708
DDOS attack-HOIC686012
DDoS attacks-LOIC-HTTP576191
DoS attacks-Hulk461912
Bot286191
FTP-BruteForce193360
SSH-Bruteforce187589
Infilteration161934
DoS attacks-SlowHTTPTest139890
DoS attacks-GoldenEye41508
DoS attacks-Slowloris10990
DDOS attack-LOIC-UDP1730
Brute Force -Web611
Brute Force -XSS230
SQL Injection87
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" label\\n\",\n \"Benign 13484708\\n\",\n \"DDOS attack-HOIC 686012\\n\",\n \"DDoS attacks-LOIC-HTTP 576191\\n\",\n \"DoS attacks-Hulk 461912\\n\",\n \"Bot 286191\\n\",\n \"FTP-BruteForce 193360\\n\",\n \"SSH-Bruteforce 187589\\n\",\n \"Infilteration 161934\\n\",\n \"DoS attacks-SlowHTTPTest 139890\\n\",\n \"DoS attacks-GoldenEye 41508\\n\",\n \"DoS attacks-Slowloris 10990\\n\",\n \"DDOS attack-LOIC-UDP 1730\\n\",\n \"Brute Force -Web 611\\n\",\n \"Brute Force -XSS 230\\n\",\n \"SQL Injection 87\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.groupby('label').agg({'label':'first', 'label':'count'}).rename_axis(None).sort_values('label', ascending=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3. Feature correlations\\n\",\n \"\\n\",\n \"To analyze the correlation between different features a heatmap is created based on the correlation matrix of the dataset.\\n\",\n \"The heatmap shows strong correlations for a number of feature-pairs.\\n\",\n \"\\n\",\n \"One such feature-pair is `tot_fwd_pkts` (Total packets in the forward direction) and `fwd_header_len` (Total bytes used for headers in the forward direction) which makes sense as an increase in the number of packets leads to a higher number of bytes used as headers.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"corr = df.drop(columns=['label', 'timestamp']).corr()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(25, 20))\\n\",\n \"sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, linewidths=.5, cmap=sns.diverging_palette(620, 10, as_cmap=True))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Strong correlations \\n\",\n \"\\n\",\n \"To get an overview of the features with a strong correlation the following listing shows all feature correlations with an absolute value > 0.8. Some of those features might be removed before training a classifier with the dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"fwd_urg_flags cwe_flag_count 1.000000\\n\",\n \"tot_fwd_pkts subflow_fwd_pkts 1.000000\\n\",\n \"fwd_pkt_len_mean fwd_seg_size_avg 1.000000\\n\",\n \"tot_bwd_pkts subflow_bwd_pkts 1.000000\\n\",\n \"totlen_fwd_pkts subflow_fwd_byts 1.000000\\n\",\n \"fwd_psh_flags syn_flag_cnt 1.000000\\n\",\n \"bwd_pkt_len_mean bwd_seg_size_avg 1.000000\\n\",\n \"totlen_bwd_pkts subflow_bwd_byts 1.000000\\n\",\n \"flow_iat_min fwd_iat_min 0.999996\\n\",\n \"flow_iat_max fwd_iat_max 0.999994\\n\",\n \"rst_flag_cnt ece_flag_cnt 0.999987\\n\",\n \"flow_duration fwd_iat_tot 0.999986\\n\",\n \"flow_iat_std fwd_iat_std 0.999981\\n\",\n \"flow_iat_mean fwd_iat_mean 0.999963\\n\",\n \"subflow_fwd_pkts fwd_act_data_pkts 0.999189\\n\",\n \"tot_fwd_pkts fwd_act_data_pkts 0.999189\\n\",\n \"bwd_header_len subflow_bwd_pkts 0.997798\\n\",\n \"tot_bwd_pkts bwd_header_len 0.997798\\n\",\n \"bwd_header_len subflow_bwd_byts 0.996040\\n\",\n \"totlen_bwd_pkts bwd_header_len 0.996038\\n\",\n \"fwd_header_len subflow_fwd_pkts 0.995571\\n\",\n \"tot_fwd_pkts fwd_header_len 0.995571\\n\",\n \"subflow_bwd_pkts subflow_bwd_byts 0.993508\\n\",\n \"tot_bwd_pkts subflow_bwd_byts 0.993508\\n\",\n \" totlen_bwd_pkts 0.993507\\n\",\n \"totlen_bwd_pkts subflow_bwd_pkts 0.993507\\n\",\n \"pkt_len_mean pkt_size_avg 0.992937\\n\",\n \"idle_std idle_max 0.992282\\n\",\n \"fwd_header_len fwd_act_data_pkts 0.991466\\n\",\n \"idle_mean idle_max 0.981809\\n\",\n \" idle_std 0.980647\\n\",\n \"fwd_iat_std idle_mean 0.974598\\n\",\n \"flow_iat_std idle_mean 0.974562\\n\",\n \"bwd_pkt_len_max bwd_pkt_len_std 0.969805\\n\",\n \"pkt_len_max pkt_len_std 0.965556\\n\",\n \"fwd_pkt_len_max fwd_pkt_len_std 0.960132\\n\",\n \"bwd_pkt_len_max pkt_len_max 0.957785\\n\",\n \"flow_iat_std flow_iat_min 0.957612\\n\",\n \" fwd_iat_min 0.957597\\n\",\n \"flow_iat_min fwd_iat_std 0.957595\\n\",\n \"fwd_iat_std fwd_iat_min 0.957591\\n\",\n \"flow_iat_std idle_std 0.956770\\n\",\n \"fwd_iat_std idle_std 0.956756\\n\",\n \" idle_max 0.956540\\n\",\n \"flow_iat_std idle_max 0.956535\\n\",\n \"bwd_pkt_len_std pkt_len_max 0.952218\\n\",\n \"flow_iat_max idle_max 0.948219\\n\",\n \"fwd_iat_max idle_max 0.948214\\n\",\n \"active_mean active_max 0.947891\\n\",\n \"bwd_pkt_len_mean pkt_len_mean 0.947487\\n\",\n \"pkt_len_mean bwd_seg_size_avg 0.947487\\n\",\n \"flow_pkts_s fwd_pkts_s 0.946846\\n\",\n \"bwd_pkt_len_std pkt_len_std 0.946080\\n\",\n \"bwd_pkt_len_mean pkt_size_avg 0.941024\\n\",\n \"pkt_size_avg bwd_seg_size_avg 0.941024\\n\",\n \"fwd_iat_max idle_std 0.940914\\n\",\n \"flow_iat_max idle_std 0.940913\\n\",\n \"flow_iat_min idle_mean 0.937020\\n\",\n \"fwd_iat_min idle_mean 0.936994\\n\",\n \"fwd_pkt_len_min pkt_len_min 0.936270\\n\",\n \"flow_iat_max idle_mean 0.930951\\n\",\n \"fwd_iat_max idle_mean 0.930937\\n\",\n \"flow_iat_min idle_max 0.927754\\n\",\n \"fwd_iat_min idle_max 0.927741\\n\",\n \"flow_iat_min idle_std 0.923615\\n\",\n \"fwd_iat_min idle_std 0.923611\\n\",\n \"bwd_pkt_len_max pkt_len_std 0.915972\\n\",\n \"active_mean active_min 0.907860\\n\",\n \"fwd_iat_std fwd_iat_max 0.895749\\n\",\n \"flow_iat_max fwd_iat_std 0.895745\\n\",\n \"flow_iat_std flow_iat_max 0.895736\\n\",\n \" fwd_iat_max 0.895729\\n\",\n \"bwd_iat_mean bwd_iat_min 0.893595\\n\",\n \"bwd_iat_std bwd_iat_max 0.878564\\n\",\n \"fwd_pkt_len_std fwd_seg_size_avg 0.877572\\n\",\n \"fwd_pkt_len_mean fwd_pkt_len_std 0.877572\\n\",\n \"fwd_pkt_len_max fwd_pkt_len_mean 0.877540\\n\",\n \" fwd_seg_size_avg 0.877540\\n\",\n \"label_code label_is_attack 0.866984\\n\",\n \"pkt_len_mean pkt_len_std 0.865838\\n\",\n \"pkt_len_std bwd_seg_size_avg 0.851521\\n\",\n \"bwd_pkt_len_mean pkt_len_std 0.851521\\n\",\n \"pkt_len_std pkt_size_avg 0.844944\\n\",\n \"protocol bwd_pkt_len_min 0.844385\\n\",\n \"fwd_iat_tot fwd_iat_min 0.814424\\n\",\n \"flow_duration fwd_iat_min 0.814414\\n\",\n \"flow_iat_min fwd_iat_tot 0.814411\\n\",\n \"flow_duration flow_iat_min 0.814401\\n\",\n \"fwd_pkt_len_max pkt_len_max 0.808898\\n\",\n \"protocol pkt_len_min 0.805709\\n\",\n \"dtype: float64\\n\"\n ]\n }\n ],\n \"source\": [\n \"def get_abs_correlations(df, t):\\n\",\n \" labels_to_drop = set()\\n\",\n \" cols = df.columns.drop(['label', 'timestamp'])\\n\",\n \" for i in range(0, df.shape[1]-2):\\n\",\n \" for j in range(0, i+1):\\n\",\n \" labels_to_drop.add((cols[i], cols[j]))\\n\",\n \" \\n\",\n \" c = corr.abs().unstack().drop(labels=labels_to_drop).sort_values(ascending=False)\\n\",\n \" return c[c > t]\\n\",\n \"\\n\",\n \"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\\n\",\n \" print(get_abs_correlations(df, 0.8))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4. Features having a correlation with the binary class of network flows\\n\",\n \"\\n\",\n \"In the next step features correlating to the binary attack label are identified. The features with the highest correlations are analyzed via their basic statistics and distribution to evaluate if those features might be relevant in predicting the label of the network flow.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"fwd_seg_size_min 0.429296\\n\",\n \"bwd_pkts_s 0.260203\\n\",\n \"ack_flag_cnt 0.220465\\n\",\n \"init_fwd_win_byts 0.218390\\n\",\n \"flow_pkts_s 0.148600\\n\",\n \"Name: label_is_attack, dtype: float64\\n\"\n ]\n }\n ],\n \"source\": [\n \"corr_is_attack = corr.label_is_attack.filter(regex=r'^(?!label).*', axis=0)\\n\",\n \"\\n\",\n \"with pd.option_context('display.max_rows', None, 'display.max_columns', None):\\n\",\n \" print(corr_is_attack[corr_is_attack.notnull()].sort_values(ascending=False)[:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def remove_outliers(data, col, zscore_thres=3):\\n\",\n \" return data[(np.abs(stats.zscore(data[[col]])) < zscore_thres).all(axis=1)]\\n\",\n \"\\n\",\n \"\\n\",\n \"def plot(fn, height, data, label, col, scale='', with_outliers=True, xlim=[], bins=10):\\n\",\n \" fig, ax = plt.subplots(figsize=(18, height))\\n\",\n \"\\n\",\n \" d = data if with_outliers else remove_outliers(data, col)\\n\",\n \" \\n\",\n \" if xlim:\\n\",\n \" ax.set_xlim(xlim)\\n\",\n \" \\n\",\n \" if scale:\\n\",\n \" ax.set(xscale=scale)\\n\",\n \" \\n\",\n \" fn(d, label, col, ax, bins)\\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \" \\n\",\n \"def plot_box(data, label, col, scale='', with_outliers=True, xlim=[]):\\n\",\n \" plot(lambda d, l, c, ax, b: sns.boxplot(y=l, x=c, data=d, ax=ax, orient='h', width=0.4), 2, data, label, col, scale, with_outliers, xlim)\\n\",\n \" \\n\",\n \" \\n\",\n \"def plot_distribution(data, label, col, scale='', with_outliers=True, xlim=[], bins=10):\\n\",\n \" plot(lambda d, l, c, ax, b: d.groupby(l)[c].plot.hist(density=True, ax=ax, alpha=0.5, bins=b), 5, data, label, col, scale, with_outliers, xlim, bins)\\n\",\n \" \\n\",\n \" \\n\",\n \"def plot_stats(data, label, col, no_outliers=False):\\n\",\n \" print(data.groupby(label)[col].describe())\\n\",\n \" plot_box(data, label, col)\\n\",\n \" \\n\",\n \" \\n\",\n \"def calculate_distribution_similarity(data, label, col):\\n\",\n \" '''\\n\",\n \" Compare distributions by calculating the p-value for the null hypothesis that data for both categories originates from the same distribution\\n\",\n \" https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html\\n\",\n \" '''\\n\",\n \" a = data[data[label] == 0][col]\\n\",\n \" b = data[data[label] == 1][col]\\n\",\n \" return ks_2samp(a, b)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Minimum segment size observed in the forward direction\\n\",\n \"\\n\",\n \"`fwd_seg_size_min` might be a good predictor as the distributions of the feature for each of the classes differ significantly and the p-value for the hypothesis that values from both classes originate from the same distribution is 0.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" count mean std min 25% 50% 75% max\\n\",\n \"label_is_attack \\n\",\n \"0 13484708.0 16.501569 6.757963 0.0 8.0 20.0 20.0 56.0\\n\",\n \"1 2748235.0 25.309007 7.817982 0.0 20.0 20.0 32.0 40.0\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_stats(df, 'label_is_attack', 'fwd_seg_size_min')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Ks_2sampResult(statistic=0.3423301956292818, pvalue=0.0)\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"calculate_distribution_similarity(df, 'label_is_attack', 'fwd_seg_size_min')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_distribution(df, 'label_is_attack', 'fwd_seg_size_min', bins=50)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Number of backward packets per second\\n\",\n \"\\n\",\n \"`bwd_pkts_s` might also be a good predictor as the distributions of the feature for each of the classes differ significantly and the p-value for the hypothesis that values from both classes originate from the same distribution is 0.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" count mean std min 25% \\\\\\n\",\n \"label_is_attack \\n\",\n \"0 13484708.0 4459.606934 43464.53125 0.0 0.161656 \\n\",\n \"1 2748235.0 68584.609375 194335.18750 0.0 0.000000 \\n\",\n \"\\n\",\n \" 50% 75% max \\n\",\n \"label_is_attack \\n\",\n \"0 4.30558 882.612549 2000000.0 \\n\",\n \"1 0.00000 350.877197 2000000.0 \\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_stats(df, 'label_is_attack', 'bwd_pkts_s')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Ks_2sampResult(statistic=0.3205970312370562, pvalue=0.0)\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"calculate_distribution_similarity(df, 'label_is_attack', 'bwd_pkts_s')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 106,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_box(df, 'label_is_attack', 'bwd_pkts_s', xlim=[-20, 2000])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_distribution(df, 'label_is_attack', 'bwd_pkts_s', bins=5000, xlim=[-100, 10000])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Number of packets with ACK flag\\n\",\n \"\\n\",\n \"`ack_flag_cnt` might also be a good predictor as the distributions of the feature for each of the classes differ significantly and the p-value for the hypothesis that values from both classes originate from the same distribution is 0.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" count mean std min 25% 50% 75% max\\n\",\n \"label_is_attack \\n\",\n \"0 13484708.0 0.284745 0.451293 0.0 0.0 0.0 1.0 1.0\\n\",\n \"1 2748235.0 0.561513 0.496202 0.0 0.0 1.0 1.0 1.0\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_stats(df, 'label_is_attack', 'ack_flag_cnt')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Ks_2sampResult(statistic=0.2767682713043731, pvalue=0.0)\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"calculate_distribution_similarity(df, 'label_is_attack', 'ack_flag_cnt')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_distribution(df, 'label_is_attack', 'ack_flag_cnt', bins=30)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### Minimum size of packet in backward direction\\n\",\n \"\\n\",\n \"To conclude the exploration of the dataset a feature identified as a promising predictor in the [paper](https://www.scitepress.org/Papers/2018/66398/66398.pdf) accompanying the dataset is analyzed. The feature in question is the minimum size of packet in backward direction `bwd_pkt_len_min`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" count mean std min 25% 50% 75% max\\n\",\n \"label_is_attack \\n\",\n \"0 13484708.0 31.486992 54.162517 0.0 0.0 0.0 59.0 1460.0\\n\",\n \"1 2748235.0 1.900770 15.138823 0.0 0.0 0.0 0.0 549.0\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_stats(df, 'label_is_attack', 'bwd_pkt_len_min')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Ks_2sampResult(statistic=0.2895803001661078, pvalue=0.0)\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"calculate_distribution_similarity(df, 'label_is_attack', 'bwd_pkt_len_min')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 108,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_box(df, 'label_is_attack', 'bwd_pkt_len_min', xlim=[-5, 100])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 110,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_distribution(df, 'label_is_attack', 'bwd_pkt_len_min', bins=100, xlim=[-10, 300])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Summary\\n\",\n \"\\n\",\n \"The exploratory data analysis revealed the following properties of the dataset:\\n\",\n \"1. The ratio between benign and malicious network traffic is heavily skewed in favor of benign traffic.\\n\",\n \"2. Some types of network attacks are under-represented. It might be difficult to train a multi-class classifier to detect those kinds of network traffic.\\n\",\n \"3. The dataset contains a high amount of features with a strong correlation, suggesting that many of those features are redundant and can be removed. This should be considered when performing feature selection and extraction.\\n\",\n \"4. A few features were analyzed for their capacity of being good predictors for a binary classifier. Due to the high amount of features a visual identification of potential features is not feasible. To identify features with a high predictability a feature selection and extraction process like PCA is suggested. \"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/03_ml-prototype/ml-prototype.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Machine Learning Prototype\\n\",\n \"\\n\",\n \"The goal of this notebook is to create a binary classifier that is able to distinguish benign from malicious network traffic based on the [CIC-IDS-2018 dataset](https://www.unb.ca/cic/datasets/ids-2018.html). \\n\",\n \"The classifier will use a subset of the features generated by the [CICFlowMeter](https://github.com/ISCX/CICFlowMeter) for each observation. \\n\",\n \"\\n\",\n \"In order to find the most suitable classifier the following machine learning algorithms will be evaluated:\\n\",\n \"* Logistic Regression\\n\",\n \"* Random Forest\\n\",\n \"* Gradient Boosting\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The preprocessed dataset from CIC-IDS-2018 can be found at [Google Drive](https://drive.google.com/open?id=1HrTPh0YRSZ4T9DLa_c47lubheKUcPl0r).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dataset_base_path = r'/home/glados/Development/Projects/ids-2018/processed'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"import glob\\n\",\n \"import os\\n\",\n \"import shap\\n\",\n \"import scikitplot as skplt\\n\",\n \"import eli5\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"from sklearn.dummy import DummyClassifier\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"from sklearn.metrics import classification_report, precision_recall_curve, average_precision_score\\n\",\n \"from catboost import CatBoostClassifier\\n\",\n \"from catboost import Pool\\n\",\n \"from eli5.sklearn import PermutationImportance\\n\",\n \"from scipy.stats import spearmanr\\n\",\n \"from scipy.cluster import hierarchy\\n\",\n \"from collections import defaultdict\\n\",\n \"from scipy.stats import ks_2samp\\n\",\n \"from scipy.stats import describe\\n\",\n \"from imblearn.over_sampling import SMOTENC\\n\",\n \"from collections import Counter\\n\",\n \"from IPython.display import display\\n\",\n \"\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"types = {\\n\",\n \" 'dst_port': 'uint32',\\n\",\n \" 'protocol': 'uint8',\\n\",\n \" 'timestamp': 'object',\\n\",\n \" 'flow_duration': 'int64',\\n\",\n \" 'tot_fwd_pkts': 'uint32',\\n\",\n \" 'tot_bwd_pkts': 'uint32',\\n\",\n \" 'totlen_fwd_pkts': 'uint32',\\n\",\n \" 'totlen_bwd_pkts': 'uint32',\\n\",\n \" 'fwd_pkt_len_max': 'uint16',\\n\",\n \" 'fwd_pkt_len_min': 'uint16',\\n\",\n \" 'fwd_pkt_len_mean': 'float32',\\n\",\n \" 'fwd_pkt_len_std': 'float32',\\n\",\n \" 'bwd_pkt_len_max': 'uint16',\\n\",\n \" 'bwd_pkt_len_min': 'uint16',\\n\",\n \" 'bwd_pkt_len_mean': 'float32',\\n\",\n \" 'bwd_pkt_len_std': 'float32',\\n\",\n \" 'flow_byts_s': 'float64',\\n\",\n \" 'flow_pkts_s': 'float64',\\n\",\n \" 'flow_iat_mean': 'float32',\\n\",\n \" 'flow_iat_std': 'float32',\\n\",\n \" 'flow_iat_max': 'int64',\\n\",\n \" 'flow_iat_min': 'int64',\\n\",\n \" 'fwd_iat_tot': 'int64',\\n\",\n \" 'fwd_iat_mean': 'float32',\\n\",\n \" 'fwd_iat_std': 'float32',\\n\",\n \" 'fwd_iat_max': 'int64',\\n\",\n \" 'fwd_iat_min': 'int64',\\n\",\n \" 'bwd_iat_tot': 'uint32',\\n\",\n \" 'bwd_iat_mean': 'float32',\\n\",\n \" 'bwd_iat_std': 'float32',\\n\",\n \" 'bwd_iat_max': 'uint32',\\n\",\n \" 'bwd_iat_min': 'uint32',\\n\",\n \" 'fwd_psh_flags': 'uint8',\\n\",\n \" 'bwd_psh_flags': 'uint8',\\n\",\n \" 'fwd_urg_flags': 'uint8',\\n\",\n \" 'bwd_urg_flags': 'uint8',\\n\",\n \" 'fwd_header_len': 'uint32',\\n\",\n \" 'bwd_header_len': 'uint32',\\n\",\n \" 'fwd_pkts_s': 'float32',\\n\",\n \" 'bwd_pkts_s': 'float32',\\n\",\n \" 'pkt_len_min': 'uint16',\\n\",\n \" 'pkt_len_max': 'uint16',\\n\",\n \" 'pkt_len_mean': 'float32',\\n\",\n \" 'pkt_len_std': 'float32',\\n\",\n \" 'pkt_len_var': 'float32',\\n\",\n \" 'fin_flag_cnt': 'uint8',\\n\",\n \" 'syn_flag_cnt': 'uint8',\\n\",\n \" 'rst_flag_cnt': 'uint8',\\n\",\n \" 'psh_flag_cnt': 'uint8',\\n\",\n \" 'ack_flag_cnt': 'uint8',\\n\",\n \" 'urg_flag_cnt': 'uint8',\\n\",\n \" 'cwe_flag_count': 'uint8',\\n\",\n \" 'ece_flag_cnt': 'uint8',\\n\",\n \" 'down_up_ratio': 'uint16',\\n\",\n \" 'pkt_size_avg': 'float32',\\n\",\n \" 'fwd_seg_size_avg': 'float32',\\n\",\n \" 'bwd_seg_size_avg': 'float32',\\n\",\n \" 'fwd_byts_b_avg': 'uint8',\\n\",\n \" 'fwd_pkts_b_avg': 'uint8',\\n\",\n \" 'fwd_blk_rate_avg': 'uint8',\\n\",\n \" 'bwd_byts_b_avg': 'uint8',\\n\",\n \" 'bwd_pkts_b_avg': 'uint8',\\n\",\n \" 'bwd_blk_rate_avg': 'uint8',\\n\",\n \" 'subflow_fwd_pkts': 'uint32',\\n\",\n \" 'subflow_fwd_byts': 'uint32',\\n\",\n \" 'subflow_bwd_pkts': 'uint32',\\n\",\n \" 'subflow_bwd_byts': 'uint32',\\n\",\n \" 'init_fwd_win_byts': 'int32',\\n\",\n \" 'init_bwd_win_byts': 'int32',\\n\",\n \" 'fwd_act_data_pkts': 'uint32',\\n\",\n \" 'fwd_seg_size_min': 'uint8',\\n\",\n \" 'active_mean': 'float32',\\n\",\n \" 'active_std': 'float32',\\n\",\n \" 'active_max': 'uint32',\\n\",\n \" 'active_min': 'uint32',\\n\",\n \" 'idle_mean': 'float32',\\n\",\n \" 'idle_std': 'float32',\\n\",\n \" 'idle_max': 'uint64',\\n\",\n \" 'idle_min': 'uint64',\\n\",\n \" 'label': 'category'\\n\",\n \"}\\n\",\n \"\\n\",\n \"def replace_infinity_with_mean(df):\\n\",\n \" inf_columns = [c for c in df.columns if df[df[c] == np.inf][c].count() > 0]\\n\",\n \" for col in inf_columns:\\n\",\n \" df[col].replace([np.inf, -np.inf], np.nan, inplace=True)\\n\",\n \" mean = df[col].mean()\\n\",\n \" df[col].fillna(mean, inplace=True)\\n\",\n \" return df\\n\",\n \"\\n\",\n \"\\n\",\n \"def replace_negative_values_with_mean(df):\\n\",\n \" numeric_cols = df.select_dtypes(include=[np.number]).columns.values\\n\",\n \" \\n\",\n \" columns = [c for c in numeric_cols if df[df[c] < 0][c].count() > 0]\\n\",\n \" for col in columns:\\n\",\n \" mask = df[col] < 0\\n\",\n \" df.loc[mask, col] = np.nan\\n\",\n \" mean = df[col].mean()\\n\",\n \" df[col].fillna(mean, inplace=True)\\n\",\n \" return df\\n\",\n \"\\n\",\n \"\\n\",\n \"def load_dataset(files, dtypes, cols=None):\\n\",\n \" df = pd.concat((pd.read_csv(f, dtype=dtypes, usecols=cols) for f in files))\\n\",\n \" \\n\",\n \" df = replace_infinity_with_mean(df)\\n\",\n \" df = replace_negative_values_with_mean(df)\\n\",\n \" \\n\",\n \" df['label_cat'] = df.label.astype('category').cat.codes\\n\",\n \" df['label_is_attack'] = (df.label != 'Benign').astype('int')\\n\",\n \" return df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Dataset Preparation\\n\",\n \"\\n\",\n \"In the first step the dataset is loaded into a Pandas dataframe, additional columns `label_cat` (numeric category of the label) and `label_is_attack` (binary target identifier specifying benign and malicious network traffic) are added and the dataframe is split into the predictor variables `X` and the target variables `y`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"csv_files = glob.glob(os.path.join(dataset_base_path, '*.csv'))\\n\",\n \"\\n\",\n \"df = load_dataset(csv_files, types)\\n\",\n \"X = df.drop(columns=['label', 'label_cat', 'label_is_attack'])\\n\",\n \"y = df[['label_is_attack', 'label_cat', 'label']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Feature Engineering\\n\",\n \"\\n\",\n \"### 2.1. Low Amount of Variance\\n\",\n \"\\n\",\n \"As a first step in feature engineering all features with zero amount of variation are removed as those features will not have any influence on the prediction of the target variable. In order to find those features descriptive statistics are created via Pandas. All features having a standard deviation of `0` are selected and will be dropped subsequently.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"7 bwd_blk_rate_avg\\n\",\n \"5 bwd_byts_b_avg\\n\",\n \"6 bwd_pkts_b_avg\\n\",\n \"0 bwd_psh_flags\\n\",\n \"1 bwd_urg_flags\\n\",\n \"4 fwd_blk_rate_avg\\n\",\n \"2 fwd_byts_b_avg\\n\",\n \"3 fwd_pkts_b_avg\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"stats = X.describe()\\n\",\n \"std = stats.loc['std']\\n\",\n \"features_no_variance = std[std == 0.0].index\\n\",\n \"pd.Series(features_no_variance).sort_values()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X = X.drop(columns=features_no_variance)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2. Undesired Features\\n\",\n \"\\n\",\n \"The predictions of a classifier should be agnostic to the features `timestamp` and `dst_port` in order to recognize attacks indifferent of time and the destination port they are executed against. To this end both of the features are removed from the dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X = X.drop(columns=['timestamp', 'dst_port'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3. Multicolliniar Features\\n\",\n \"\\n\",\n \"Features having a high correlation amongst each other are removed in the next step. Those features won't bring any additional predictability but may introduce noise. \\n\",\n \"In order to identify those features hierarchical clustering on the Spearman rank-order correlations is performed.\\n\",\n \"After picking a threshold a single feature of each cluster is preserved in the dataset.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following figures show a heatmap visualzing the correlation between different pairs of features and a dendogram illustrating the clustering of the features.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"cluster_threshold = 1\\n\",\n \"\\n\",\n \"corr = spearmanr(X).correlation\\n\",\n \"corr_linkage = hierarchy.ward(corr)\\n\",\n \"\\n\",\n \"fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(25, 40))\\n\",\n \"\\n\",\n \"sns.heatmap(corr, xticklabels=X.columns, yticklabels=X.columns, linewidths=.5, cmap=sns.diverging_palette(620, 10, as_cmap=True), ax=ax1)\\n\",\n \"\\n\",\n \"dendro = hierarchy.dendrogram(corr_linkage, labels=X.columns, ax=ax2, leaf_rotation=90)\\n\",\n \"dendro_idx = np.arange(0, len(dendro['ivl']))\\n\",\n \"ax2.plot([0, 1000], [cluster_threshold, cluster_threshold], ':r')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The heatmap demonstrates that the dataset has a fair amount of features with high correlation amongst each other. In order to remove redundant features the cluster distance threshold of `1` is used to pick features from their respective clusters.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Selected features:\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"0 protocol\\n\",\n \"1 flow_duration\\n\",\n \"2 tot_fwd_pkts\\n\",\n \"3 tot_bwd_pkts\\n\",\n \"4 totlen_fwd_pkts\\n\",\n \"5 totlen_bwd_pkts\\n\",\n \"6 fwd_pkt_len_mean\\n\",\n \"7 fwd_pkt_len_std\\n\",\n \"8 bwd_pkt_len_mean\\n\",\n \"9 flow_byts_s\\n\",\n \"10 flow_pkts_s\\n\",\n \"11 flow_iat_std\\n\",\n \"12 flow_iat_min\\n\",\n \"13 fwd_iat_tot\\n\",\n \"14 fwd_iat_min\\n\",\n \"15 bwd_iat_tot\\n\",\n \"16 bwd_iat_min\\n\",\n \"17 fwd_psh_flags\\n\",\n \"18 fwd_urg_flags\\n\",\n \"19 bwd_pkts_s\\n\",\n \"20 fin_flag_cnt\\n\",\n \"21 rst_flag_cnt\\n\",\n \"22 psh_flag_cnt\\n\",\n \"23 ack_flag_cnt\\n\",\n \"24 urg_flag_cnt\\n\",\n \"25 down_up_ratio\\n\",\n \"26 init_fwd_win_byts\\n\",\n \"27 init_bwd_win_byts\\n\",\n \"28 fwd_seg_size_min\\n\",\n \"29 active_mean\\n\",\n \"30 idle_mean\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"cluster_ids = hierarchy.fcluster(corr_linkage, cluster_threshold, criterion='distance')\\n\",\n \"cluster_id_to_feature_ids = defaultdict(list)\\n\",\n \"\\n\",\n \"for idx, cluster_id in enumerate(cluster_ids):\\n\",\n \" cluster_id_to_feature_ids[cluster_id].append(idx)\\n\",\n \"selected_features = [v[0] for v in cluster_id_to_feature_ids.values()]\\n\",\n \"\\n\",\n \"selected_features = X.columns[selected_features].tolist()\\n\",\n \"\\n\",\n \"print('Selected features:')\\n\",\n \"pd.Series(selected_features)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Remaining amount of features: 31\\n\"\n ]\n }\n ],\n \"source\": [\n \"X = X[selected_features]\\n\",\n \"print(f\\\"Remaining amount of features: {len(X.columns)}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following figure shows the correlation heatmap after the removal of highly correlated features.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"corr_updated = spearmanr(X).correlation\\n\",\n \"plt.figure(figsize=(25, 20))\\n\",\n \"sns.heatmap(corr_updated, xticklabels=X.columns, yticklabels=X.columns, linewidths=.5, cmap=sns.diverging_palette(620, 10, as_cmap=True))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Model Training Preparation\\n\",\n \"\\n\",\n \"### 3.1. Train / Test split\\n\",\n \"\\n\",\n \"In order to train and evaluate different models a train/evaluation/test split is created with the ratios of `0.8/0.1/0.1`. \\n\",\n \"The split is stratified using the attack category in order to guarantee that all attacks are represented in the training and test set based on their occurences in the dataset. \\n\",\n \"To use algorithms expecting categorical values being one-hot-encoded, copies of the training and test data are created (`X_train_oh`, `X_test_oh`) with the feature `protocol` being one-hot-encoded.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def print_report(ds_type, cls, X_vals, y_true, y_predict, plot_pr=False, plot_roc=False):\\n\",\n \" print(f\\\"Classification Report ({ds_type}):\\\")\\n\",\n \" print(classification_report(y_true, y_predict))\\n\",\n \" print(f\\\"Avg Precision Score: {average_precision_score(y_true, y_predict, average='weighted')}\\\")\\n\",\n \" \\n\",\n \" if plot_roc:\\n\",\n \" print(f\\\"ROC AUC Score: {roc_auc_score(y_true, y_predict)}\\\")\\n\",\n \" skplt.metrics.plot_roc(y_true, cls.predict_proba(X_vals))\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \" if plot_pr:\\n\",\n \" \\n\",\n \" skplt.metrics.plot_precision_recall(y_true, cls.predict_proba(X_vals))\\n\",\n \" plt.show()\\n\",\n \" \\n\",\n \" print('\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_train, X_hold, y_train, y_hold = train_test_split(X, y, test_size=0.2, stratify=y.label_cat)\\n\",\n \"X_eval, X_test, y_eval, y_test = train_test_split(X_hold, y_hold, test_size=0.5, stratify=y_hold.label_cat)\\n\",\n \"\\n\",\n \"X_train_oh = pd.get_dummies(X_train, columns=['protocol'])\\n\",\n \"X_eval_oh = pd.get_dummies(X_eval, columns=['protocol'])\\n\",\n \"X_test_oh = pd.get_dummies(X_test, columns=['protocol'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Benign 13484708\\n\",\n \"DDOS attack-HOIC 686012\\n\",\n \"DDoS attacks-LOIC-HTTP 576191\\n\",\n \"DoS attacks-Hulk 461912\\n\",\n \"Bot 286191\\n\",\n \"FTP-BruteForce 193360\\n\",\n \"SSH-Bruteforce 187589\\n\",\n \"Infilteration 161934\\n\",\n \"DoS attacks-SlowHTTPTest 139890\\n\",\n \"DoS attacks-GoldenEye 41508\\n\",\n \"DoS attacks-Slowloris 10990\\n\",\n \"DDOS attack-LOIC-UDP 1730\\n\",\n \"Brute Force -Web 611\\n\",\n \"Brute Force -XSS 230\\n\",\n \"SQL Injection 87\\n\",\n \"Name: label, dtype: int64\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y.label.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Benign 10787766\\n\",\n \"DDOS attack-HOIC 548809\\n\",\n \"DDoS attacks-LOIC-HTTP 460953\\n\",\n \"DoS attacks-Hulk 369530\\n\",\n \"Bot 228953\\n\",\n \"FTP-BruteForce 154688\\n\",\n \"SSH-Bruteforce 150071\\n\",\n \"Infilteration 129547\\n\",\n \"DoS attacks-SlowHTTPTest 111912\\n\",\n \"DoS attacks-GoldenEye 33206\\n\",\n \"DoS attacks-Slowloris 8792\\n\",\n \"DDOS attack-LOIC-UDP 1384\\n\",\n \"Brute Force -Web 489\\n\",\n \"Brute Force -XSS 184\\n\",\n \"SQL Injection 70\\n\",\n \"Name: label, dtype: int64\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_train.label.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Benign 1348471\\n\",\n \"DDOS attack-HOIC 68601\\n\",\n \"DDoS attacks-LOIC-HTTP 57619\\n\",\n \"DoS attacks-Hulk 46191\\n\",\n \"Bot 28619\\n\",\n \"FTP-BruteForce 19336\\n\",\n \"SSH-Bruteforce 18759\\n\",\n \"Infilteration 16193\\n\",\n \"DoS attacks-SlowHTTPTest 13989\\n\",\n \"DoS attacks-GoldenEye 4151\\n\",\n \"DoS attacks-Slowloris 1099\\n\",\n \"DDOS attack-LOIC-UDP 173\\n\",\n \"Brute Force -Web 61\\n\",\n \"Brute Force -XSS 23\\n\",\n \"SQL Injection 9\\n\",\n \"Name: label, dtype: int64\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_eval.label.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Benign 1348471\\n\",\n \"DDOS attack-HOIC 68602\\n\",\n \"DDoS attacks-LOIC-HTTP 57619\\n\",\n \"DoS attacks-Hulk 46191\\n\",\n \"Bot 28619\\n\",\n \"FTP-BruteForce 19336\\n\",\n \"SSH-Bruteforce 18759\\n\",\n \"Infilteration 16194\\n\",\n \"DoS attacks-SlowHTTPTest 13989\\n\",\n \"DoS attacks-GoldenEye 4151\\n\",\n \"DoS attacks-Slowloris 1099\\n\",\n \"DDOS attack-LOIC-UDP 173\\n\",\n \"Brute Force -Web 61\\n\",\n \"Brute Force -XSS 23\\n\",\n \"SQL Injection 8\\n\",\n \"Name: label, dtype: int64\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_test.label.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.2 Metrics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Class occurences:\\n\",\n \"0 10787766\\n\",\n \"1 2198588\\n\",\n \"Name: label_is_attack, dtype: int64\\n\",\n \"Percentage of benign samples: 0.8307\\n\"\n ]\n }\n ],\n \"source\": [\n \"percentage_benign = len(y_train.label_is_attack[y_train.label_is_attack == 0]) / len(y_train.label_is_attack)\\n\",\n \"\\n\",\n \"print('Class occurences:')\\n\",\n \"print(y_train.label_is_attack.value_counts())\\n\",\n \"print('Percentage of benign samples: %.4f' % percentage_benign)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The distribution of classes shows that the dataset is highly imbalanced with class `0 - Benign` contributing to `~83%` of all the samples. \\n\",\n \"For this reason the metric `accuracy` is not suitable to measure the performance of a classifier based on this dataset as the `accuracy` of a dummy classifier reporting the class `0 - Benign` would already be `0.83` as shown in section `4.1`.\\n\",\n \"\\n\",\n \"To evaluate the performance of a classifier two metrics will be used:\\n\",\n \"* `Recall (weighted avg)` will be used as the primary metric as the goal of the classifier should be detect as many attacks as possible. This is the metric that classifiers will be optimized for.\\n\",\n \"* `Precision (weighted avg)` will be used as a secondary classifier as the the number of false-positives should be kept to a minimum. This metric should be have a value over the threshold of `0.95` to have a maximum of `5%` false-positives.\\n\",\n \"\\n\",\n \"The `Avg Precision Score` score will also be calculated for each classifier.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Classifiers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.1 Baseline Classifier\\n\",\n \"\\n\",\n \"To establish a baseline for all classifiers a `DummyClassifier` is created choosing the class with the most frequent occurences to make predictions. In this case all samples are classified as `0 - Benign` as this is the majority class of the dataset. \\n\",\n \"This classifier shows that `~83%` of all training examples are of class `0 - Benign`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Train):\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/glados/anaconda3/envs/ml-ids/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\\n\",\n \" 'precision', 'predicted', average, warn_for)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.83 1.00 0.91 10787766\\n\",\n \" 1 0.00 0.00 0.00 2198588\\n\",\n \"\\n\",\n \" accuracy 0.83 12986354\\n\",\n \" macro avg 0.42 0.50 0.45 12986354\\n\",\n \"weighted avg 0.69 0.83 0.75 12986354\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.16929986661383173\\n\",\n \"\\n\",\n \"\\n\",\n \"Classification Report (Eval):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.83 1.00 0.91 1348471\\n\",\n \" 1 0.00 0.00 0.00 274823\\n\",\n \"\\n\",\n \" accuracy 0.83 1623294\\n\",\n \" macro avg 0.42 0.50 0.45 1623294\\n\",\n \"weighted avg 0.69 0.83 0.75 1623294\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.16929958467166145\\n\",\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"cls_dummy = DummyClassifier('most_frequent')\\n\",\n \"cls_dummy.fit(X_train, y_train.label_is_attack)\\n\",\n \"\\n\",\n \"print_report('Train', cls_dummy, X_train, y_train.label_is_attack, cls_dummy.predict(X_train))\\n\",\n \"print_report('Eval', cls_dummy, X_eval, y_eval.label_is_attack, cls_dummy.predict(X_eval))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.2 Logistic Regression\\n\",\n \"\\n\",\n \"Using the Logistic Regression estimator a linear model will be fitted to the data using the [`LogisticRegression`](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) implementation from [scikit-learn](https://scikit-learn.org/). In order to use Logistic Regression the predictor variables are scaled via a [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html). \\n\",\n \"Using Logistic Regression a weighted recall of `0.88` and a precision of `0.87` which is better than the baseline but not sufficient for real-world usage.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"StandardScaler(copy=True, with_mean=True, with_std=True)\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"scaler = StandardScaler()\\n\",\n \"scaler.fit(X_train_oh)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 24 concurrent workers.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"max_iter reached after 1383 seconds\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/glados/anaconda3/envs/ml-ids/lib/python3.7/site-packages/sklearn/linear_model/sag.py:337: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\\n\",\n \" \\\"the coef_ did not converge\\\", ConvergenceWarning)\\n\",\n \"[Parallel(n_jobs=-1)]: Done 1 out of 1 | elapsed: 23.0min finished\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Train):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.90 0.95 0.93 10787766\\n\",\n \" 1 0.68 0.49 0.57 2198588\\n\",\n \"\\n\",\n \" accuracy 0.88 12986354\\n\",\n \" macro avg 0.79 0.72 0.75 12986354\\n\",\n \"weighted avg 0.87 0.88 0.87 12986354\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.42270786673763255\\n\",\n \"\\n\",\n \"\\n\",\n \"Classification Report (Eval):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.90 0.95 0.93 1348471\\n\",\n \" 1 0.68 0.49 0.57 274823\\n\",\n \"\\n\",\n \" accuracy 0.88 1623294\\n\",\n \" macro avg 0.79 0.72 0.75 1623294\\n\",\n \"weighted avg 0.86 0.88 0.87 1623294\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.4220228570050777\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"cls_lr = LogisticRegression(solver='saga', n_jobs=-1, verbose=2)\\n\",\n \"cls_lr.fit(scaler.transform(X_train_oh), y_train.label_is_attack)\\n\",\n \"\\n\",\n \"print_report('Train', cls_lr, scaler.transform(X_train_oh), y_train.label_is_attack, cls_lr.predict(scaler.transform(X_train_oh)))\\n\",\n \"print_report('Eval', cls_lr, scaler.transform(X_eval_oh), y_eval.label_is_attack, cls_lr.predict(scaler.transform(X_eval_oh)), plot_pr=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.3 Random Forest\\n\",\n \"\\n\",\n \"The next algorithm evaluated is the [`RandomForestClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) implementation from [scikit-learn](https://scikit-learn.org/). The default values are used to train the estimator. \\n\",\n \"`RandomForestClassifier` performs very well with recall of `0.99` and precision of `0.99`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/glados/anaconda3/envs/ml-ids/lib/python3.7/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\\n\",\n \" \\\"10 in version 0.20 to 100 in 0.22.\\\", FutureWarning)\\n\",\n \"[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 24 concurrent workers.\\n\",\n \"[Parallel(n_jobs=-1)]: Done 7 out of 10 | elapsed: 1.6min remaining: 42.3s\\n\",\n \"[Parallel(n_jobs=-1)]: Done 10 out of 10 | elapsed: 1.7min finished\\n\",\n \"[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\\n\",\n \"[Parallel(n_jobs=10)]: Done 2 out of 10 | elapsed: 5.9s remaining: 23.4s\\n\",\n \"[Parallel(n_jobs=10)]: Done 10 out of 10 | elapsed: 7.1s finished\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Train):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 1.00 0.99 0.99 10787766\\n\",\n \" 1 0.97 0.98 0.97 2198588\\n\",\n \"\\n\",\n \" accuracy 0.99 12986354\\n\",\n \" macro avg 0.98 0.98 0.98 12986354\\n\",\n \"weighted avg 0.99 0.99 0.99 12986354\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.9502138122792624\\n\",\n \"\\n\",\n \"\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\\n\",\n \"[Parallel(n_jobs=10)]: Done 2 out of 10 | elapsed: 0.7s remaining: 2.8s\\n\",\n \"[Parallel(n_jobs=10)]: Done 10 out of 10 | elapsed: 1.0s finished\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Eval):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.99 0.99 0.99 1348471\\n\",\n \" 1 0.96 0.95 0.96 274823\\n\",\n \"\\n\",\n \" accuracy 0.99 1623294\\n\",\n \" macro avg 0.98 0.97 0.97 1623294\\n\",\n \"weighted avg 0.99 0.99 0.99 1623294\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.9253892970122982\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[Parallel(n_jobs=10)]: Using backend ThreadingBackend with 10 concurrent workers.\\n\",\n \"[Parallel(n_jobs=10)]: Done 2 out of 10 | elapsed: 0.7s remaining: 2.8s\\n\",\n \"[Parallel(n_jobs=10)]: Done 10 out of 10 | elapsed: 1.1s finished\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"cls_forest = RandomForestClassifier(verbose=1, n_jobs=-1, class_weight='balanced')\\n\",\n \"cls_forest.fit(X_train_oh, y_train.label_is_attack)\\n\",\n \"\\n\",\n \"print_report('Train', cls_forest, X_train_oh, y_train.label_is_attack, cls_forest.predict(X_train_oh))\\n\",\n \"print_report('Eval', cls_forest, X_eval_oh, y_eval.label_is_attack, cls_forest.predict(X_eval_oh), plot_pr=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.4 Gradient Boosting\\n\",\n \"\\n\",\n \"The last algorithm to evaluate is Gradient Boosting by facilitating the library [CatBoost](https://catboost.ai/). A grid search using cross-validation over a variation of hyper-parameters is performed in order to identify the optimal parameters. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_pool = Pool(X_train, y_train.label_is_attack, cat_features=['protocol'])\\n\",\n \"eval_pool = Pool(X_eval, y_eval.label_is_attack, cat_features=['protocol'])\\n\",\n \"test_pool = Pool(X_test, cat_features=['protocol'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"minority_class_weight = len(y_train[y_train.label_is_attack == 0]) / len(y_train[y_train.label_is_attack == 1])\\n\",\n \" \\n\",\n \"cls_cb = CatBoostClassifier(loss_function='Logloss',\\n\",\n \" eval_metric='Recall', \\n\",\n \" class_weights=[1, minority_class_weight],\\n\",\n \" task_type='GPU',\\n\",\n \" verbose=True)\\n\",\n \"\\n\",\n \"cls_cb.fit(train_pool, eval_set=eval_pool)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Train):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.99 0.99 0.99 10787766\\n\",\n \" 1 0.97 0.96 0.96 2198588\\n\",\n \"\\n\",\n \" accuracy 0.99 12986354\\n\",\n \" macro avg 0.98 0.98 0.98 12986354\\n\",\n \"weighted avg 0.99 0.99 0.99 12986354\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.9341507909896061\\n\",\n \"\\n\",\n \"\\n\",\n \"Classification Report (Eval):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.99 0.99 0.99 1348471\\n\",\n \" 1 0.97 0.96 0.96 274823\\n\",\n \"\\n\",\n \" accuracy 0.99 1623294\\n\",\n \" macro avg 0.98 0.97 0.98 1623294\\n\",\n \"weighted avg 0.99 0.99 0.99 1623294\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.9333906081606897\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print_report('Train', cls_cb, train_pool, y_train.label_is_attack, cls_cb.predict(train_pool))\\n\",\n \"print_report('Eval', cls_cb, eval_pool, y_eval.label_is_attack, cls_cb.predict(eval_pool), plot_pr=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.5 Model selection\\n\",\n \"\\n\",\n \"Comparing the models the following results are obtained:\\n\",\n \"\\n\",\n \"|Model|Recall|Precision|F1|Avg PR|Recall Attack|Precision Attack|\\n\",\n \"|-----|---------|------|--|------|-------------|----------------|\\n\",\n \"|Baseline|0.83|0.69|0.75|0.169|0.00|0.00|\\n\",\n \"|Logistic Regression|0.88|0.86|0.87|0.422|0.49|0.68|\\n\",\n \"|Random Forest|**0.99**|0.99|0.99|0.925|**0.95**|**0.96**|\\n\",\n \"|Gradient Boost|**0.99**|0.99|0.99|0.933|**0.96**|**0.97**|\\n\",\n \"\\n\",\n \"Gradient Boost performed best of all models and will be used as the final estimator.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"estimator = cls_cb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.6 Performance on Test dataset\\n\",\n \"\\n\",\n \"To predict real-world performance the final estimator is evaluated on the test dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Test):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.99 0.99 0.99 1348471\\n\",\n \" 1 0.97 0.96 0.96 274824\\n\",\n \"\\n\",\n \" accuracy 0.99 1623295\\n\",\n \" macro avg 0.98 0.97 0.98 1623295\\n\",\n \"weighted avg 0.99 0.99 0.99 1623295\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.933930661288721\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print_report('Test', estimator, test_pool, y_test.label_is_attack, estimator.predict(test_pool), plot_pr=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The final estimator shows very good performance with recall of `0.99` and precision of `0.99`. \\n\",\n \"The following listing shows the misclassifications in the test dataset which demonstrate that attacks of type `Infiltration` are often misclassified. \\n\",\n \"Futhermore minority classes of attacks are often misclassified. Synthetic minority oversampling may be applied to the train dataset for these classes to gain better performance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def calculate_misclassifiations(y, predictions):\\n\",\n \" classifications = y.copy()\\n\",\n \" classifications['pred'] = predictions\\n\",\n \" counts = classifications.label.value_counts()\\n\",\n \" \\n\",\n \" misclassifications = classifications[classifications.label_is_attack != classifications.pred]\\n\",\n \" mc = pd.DataFrame(misclassifications.label.value_counts())\\n\",\n \" mc['percentage'] = mc.apply(lambda x: x[0] / counts[x.name], axis=1)\\n\",\n \" mc = mc.sort_values('percentage', ascending=False)\\n\",\n \" \\n\",\n \" print('Misclassifications:')\\n\",\n \" display(mc)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Misclassifications:\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
labelpercentage
Infilteration119370.737125
Brute Force -Web320.524590
SQL Injection30.375000
Brute Force -XSS30.130435
DoS attacks-Slowloris130.011829
Benign84250.006248
Bot350.001223
DDoS attacks-LOIC-HTTP80.000139
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" label percentage\\n\",\n \"Infilteration 11937 0.737125\\n\",\n \"Brute Force -Web 32 0.524590\\n\",\n \"SQL Injection 3 0.375000\\n\",\n \"Brute Force -XSS 3 0.130435\\n\",\n \"DoS attacks-Slowloris 13 0.011829\\n\",\n \"Benign 8425 0.006248\\n\",\n \"Bot 35 0.001223\\n\",\n \"DDoS attacks-LOIC-HTTP 8 0.000139\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"calculate_misclassifiations(y_test, estimator.predict(test_pool))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Novel Data from different network environment\\n\",\n \"\\n\",\n \"In order to verify that the estimator has an equally good performance as shown in the test dataset additional tests a performed on the [CIC-IDS-2017](https://www.unb.ca/cic/datasets/ids-2017.html) dataset which contains the same attack scenarios but recorded in a different network environment.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.1. Test on novel data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The preprocessed dataset from CIC-IDS-2017 can be found at [Google Drive](https://drive.google.com/open?id=1Q2J_pPB0K0PHjq0YO5BPwYQwrvoZgYqo).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"novel_base_path= r'/home/glados/Development/Projects/ids-2017/processed'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"novel_files = glob.glob(os.path.join(novel_base_path, '*.csv'))\\n\",\n \"\\n\",\n \"df_novel = load_dataset(novel_files, types, cols=selected_features + ['label'])\\n\",\n \"\\n\",\n \"X_novel = df_novel.drop(columns=['label', 'label_cat', 'label_is_attack'])\\n\",\n \"y_novel = df_novel[['label_is_attack', 'label_cat', 'label']]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Number of test samples: 2830743\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f\\\"Number of test samples: {len(X_novel)}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Novel):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.84 0.96 0.90 2273097\\n\",\n \" 1 0.63 0.26 0.36 557646\\n\",\n \"\\n\",\n \" accuracy 0.82 2830743\\n\",\n \" macro avg 0.73 0.61 0.63 2830743\\n\",\n \"weighted avg 0.80 0.82 0.79 2830743\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.307365267074596\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"novel_pool = Pool(X_novel, cat_features=['protocol'])\\n\",\n \"\\n\",\n \"print_report('Novel', estimator, novel_pool, y_novel.label_is_attack, estimator.predict(novel_pool), plot_pr=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Unfortunately the estimator has very poor performance on data recorded in a different network environment showing a recall of `0.82` and a precision of `0.80`. Moreover the estimator has only attack recall of `0.26` which is insufficient for real-world usage. \\n\",\n \"This result suggests that data from one network environment does not generalize well to a different network environment.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Misclassifications:\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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labelpercentage
Bot19661.000000
Infilteration361.000000
Heartbleed111.000000
DDOS LOIT1279270.999219
SSH-Bruteforce58780.996778
Brute Force -XSS6380.978528
Brute Force -Web14350.952223
SQL Injection180.857143
DoS attacks-SlowHTTPTest46700.849245
DoS attacks-Hulk1806590.781827
DoS attacks-GoldenEye74630.725056
PortScan788060.495854
FTP-BruteForce29370.369992
DoS attacks-Slowloris20830.359386
Benign851280.037450
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" label percentage\\n\",\n \"Bot 1966 1.000000\\n\",\n \"Infilteration 36 1.000000\\n\",\n \"Heartbleed 11 1.000000\\n\",\n \"DDOS LOIT 127927 0.999219\\n\",\n \"SSH-Bruteforce 5878 0.996778\\n\",\n \"Brute Force -XSS 638 0.978528\\n\",\n \"Brute Force -Web 1435 0.952223\\n\",\n \"SQL Injection 18 0.857143\\n\",\n \"DoS attacks-SlowHTTPTest 4670 0.849245\\n\",\n \"DoS attacks-Hulk 180659 0.781827\\n\",\n \"DoS attacks-GoldenEye 7463 0.725056\\n\",\n \"PortScan 78806 0.495854\\n\",\n \"FTP-BruteForce 2937 0.369992\\n\",\n \"DoS attacks-Slowloris 2083 0.359386\\n\",\n \"Benign 85128 0.037450\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"calculate_misclassifiations(y_novel, estimator.predict(novel_pool))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.2. Feature importance\\n\",\n \"\\n\",\n \"In order to analyse the problem the feature importances of the model are calculated. The most important features will be used to compare data from the two datasets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
WeightFeature
\\n\",\n \" 0.0575\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" init_fwd_win_byts\\n\",\n \"
\\n\",\n \" 0.0274\\n\",\n \" \\n\",\n \" ± 0.0002\\n\",\n \" \\n\",\n \" \\n\",\n \" fwd_seg_size_min\\n\",\n \"
\\n\",\n \" 0.0131\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" tot_fwd_pkts\\n\",\n \"
\\n\",\n \" 0.0114\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" fwd_iat_tot\\n\",\n \"
\\n\",\n \" 0.0099\\n\",\n \" \\n\",\n \" ± 0.0000\\n\",\n \" \\n\",\n \" \\n\",\n \" totlen_fwd_pkts\\n\",\n \"
\\n\",\n \" 0.0046\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" tot_bwd_pkts\\n\",\n \"
\\n\",\n \" 0.0046\\n\",\n \" \\n\",\n \" ± 0.0002\\n\",\n \" \\n\",\n \" \\n\",\n \" init_bwd_win_byts\\n\",\n \"
\\n\",\n \" 0.0032\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" flow_duration\\n\",\n \"
\\n\",\n \" 0.0027\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" bwd_pkt_len_mean\\n\",\n \"
\\n\",\n \" 0.0026\\n\",\n \" \\n\",\n \" ± 0.0000\\n\",\n \" \\n\",\n \" \\n\",\n \" flow_iat_min\\n\",\n \"
\\n\",\n \" 0.0019\\n\",\n \" \\n\",\n \" ± 0.0000\\n\",\n \" \\n\",\n \" \\n\",\n \" rst_flag_cnt\\n\",\n \"
\\n\",\n \" 0.0018\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" flow_pkts_s\\n\",\n \"
\\n\",\n \" 0.0013\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" protocol\\n\",\n \"
\\n\",\n \" 0.0011\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" fwd_iat_min\\n\",\n \"
\\n\",\n \" 0.0007\\n\",\n \" \\n\",\n \" ± 0.0000\\n\",\n \" \\n\",\n \" \\n\",\n \" bwd_pkts_s\\n\",\n \"
\\n\",\n \" 0.0005\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" fwd_pkt_len_std\\n\",\n \"
\\n\",\n \" 0.0004\\n\",\n \" \\n\",\n \" ± 0.0001\\n\",\n \" \\n\",\n \" \\n\",\n \" bwd_iat_tot\\n\",\n \"
\\n\",\n \" 0.0003\\n\",\n \" \\n\",\n \" ± 0.0000\\n\",\n \" \\n\",\n \" \\n\",\n \" urg_flag_cnt\\n\",\n \"
\\n\",\n \" 0.0003\\n\",\n \" \\n\",\n \" ± 0.0000\\n\",\n \" \\n\",\n \" \\n\",\n \" idle_mean\\n\",\n \"
\\n\",\n \" 0.0002\\n\",\n \" \\n\",\n \" ± 0.0000\\n\",\n \" \\n\",\n \" \\n\",\n \" flow_byts_s\\n\",\n \"
\\n\",\n \" … 11 more …\\n\",\n \"
\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"perm = PermutationImportance(estimator, scoring='recall_weighted', n_iter=3).fit(X_test, y_test.label_is_attack)\\n\",\n \"eli5.show_weights(perm, feature_names=X_test.columns.tolist())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"features_to_analyze = ['init_fwd_win_byts', 'fwd_seg_size_min', 'tot_fwd_pkts', 'fwd_iat_tot']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.3. Statistical Analysis\\n\",\n \"\\n\",\n \"The following listings compare the data from both datasets for the identified features. \\n\",\n \"The complete, benign and attack data of the two datasets are compared by plotting their distributions, calculating summary statistics and performing a [Kolmogorov-Smirnov statistic](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def stats(vals):\\n\",\n \" d = describe(vals)\\n\",\n \" return pd.DataFrame.from_dict({\\n\",\n \" 'nobs': d.nobs, \\n\",\n \" 'min': d.minmax[0], \\n\",\n \" 'max': d.minmax[1], \\n\",\n \" 'mean': d.mean, \\n\",\n \" 'median': np.median(vals),\\n\",\n \" 'std': np.std(vals),\\n\",\n \" 'variance': d.variance,\\n\",\n \" 'skewness': d.skewness, \\n\",\n \" 'kurtosis': d.kurtosis\\n\",\n \" }, orient='index')\\n\",\n \"\\n\",\n \"\\n\",\n \"def print_distributions(X_1, y_1, X_2, y_2, f, label_1='1', label_2='2'):\\n\",\n \" print(f\\\"Distribution for field '{f}'\\\")\\n\",\n \" print(\\\"===================================\\\")\\n\",\n \" fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 20))\\n\",\n \" \\n\",\n \" X_f_1 = X_1[f]\\n\",\n \" X_f_2 = X_2[f]\\n\",\n \" X_attack_1 = X_f_1[y_1.label_is_attack == 1]\\n\",\n \" X_benign_1 = X_f_1[y_1.label_is_attack == 0]\\n\",\n \" X_attack_2 = X_f_2[y_2.label_is_attack == 1]\\n\",\n \" X_benign_2 = X_f_2[y_2.label_is_attack == 0]\\n\",\n \"\\n\",\n \" sns.distplot(X_f_1, label=f'All {label_1}', hist=False, ax=ax1)\\n\",\n \" sns.distplot(X_f_2, label=f'All {label_2}', hist=False, ax=ax1)\\n\",\n \"\\n\",\n \" sns.distplot(X_benign_1, label=f'Benign {label_1}', hist=False, ax=ax2)\\n\",\n \" sns.distplot(X_benign_2, label=f'Benign {label_2}', hist=False, ax=ax2)\\n\",\n \"\\n\",\n \" sns.distplot(X_attack_1, label=f'Attack {label_1}', hist=False, ax=ax3)\\n\",\n \" sns.distplot(X_attack_2, label=f'Attack {label_2}', hist=False, ax=ax3) \\n\",\n \" plt.show()\\n\",\n \"\\n\",\n \" print('K2 All:')\\n\",\n \" print(ks_2samp(X_f_1, X_f_2))\\n\",\n \" print('K2 Benign:')\\n\",\n \" print(ks_2samp(X_benign_1, X_benign_2))\\n\",\n \" print('K2 Attack:')\\n\",\n \" print(ks_2samp(X_attack_1, X_attack_2))\\n\",\n \"\\n\",\n \" st = pd.concat({\\n\",\n \" f'All {label_1}': stats(X_f_1),\\n\",\n \" f'All {label_2}': stats(X_f_2),\\n\",\n \" f'Benign {label_1}': stats(X_benign_1),\\n\",\n \" f'Benign {label_2}': stats(X_benign_2),\\n\",\n \" f'Attack {label_1}': stats(X_attack_1),\\n\",\n \" f'Attack {label_2}': stats(X_attack_2)\\n\",\n \" }, axis=1)\\n\",\n \" \\n\",\n \" print()\\n\",\n \" print(st)\\n\",\n \" print('\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Distribution for field 'init_fwd_win_byts'\\n\",\n \"===================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"K2 All:\\n\",\n \"Ks_2sampResult(statistic=0.44235392107565924, pvalue=0.0)\\n\",\n \"K2 Benign:\\n\",\n \"Ks_2sampResult(statistic=0.47378059106970455, pvalue=0.0)\\n\",\n \"K2 Attack:\\n\",\n \"Ks_2sampResult(statistic=0.4434562553612962, pvalue=0.0)\\n\",\n \"\\n\",\n \" All 2018 All 2017 Benign 2018 Benign 2017 \\\\\\n\",\n \" 0 0 0 0 \\n\",\n \"nobs 1.623294e+07 2.830743e+06 1.348471e+07 2.273097e+06 \\n\",\n \"min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \\n\",\n \"max 6.553500e+04 6.553500e+04 6.553500e+04 6.553500e+04 \\n\",\n \"mean 1.118693e+04 9.462386e+03 1.003846e+04 9.970655e+03 \\n\",\n \"median 8.192000e+03 6.989837e+03 8.192000e+03 6.989837e+03 \\n\",\n \"std 1.538657e+04 1.349816e+04 1.455744e+04 1.386692e+04 \\n\",\n \"variance 2.367467e+08 1.822004e+08 2.119191e+08 1.922915e+08 \\n\",\n \"skewness 2.622149e+00 2.564942e+00 3.179946e+00 2.730492e+00 \\n\",\n \"kurtosis 6.227754e+00 7.048015e+00 9.178910e+00 7.574957e+00 \\n\",\n \"\\n\",\n \" Attack 2018 Attack 2017 \\n\",\n \" 0 0 \\n\",\n \"nobs 2.748235e+06 5.576460e+05 \\n\",\n \"min 0.000000e+00 0.000000e+00 \\n\",\n \"max 6.553500e+04 6.424000e+04 \\n\",\n \"mean 1.682212e+04 7.390563e+03 \\n\",\n \"median 8.192000e+03 2.740000e+02 \\n\",\n \"std 1.789805e+04 1.164993e+04 \\n\",\n \"variance 3.203403e+08 1.357212e+08 \\n\",\n \"skewness 1.095240e+00 1.264893e+00 \\n\",\n \"kurtosis 7.084984e-01 -2.385989e-01 \\n\",\n \"\\n\",\n \"\\n\",\n \"Distribution for field 'fwd_seg_size_min'\\n\",\n \"===================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"K2 All:\\n\",\n \"Ks_2sampResult(statistic=0.4055406906104775, pvalue=0.0)\\n\",\n \"K2 Benign:\\n\",\n \"Ks_2sampResult(statistic=0.4385982621560479, pvalue=0.0)\\n\",\n \"K2 Attack:\\n\",\n \"Ks_2sampResult(statistic=0.22266068679150747, pvalue=0.0)\\n\",\n \"\\n\",\n \" All 2018 All 2017 Benign 2018 Benign 2017 \\\\\\n\",\n \" 0 0 0 0 \\n\",\n \"nobs 1.623294e+07 2.830743e+06 1.348471e+07 2.273097e+06 \\n\",\n \"min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \\n\",\n \"max 5.600000e+01 2.550000e+02 5.600000e+01 2.550000e+02 \\n\",\n \"mean 1.799267e+01 2.611845e+01 1.650157e+01 2.579871e+01 \\n\",\n \"median 2.000000e+01 2.400000e+01 2.000000e+01 2.000000e+01 \\n\",\n \"std 7.693845e+00 6.625706e+00 6.757963e+00 6.402757e+00 \\n\",\n \"variance 5.919525e+01 4.389999e+01 4.567006e+01 4.099532e+01 \\n\",\n \"skewness 2.556202e-01 8.693634e-01 -1.903372e-01 9.961939e-01 \\n\",\n \"kurtosis 5.638952e-01 1.627596e+01 -6.595508e-02 2.375664e+01 \\n\",\n \"\\n\",\n \" Attack 2018 Attack 2017 \\n\",\n \" 0 0 \\n\",\n \"nobs 2.748235e+06 557646.000000 \\n\",\n \"min 0.000000e+00 0.000000 \\n\",\n \"max 4.000000e+01 44.000000 \\n\",\n \"mean 2.530901e+01 27.421769 \\n\",\n \"median 2.000000e+01 24.000000 \\n\",\n \"std 7.817980e+00 7.322893 \\n\",\n \"variance 6.112084e+01 53.624852 \\n\",\n \"skewness 5.470390e-01 0.423485 \\n\",\n \"kurtosis -6.289220e-01 -1.218904 \\n\",\n \"\\n\",\n \"\\n\",\n \"Distribution for field 'tot_fwd_pkts'\\n\",\n \"===================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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+oJMkeqa4x97lwtGCLS8rrQU9RQQcAAAiiig7oxpgGSR+Q9AVJstb2S8rfG+JXTsptaRksHCvc4pJpbUmxBx0AACCQKr3F5TxJ7ZJ+ZIx50xjzz8aYunIfqiROWgqFc58Lx0avoDMkCgAAEEiVHtAjklZJ+r61dqWkbknfHPomY8ztxpgNxpgN7e3tZ/uMI8tXQY/ERtiDzppFAACAIKv0gN4iqcVa+1rm+w/LDew5rLV3W2vXWGvXNDU1ndUDjsqmS2pxSWa3uFBBBwAACKKKDujW2iOSDhhjFmae+rCkrWU8UumctGSGtrgUHhLloiIAAIBgq+gh0Yw/lHRfZoPLHklfLPN5SlOwB73QkCg96AAAAEFW8QHdWrtZ0ppyn+OUOak8Ab3wHnSvck4FHQAAIJgqusWlIuTtQR99DzprFgEAAIKJgO53eXvQR9+DTosLAABAMBHQ/S5vi0sxe9CpoAMAAAQRAd3vnDwtLpHRh0RZswgAABBMBHS/K1hBL3BRUab3vJ8KOgAAQCAR0P3M2pKGRB3HKtPhku1FBwAAQLAQ0P3MZkJ2kUOiyUGbWxgSBQAACCYCup85afcx3x70PAHdu0VUYkgUAAAgqAjofuak3MdhAT0qpfJU0AdVzVMOFXQAAIAgIqD7WTagD+1Bz9/iMrjvnAo6AABAMBHQ/cx6LS55ArpND7TAZOS2uFBBBwAACCICup95AXzokGgk5j4O2eSSpAcdAAAg8AjoflZwSNQL6Lm70Ae3uLBmEQAAIJgI6H5WcEg0fwXdGwyNhUO0uAAAAAQUAd3PCvagR93HIYOiqUwor46GaHEBAAAIKAK6n3kV9GEXFVW5j0MCuhfKa2Jh1iwCAAAEFAHdz5xRKuhDdqF7obwmGlYyRQUdAAAgiAjofjbqkGhuQE87biivjoaVdAjoAAAAQURA97NRh0SHtri4FfTaWDjbjw4AAIBgIaD72ahDokO2uGSHRN0edGsJ6QAAAEFDQPezQkOiEW9IdMge9ExbS03UfT+rFgEAAIKHgO5nXh/5sAp6/hYXr4JeE/MCOn3oAAAAQUNA97NsD/qQX6ZCLS5DKuj0oQMAAAQPAd3PsgG9yAq6M6SCziYXAACAwCGg+1nBIdFMD/rQPejpgT3oEi0uAAAAQURA97OCN4l6LS75bxKtpsUFAAAgsAjoflbikGh6SItLPxV0AACAwCGg+1nBIVEvoOcOiSad3BYXKugAAADBQ0D3s0I96BEvoA/Zg56pmLNmEQAAILgI6H5WsAd9lBYXhkQBAAACi4DuZ06BCnoo/x705JAtLt7aRQAAAAQHAd3PsgF9SAU9FHJD+7CbRIe0uKSooAMAAARNZPS3BJsxZq+kTklpSSlr7ZrynqgE2SHR8PDXwlXD1yxmKubV0VDO9wEAABAcFR/QMz5krT1a7kOUrNCQqOTuQk8N7UF3FA4ZRcOZgE4FHQAAIHBocfGzQkOikjsoOqzFxSoyKKCnHAI6AABA0JwLAd1K+qUxZqMx5vZ8bzDG3G6M2WCM2dDe3n6WjzeCQkOiUiagDx8SjYZDioZN9vsAAAAIlnMhoK+11q6SdL2krxpjPjD0Ddbau621a6y1a5qams7+CQspNCQqubvQh61ZdFtcIpmLjVizCAAAEDwVH9CttYcyj22SHpN0SXlPVAI7QkAPx4ZdVJR0rKJho2gk0+JCBR0AACBwKjqgG2PqjDH13rclfUTSO+U9VQlG7EGPDmtxSaUdRUIhRUNui0s/FXQAAIDAqfQtLlMlPWaMkdyf6/3W2qfKe6QSZNcsFupBHzIk6ticLS4pAjoAAEDgVHRAt9bukXRRuc9xyrwtLHkD+vA96Km02+ISyQyJcpMoAABA8FR0i0vgjXhR0fA96CnHUSQcylbQaXEBAAAIHgK6n9m0ZEKS26KTq5g96AyJAgAABA4B3c+cVP4BUSnvHvSUYxUJG4VDRsawZhEAACCICOh+5qTz959LefegJzNbXCQpGg5xUREAAEAAEdD9zEnn7z+X8u5BT2f2oEtSNGTY4gIAABBABHQ/c1IjBPR8e9DdNYuSFAmHaHEBAAAIIAK6n9kRWlzyDIkmHSc7IBoNh5RkzSIAAEDgEND9bMQh0fx70COZCno0bJRMUUEHAAAIGgK6n400JJp3D7pVeNCQKBcVAQAABA8B3c9GHRIdWkF3skOikbChBx0AACCACOh+ZkcJ6DbthvgMdw96poIeYkgUAAAgiAjofjZSD3ok5j4O2uSScpyBHvSI4SZRAACAACKg+5mTGnmLi5SzC33wkGgkFFI/FXQAAIDAIaD72YhDosMr6Mn0QItLLByigg4AABBABHQ/c9JSqMAvUTjqPg4aFE0PanFhSBQAACCYCOh+NuJFRVXu46CAnkpbRcLeHnQuKgIAAAgiArqfjXhRUaaCPmgXeu5NokYpKugAAACBQ0D3s6J60Ae3uOQOidLiAgAAEDwEdD8b7aIiKRvQrbXukGh2zSJDogAAAEFEQPczJ1U4oA/Zg57O9JsPXFRkWLMIAAAQQAR0PxtxSDS3gp7KBvSBIVEq6AAAAMFDQPezEYdEcy8qygb0QWsWUw4VdAAAgKAhoPvZiEOi3h50t8XF29gSCXlbXELqTxHQAQAAgoaA7mcjDonm7kFPZtpZotkWF5OtqgMAACA4COh+ZovZ4pI7JBrOVNAjYdYsAgAABBEB3c+KuqjI7UH3wnjOTaJpK2upogMAAAQJAd3PnFTJW1yyLS6ZYdE0bS4AAACBQkD3M8cpHNAjXg+61+LiVtAHt7hIA73pAAAACAYCup85KSlU4Jcou8VlyJBoaGBIVJKSrFoEAAAIFAK6nxV1UVFmD3p6yE2iXgWdVYsAAACBQkD3s2IuKkp5FxUNHxJ1n6fFBQAAIEjOiYBujAkbY940xvy83GcpyUgXFRkjRWqkZK+k/DeJSmLVIgAAQMCcEwFd0h9J2lbuQ5RspIuKJClWKyV7JA1as5i9SdQL6FTQAQAAgqTiA7oxplnSDZL+udxnKdlIFxVJUrRW6ncDenromkWvxYUKOgAAQKBUfECX9HeSvi6pYFI1xtxujNlgjNnQ3t5+9k42mpH2oEtuQM9U0L0h0bDX4pKppPcT0AEAAAKlogO6MeZGSW3W2o0jvc9ae7e1do21dk1TU9NZOl0RRhoSlaRozbAWF69yHou4QT1FiwsAAECgVHRAl7RW0seMMXslPSDpKmPMv5T3SEWyVrIjXFQkSbG67JCo1+LiDYd6FfQUe9ABAAACpaIDurX2z621zdbauZJukbTeWvu5Mh+rOE7afRy1B71bkpQcssXFq6T3p6igAwAABElFB/RAs8UE9EFrFgtscaGCDgAAECwj9E9UFmvtc5KeK/Mxiuek3MeRetBjdVLSraCnhra4eDeJMiQKAAAQKFTQ/Srb4jLSFpfBFXSvxYU96AAAAEFGQPcrr4I+2prFzB50r5UlMmQPOhV0AACAYCGg+1WxQ6LJHsnabKU8mq2gexcVUUEHAAAIEgK6XxUzJBqrlWSlVJ/SmQp6OLtm0WtxoYIOAAAQJAR0vypmSDRa6z4me7MV9KFrFulBBwAACBYCul8VNSSaCej93dlWFi+Ys2YRAAAgmM6ZNYuBkx0SHWUPuiQle5R2JGOkcCh3zWJ/ioAOAAAQJFTQ/cpmgvVIFfRYnfuY7FHSsdn2FkmKeUOiDi0uAAAAQUJA96tsD/oIv0ReBb2/R6m0k92BLg2sW0wxJAoAABAoBHS/KmoPuldB71XKsdlQLg0Mi/YzJAoAABAoBHS/KvYmUUlKukOig1tcjDGKhg0VdAAAgIAhoPtVMRcVxQbWLKYcJzsY6omEQuxBBwAACBgCul8Vc1GR1+KSWbMYHVRBl9xVi+xBBwAACBYCul8VdVGR1+Li9qCHw0MDeog96AAAAAFDQPerUi4qSvYomXYUDQ1pcQkbJVNU0AEAAIKEgO5XxVxUFI5I4VjmoqLcLS6SW0FPUkEHAAAIFAK6XxVTQZfcKnp/j5Jpq/CQCno0HKIHHQAAIGAI6H5VzJCo5Ab0ZI9SjqPosAo6axYBAACChoDuV8UMiUruqsVkz7A96JK3ZpEKOgAAQJAQ0P2q6BaXGqm/J+8edHfNIhV0AACAICGg+1UxQ6KSuwu9QAWdNYsAAADBQ0D3K5sJ1sVU0JM9Sjp2+E2irFkEAAAIHAK6X2V70Ef5JYrVSclepR0nz02irFkEAAAImlHKsyibknrQu5WyVuF8AZ0edAAAgEAhoPtVtge9iD3oyV4ljaNoniHRFFtcAAAAAoUWF78qeki0tuBNohEq6AAAAIFDBd2vih0SzexBT5o8LS4hwx50AACAgKGC7lfFDolGa933phOKhoa2uIS4SRQAACBgCOh+VfSQaK374CTytrj0U0EHAAAIFAK6XxXbgx7LBPR077CLimJhw0VFAAAAAUNA9ytbWgU94vTluagoxBYX4Cyz1uqJzQfV258u91EAAAFV0QHdGFNtjHndGPOWMWaLMea/lftMRSuxxSXm9OVpcTHqpwcdOKs27T+uP3pgs+5+YU+5jwIACKiKDuiSEpKustZeJGmFpOuMMZeW+UzFKXpItEaSFLPDh0RjDIkCZ92mfSckSf/y2j71p/j3DwBQuooO6NbVlfluNPO/YPR8OGnJhCVjRn5frE6SVGX7hq1ZjIRCcqyUdoLxUwYqwab9xxUNG7V3JvRvbx8u93EAAAFU0QFdkowxYWPMZkltkp621r5W7jMVxUmNPiAqZSvotepTdEiLSzTifp/LioCzw1qrTfuP6/pl03VeU51+9PLeM/Jj/Gp7G/91DAAqWMUHdGtt2lq7QlKzpEuMMcuGvscYc7sxZoMxZkN7e/vZP2Q+Nj16/7kkRd0KerX6hw2Jei0vKSrowFlx+GSfWuMJrZ4zUV+4fK7eOnBCm/YfH9Mf45V3O/TFH7+hZ7e3jennAgD8o+IDusdae0LSc5Kuy/Pa3dbaNdbaNU1NTWf9bHl5LS6j8SroJjFszaI3NJqkDxY4K7wwvmr2RN28qln11RH96KW9Y/pjPL/LLSK8d7R7TD8XAOAfFR3QjTFNxpgJmW/XSLpa0vbynqpITrq4FpfMHvQaDQ/o0UxFPckudGBE1lod7Uqc9ue8uf+EqqMhLZper7qqiD69ZpZ+8fZhHTnZNwandL20+6gk6cCxnjH7TACAv1R0QJc0XdKvjDG/kfSG3B70n5f5TMVxUkW2uAwK6ENbXLwKOrvQgRH9etdRXfo/ntX+jtMLvZv2H9eFMydk/3J822VzlbZW//LqvrE4po5192vLobgkaT8BHQAqVkUHdGvtb6y1K621F1prl1lr/7rcZypasUOi4ZisCavG9BesoDNMBozs3fYupRyrt1pOjPg+a23BrUiJVFpbDsa1cvaE7HOzJ9fq6sVTdf/r+9WXPP2Li155t0PWSjMn1KjleO9pfx4AwJ8qOqAHWrFDosbIRmpUm6eC7n2fCjowMq+9ZceRzhHf9wf3v6nf+8nGvK9tORRXf9rRytkTc57/4uVzday7Xz9769Bpn/PF3UdVXxXRR5dP08HjvaxQBYAKRUD3q2KHRCU50VrVKDF8zWKINYtAMdo73YC+fYSAbq3VS+8e1TPbWrWvY/iA5qZ93oDohJznL5s/WQun1uvHL+2VtacXqF/c3a5L50/W3MY69acdtcbHrrcdAOAfBHS/KnZIVJITqVGNSQy7qGigxYUqGzCSo139kqQdrfGC7zl0sk8nepKSpIc2tAx7/c0DJzRzQo2mNFTnPG+M0ecvn6uth+PZ/vFTsb+jRweO9eqKBY2aPcmdPWFQFAAqEwHdr4rtQVcmoKtfkdDQFhc3sPdTQQdG5FXQDxzrVXcilfc9WzPhevr4aj208cCw2Y439x3XqjkT832prls2TSEj/XJr6ymf8cXM9pa1Cxo1a6Ib0BkUBYDKRED3q2J70CWlIzV5bxKNMSQKFOVoV0ITa6OSpJ2t+dtcthw6KWOkr1+3UK3xhJ7fOXCp2ZGTfTp0sk8rZ03I+7WT6mJaM2eSnhkloD+ysUU//03+XvWXdh/V9PHVmt9UpxkTahQy0gEGRQGgIhHQ/ThsRN0AACAASURBVMopIaCHa1Rtht8k6n2fm0SBwrwd6JcvaJRUeFB0y6G45k2u040XzlDjuJgefONA9rU3vQuKClTQJenqJVO09XBcLcfzV717+lP6yyfe0Z8+9JYOncgN3o7j9r+vXdAoY4xikZCmj6+hxQUAKhQB3a+clGSK++VJhzNbXArcJEqLC1DYyd6kkmmrlbMmqDYWLjgouvVQXEtmNCgaDunm1c16dnub2jrdIc1N+48rFglpyfSGgj/ONUumSZKe3daW9/V/33JE3f1p9acc3fmL3PvUthyK60RPUldk/hIhSc0TCegAUKkI6H5VUgW9Ou9NojGGRIFRef3nUxqqdf7U+rwV9BM9/Tp4oldLZ4yXJH1qzSylHatHNh6UJG3af0LLZ45XLFL4t9R5jXWa31Snpwu0uTyy8aBmTarRH3xogZ5865Bef+9Y9jWv//zyBZOzz82eVEsPOgBUKAK6X5UwJJoMu1tchre4sGYRGE17Zgd607gqLZparx2tncPWIW497A6ILpnhVsjnN43TJXMn6acbDiiRSuvtgycL9p8PdvWSqXp1T4fifcmc5w+d6NVL7x7VJ1Y2644PLtCM8dX6rz/bkt1z/tLuo1o0rV5T6gc2xMyaVKu2zsSYXIAEAPAXArpflTAkmgpV521xiWYvKiKgA4V4FfSm+pgWTqvXse7+7NpFj7fBZXALy6cunqX3jnbr3pf3qT/ljNh/7vnIkqlKOVbP72jPef6xNw/KWukTq2aqJhbWn390sbYejuvBNw6oL5nW63uPae2g9hZJ2VWLhXraAQDBRUD3qxIuKkqGM2sWh11UxE2iwGi8MN40rlqLptVLGj4ouuVQXFPqq9RUX5V97qPLp6m+KqLvPrNTkrRq9ugBfcWsiZpcF8tpc7HW6pFNLbp47kTNmVwnSbrxwum6ZO4k/f+/3KH129vUn3Jy+s8ladakGknuakgAQGUhoPtVCRcVJUPVqjJJRU1uEI9G3MDOmkWgsPbOhGLhkBpqIlqYCejbj+ReKLT1UFxLZ+QOgNbGIvrYihnq6U9r+vhqTRufe0FRPuGQ0YcXT9GvdrRl/8vW5gMntKe9Wzevas6+zxijv/ytJTre069vPPIbRUJGl8yblPNZsyaxCx0AKhUB3a+cVNEtLsmQGwwiTu61397FRUnWLAIFHe1KqHFcTMYYTR5XpcZxsZwKel8yrd3tXdkB0cE+ffEsScVVzz1XL56qzr5Udgj0kU0tqoqE9NELp+e8b9nM8brl4tnq7Etp1eyJqqvK/f2gaVyVqqMhNrkAQAUioPtVCUOi/ZmAHkvnBnTv4qJkigo6UEh7Z0KNg1pXFk5zB0U9O1s7lXZsdkB0sOUzx+srV87X5y6dU/SP9/7zm1QVCenpra1KpNJ68q3DunbpNDVUR4e9908/coEax1Xp2mXThr1mjNGsiWxyAYBKVFyJFmdfCUOiXgU9aocGdO+iIgI6UEh7Z0LTB7WnLJzaoPtf3yfHsQqFjLZkBkSHtrhIbkj+5vWLSvrxamJhvf/8Rj29tVXvmzdJJ3uTunl1c973Th5XpVf//KphG5o8sybVcpsoAFQgKuh+5aSLvqgoYdzqXySVW0kbWLNIiwtQyNGuRM7w56Jp9epLOtnK9NZDcY2rimjWxNox+zGvWTJVB0/06n/+coemNlQNGwAdrFA4l9xNLgeO9QxbCwkACDYCul+VcFFRwng96Imc5we2uFBBB/JxHKuO7n41jsttcZGUvVF0y6GTWjK9QaEha0xPx1WLpsoYaU97t3575UyFT/GzmyfWqCuR0ome5OhvBgAEBgHdr0roQc9W0NO5FfRQyCgcMtwkChRwvKdfacfmVNAvmFovY9xVi2nHatvhzrz956ejqb4qe7HR4O0tpZrNJhcAqEgEdL8qoQc9Ydx9yOEhQ6KSFAkZKuhAAd4tooMr6DWxsOZMqtWO1rj2dnSrN5ke84AuSXd8cIG+dMU8XTC1/pQ/w1u1eIDLigCgojAk6lclXFTUl+1BHz4sFguH6EEHCjjambmkaFAFXXKr6DuOdI44IHq6rlkyVdcsmXpan5EN6FxWBAAVhQq6X5VwUVGf3HARTg+vokXCVNCBQtq73P/q1DgulvP8omn12tvRozf3H1c0bHT+lFOvcp9J46oimlQXo8UFACoMAd2vSrioqE/ukKhJDm9xiYZDrFkECihUQV84rUFpx+rnvzms86fUKxbx72+VsybVqiVPi0tfMq2OrkSerwAA+J1//9Q515UwJNqjTPUv2T3stSgtLkBB7V0JVUVCGjfklk5vk0t7Z+KM9J+PpVkTa/JW0P/TA5v1O//4ShlOBAA4XQR0vyplSNR6AX14HyotLkBhRzvdHejG5K45nDu5Nls1PxP952Np9qRaHTzeq7Qz8Bfxt1tO6qktR7TnaLe6E6kyng4AcCoI6H5VwpBoykq9qpL681fQWbMI5NfelcjZ4OKJhEM6f8o4SdKS6f4O6LMm1SrlWB0+OfAX9O8+szP77b0dw39fAAD4GwHdr0oYEk2mHTeg56ugh4z6qaADebV3Job1n3u8Nhe/t7jMHrLJ5c39x7V+e5t+66IZkqT3jhLQASBoCOh+VUIPeipt3U0uyeF9qLFISCkCOpDX0QIVdEn6/GVz9f98dJHqq6Nn+VSlmTXRC+juv/9/98wuTayN6q9+a4kk6b12AjoABA170P2qhB70lGOVMNV5A3okZJRyaHEBhko7Vse6+wtW0C+aNUEXZW779LPpE6oVMu5lRRv3HdPzO9v1zesXqXFclaaPr6aCDgABRAXdjxxHsk4JAd1RwlRJ/fn2oIfUn6KCDgzV0Z2QY6WmITvQgyYaDmnGBHeTy3ef3qXJdTHddtkcSdK8xjrtIaADQOAQ0P3Ipt3HYodE01Z9pjpvD3osHKKCDuRRaAd6EM2aWKvnd7brxd1H9ZUr56s25v7lfl5jHRV0AAggArofOZmAXmwPuuOo31Tl3YN+ymsWrZU2r5NSXHSCytSeucSnUA96kMyeVKsTPUk1jqvS5y6dk31+XmOdTvYmdby7v4ynAwCUqqIDujFmljHmV8aYbcaYLcaYPyr3mYriZPYWlzAk2l+ggn7KFxUd3CQ9/hVp97Olfy0QAO2dbkCviAr6pBpJ0h0fnK+a2MDvG+c11UkSbS4AEDCVPiSakvQn1tpNxph6SRuNMU9ba7eW+2Aj8lpciuxBTzpW/aFq6WSL9Oy3pKZF0rTl0pRFip5qBb2r1X1MdJb+tUAAHK2gCvq1S6dp/7EeffZ9s3Oen9fo7nJ/72i3Vs+ZWI6jAQBOQUUHdGvtYUmHM9/uNMZskzRTkr8DulNaD3racbSx+jJdFT0gvfjdgYD/xV8oEqo5tTWL3e3uY57NMEAlaO9MqDYWVl1V8H8bPH9qvb79yYuGPd88sUaRkNF7R7vKcCoAwKmq6BaXwYwxcyWtlPRaeU9ShBJ70JNpq411V0h/8Ib0F0ekzz7svnDy4Km3uGQD+vC2GaASjLQDvVJEwyHNnlTLoCgABMw5EdCNMeMkPSLpP1lr43lev90Ys8EYs6G9vf3sH3CobA96kWsW044iocwvZSTmtrdIUiJ+6i0u3UfdxzyDp0AlGOkW0Uoyr7FOe7isCAACpeIDujEmKjec32etfTTfe6y1d1tr11hr1zQ1NZ3dA+ZT4pBo2rGKhM3AE1XuFeVuQD/FNYtU0FHhjnYl1FThFXTJDeh7O7rlsG4VAAKjogO6McZI+oGkbdbavy33eYpW6pBo2g5U0CUpWuv2r/fF3TWLp3JRUXdb5sMJ6KhM7Z0JNdYH+5KiYsxrqlNf0tGReF+5jwIAKFJFB3RJayXdKukqY8zmzP8+Wu5DjarEIdGU4ygSGlRBN0aqbpASnYqFQ0o6p9Hi0s9/GkflSaYdHe9JqmlcdbmPcsbNa3RXLdKHDgDBEfz1BSOw1r4oyYz6Rr8p+aKiIS0uktvmkogrUmeUYkgUyNHR5V7ccy5U0M/LrFrcc7Rbaxc0lvk0AIBiVHoFPZgK9KCf7EkqnaePNJW2ioaH/FJWjXdbXEJuD7q1JYR0Jy31dLjfZs0iKpC3A/1c6EGf2lClmmhY7zEoCgCBQUD3ozw96P0pR+//9no9+MaBYW9PpR2FQ0Mq6F6LS8T9JS5p1WLvcclm2mII6KhA3i2ijefAFhdjjOY11rELHQAChIDuR3nWLJ7o7Ve8L6V9x4ZXwZKOVTRvi8vJbG96SasWuwetmqTFBRWo/RyqoEvuoCg96AAQHAR0P8ozJBrvTeY8DpZ2hmxxkaSqBqkvnm19KakP3Qvo0Toq6KhIXgX9XNiDLknnNdbpwPFe9Z/KRicAwFlHQPejPEOiJ3vdqno88zhYcoQWF6+yXtImFy+gT5gt9RPQUXmOdiVUXxVRdbS4Qeygm9dYp7RjdeA4/z4DQBAQ0P0oz5CoVzk/maeC7g6JFtjickotLpkVixPn0OKCiuTuQD83qufSoFWLDIoCQCAQ0P0oz5DoyRECunuTaJ4WFyelarnr5EpucTEhqWEGLS4IvN1tnWrrzL2k51y5RdTDLnQACBYCuh95FfTBPeh9yZzHwZKOo2i+FhdJNdb9A7m/1CHR2kYpRg86gs1xrG7+/iv68Hee16ObWrLrRs+VW0Q9E2pjmlQX0x4COgAEAgHdj7x+8cEV9J78FfS0Y2WtFM43JCqpxnH/QC6tgn5Uqmtyh0RTfQPnAQLm4IlenexNKhIy+s8/fUu/f98mHevuV3vnuVVBl8SqRQAIkIq+STSw8vWg9w1scbHWyhi3Yp7KhOfhN4m6Ab0q7Qb0ktcs1jVK0Rr3+6let5oOBMyutk5J0vc/t1pv7j+hv316h97Ye1zxvpQaz8GA/utd7aO/EQBQdlTQ/ShPQPcq546VuhIDm1y8yviwIdFMi0v1KQf0poFQziYXBNTOVrdivHhag+744Hw98dUr1DjObW2ZOr66nEc76+Y11qk1nlB3YvgmKACAv1BB96MRhkS9b9dXRyUNBPThLS717kO6W1JMKedUWlwyFXT60BFQO1s7NaW+SuNr3X9flsxo0BN/sFZPb23V1Yunlvl0Z9d5gwZFl80cX+bTAABGQgXdj/INiQ7afz74216Ly/A1i24FPZZ2K4jJYi8oSfZJiXhuiwurFhFQu1q7dMHU+pznqiJh3XjhjHNmB7pnXhObXAAgKAjofpRvSLQ3qdpYOPttj1cZH36TqBtKYqlMQC+2gt6T2YHuDYlKUpI/0BE8jmO1u61L508dV+6j+MKcSQR0AAgKArofZXvQB3554n1JzZpYm/22x+stjwxds5ipoEczAT1VbA+6d4toTosLFXQET8vxXvUm08Mq6OeqmlhYM8ZX69ltreroSpT7OACAERDQ/ahAD/qsSTXZb3vSXgV9aItLOCJF6xRNZiroRQf0wRV09y8EBHQE0c5Wd4PLBVTQs/74mgu07XCnrv27F/TsttZyHwcAUAAB3Y+yFXQ3oKcdq86+lJq9Cnrv4Aq6F9Dz/FJW1SuS6sx536iyFfRGKZYJ6P38J3H4T1u8T7/a3lbw9Z2ZFYsLplBB9/zOmln62R+uVVN9tb50zwZ985Hf5GyFAgD4AwHdj5xMBT0zJNrV5/4BOnNCjYzJDejZPehDW1wkqbpBkf5SK+i0uCAY7nphj373njcKtmvsau3StIZqja+JnuWT+duiaQ16/KuX6ytXzteDGw7o+r9/Qbsy/7UBAOAPBHQ/cnJbXLyWlgm1UdVXRXKHRL0Ker6AXtWgcLIr532j6m6XIjXuDvRsiwtrFuE/2w7HZa302nvH8r6+s7WTAdECqiJhffP6Rfrp712mzr6U/t9/21buIwEABiGg+9GQIVFvKHR8TVTja6OK9w1es+hdVJS/xSXcH5ck9ZfSg17XJBlDQIev7TjiVn1ffvfosNfSmQ0uDIiO7OK5k/SltfP03I52bTscL/dxAAAZBHQ/svkr6A01UTVUR4dU0DMtLkOHRCWpukGh/s6c942qu93tP5docYFvtXcm1NHdL0l6+d2OYa8fONajRMphQLQIt142R7WxsO5+YU+5jwIAyCCg+9GQi4q8QD6+JqrxNbkBPZm9STR/i4vxAnqxe9C7290KuiSFo1IoSgUdvuNVzz+0sEl72rvVGu/Led3b4HI+FfRRTaiN6ZaLZ+tnbx1Sy3H+XQcAPyCg+9GQHvT4kAp6PM+axfwtLg0yCTeolNzi4onVSv38oQ1/2X7Ebcf44tp5kqRXhlTRd7W5sxfnT6GCXowvvX+ejKQfvPhe3tefeueIvvCj14sfNgcAnBYCuh8VGBLNW0EfZYuLSXYrJKe4IVFrc1tcJLcPnQo6fGbHkU41jqvSFQsaNb4mOqwPfWdrp2aMr1Z9NRtcijFzQo0+dtEMPfD6AR3PtA55Nu47rq898Kae29Ge/S8TAIAzi4DuR9ke9IEh0XDIqC4WzgyJ5tvikr+CLkn1pqe4ylciLqX7cyvo0Rp60OE7O1o7tWhavUIho0vPmzSsD31naxftLSW6/crz1JtM6yev7ss+t7+jR7ffu0EN1W6xYMshBkkB4GwgoPuRkxp2i2hDdUTGGDVUR9SXdJRIuSE+7YwwJFrlBpRJ4b7iLioafIuoJ1pHBR2+knasdrZ2auE095/vy+c3quV4rw4c68m+/m57FwOiJVo0rUEfWtikH7+8V33JtE72JvXFH7+ulGP1wO2XqTYW1lYCOgCcFQR0P3JS2QFRSTrZm8petuI9em0uXvCOFtjiIkkTQn3FbXEZfIuoJ1pDQMdZN9I/r/s6utWXdAYF9MmSBtYt7uvoVn/KoYJ+Cr5y5Xwd6+7X/a/t1+/ft1H7j/XorltXa8GUcVo8vYGADgBnCQHdj5x0TgU93ptUQyaYe4/xXnfTi3eTaDhvi4sbUMaH+oprcenKXJs+bsrAc7FaWlxwVj3w+n4t/at/17+9fTjv694Gl0WZgL5gyjg1jqvKDorubHUHRNmBXrpL5k3SilkT9K1/3aqXdnfobz5xoS49z/0L0NIZDdp6OC6n2I1QAIBTRkD3IycthQZX0JPZynnDkAr6yDeJjpckTQj1KlnMH6rZCvrgFhe2uODsSKYd/eUT7+ibj76tRMrRzzYfyvu+7Uc6ZYx0/hQ3gBtjdPn8yXr53Q5Za7PX1rPBpXTGGH31QwtkrfSHVy3Qzaubs68tndGgrkRK+4/x+wEAnGmR0d+Cs87mBvR4X1IzJ7qXBo3PVtAzAX2kNYuZFpfxoV71pApU0F/+npTolKYuldq2us/VTh54nRYXnAUdXQn9/n2b9Np7x3T7B87T8e5+PfXOESXTzrB/tncc6dTcyXWqiQ38O3L5/Mn62VuH9G57t3a2dal5Yo3qqvjt7VRcs2Sqfv31D6k583uOZ8l09y/8Ww7FNbexrhxHA4BzBn+C+dGQHvR4b1INmXVx3qO3ycXr1c1/UZFbYWwwvYrnq6D3d0u//Ivc52omuRcUeaK0uODM2nLopG6/d6PauxL67qcv0k0rm/XUO4f10MYWbdx3PNti4dnR2qmFQ9pXLsv0ob/y7lHtau2kveU0zZpUO+y5C6aNUyRktOXQSd1w4fQynAoAzh0EdD8a1INurc1pcRk6JDpQQc9/k6gkNRRasxjP9Pje+F1p+kVS6xapfsgfvNFaKdl9uj8jIK/+lKMv/OgNhY3Rw1+5TBc2T5AkrV3QqEjI6Lkd7TkBvbc/rb0d3frYRTNyPmf2pFrNnFCjX+86qj3t3bpyYZMwtqoiYS2YMo5ViwBwFlR0D7ox5ofGmDZjzDvlPktJBgX0vqSjZNoO6kHPvV0024Oer8UlWiOFIqo3BYZEOzM9vpPmSzNXS6tuk86/ZvhnUEHHGfL01la1dyZ0583Ls+Fckuqro1o9Z6Ke29GW8/5dbZ2ydmBA1GOM0WXzJ2v99jb1px1dMIUK+pmwdMZ4AjoAnAUVHdAl/VjSdeU+RMmcVPaSIq9S7gXzqkhY1dHQwJrFkW4SNUaqqtc49eS/SdSroDfMGP6aJ1bnXl6UTp3iTwYo7IE39mvG+Gp94PzhFe8PLpyi7Uc6deRkX/a57ZkNLgunDQ/gl8+fnP0vSrS4nBlLZzToaFdCbZ19o78ZAHDKKjqgW2tfkHSs3OcomR2ooHu95l4F3ft2UVtcJKmqQePUo/6RKuhD21oGi2YGxRgUPSc4jj1ra/QOHOvRr3cd1e+smZV3huKDmTaVF3a2Z5/bcaRT1dGQ5kwePqTo9aEb465exNhbOsNtm6OKDgBnVkUH9GIZY243xmwwxmxob28f/QvOtEFDotkKevVAQG+ojg7ag+6GqbxDopJU3TByBb2qQaoaIcxkAzptLueCv3j8Ha3+70/rruffVW9/+oz+WD/dcEDGSJ+6eFbe1xdNq9e0hmo9t3OgzWXHEXcANN8/79PH1+i8xjrNmlibs+EFY2dxJqBzYREAnFkEdEnW2ruttWustWuamnwwXDaoB93rNS9cQXcUDRsZU7iCXmd7shca5eg8NHL1XJKimUolFfSK196Z0MMbDygSDunOX2zXlf/zV/rJK3vVX2hF52lIpR39dMMBXXlBk2ZOqMn7HmOMrrygSb/edTS7rWj7keEbXAb7+nWL9CcfuWDMzwtXQ3VUsyfVasuhk+U+CgBUNAK6Hw26qOhkgYCeXbPo2MLVc0mqalCt7VF/oQp6w2gBnRaXc8WDb+xXMm31wO2X6qe/d5nmTq7Tf3lii676znPDhjVP13M72tUaT+iWi2eP+L4rFzapsy+lTftP6GhXQke7Enn7zz3XLZumj6+YOaZnRa6lMxpocQGAM4yA7kd2eEBvGBTQG4b0oEdDI/wyVjeo1nZnK5A5Og9L9SMMiErumkWJFpcKl0o7uu+1/bpiQaPmN43TJfMm6cHfu1T3/O4lqotFdPu9G/Xqno4x+/EeeGO/GsdV6cOLp4z4vrULGhUOGT23o007MgOii6Y1jNk5ULqlMxq0r6MnWyQAAIy9ig7oxph1kl6RtNAY02KM+VK5z1QUJzWoxcXtNW+oHlhZn9Pi4jiK5NuB7qmqV42TZw+6k5Y6j4xeQY95AZ0KeiV7ZlubDp/s062Xzck+57WYPPh7l2r25Fr9x3s2jEnv8ZGTfVq/vU2fXN2c/wbcQcbXRLV69kQ9t6N9xA0uOHuWznBvFN1GFR0AzpiKDujW2s9Ya6dba6PW2mZr7Q/KfaaiDBkSrYuFc/acN1RH1JVIyXGskmmr8EgV9KoG1TjdOtHdL2sHtbl0t7uV+lF70DMtLv0E9Er2k1f3asb4an140fCK9oTamO793UtUVxXR53/0ug4cO71/Fh7acECOlW4pMBw61JULm7T1cFy/3tWuyXUxNdVXndaPj9PjbXLZepiADgBnSkUH9MByctcsDu4/l9wWF2ulzr6U0o6T/xZRT3WDwkor3tWpN/YeH3g+nlmxONIOdGlQiwsBPcg6uhL6zz/drF2tncNe293WqZd2d+izl87Jf+GVpBkTanTvly5RIpnW53/4ujq6Eqd0DsexenDDAV123mTNbRy+KjEfb93iczvaqZ77wJSGajWOq8rbh/5ue5eOnuI/GwCAAQR0PxoyJNowJKB7gT3el1QqbUdtcZGkKbGEHt3UMvB8Z+aSolEr6PSgV4L7XtuvRzcd1Od+8NqwCvhPXtmnWDikT49S0b5gar1++IWLdfBEr373ng3qTpR+edVL7x5Vy/Fe3XJJcdVzSVoyvUFTMlVzAro/LMkzKLpx3zFd//e/1p899FaZTgUAlYOA7kd25IDuff9kb1JJxyoyYouL2y/60fPH6V9/c1h9ycxuayro5wzHsfrphgNaNK1efUlHn/vBa2qLuzdBdiVSemTTQX10+TQ1jhu9dWTN3En63n9YpbdbTuhvfrG95LOse32/JtRGde3SaUV/jdcLL7m70VF+S2c0aFdrpxIp9/eTA8d6dPu9G9WfcvTS7g51ncJf3gAAAwjofjSoBz3eO7zFZfyggJ52nMK3iErZCvpHz69VZyKlX25tdZ/vPOz+GHWj7H1nSDTwXn63Qy3He3XHB+frx1+8WO2dCd32w9d1oqdfj715UF2JlG67fG7Rn3fNkqm67bK5uu+1fXrnYPH7sFvjffrlllZ9clWzqqOlXST0kUygv2jWhJK+DmfG0hkNSjlWu1q7FO9L6nd//IaSaUd3fmK5+tOOnt/hgwvfACDACOh+NOSiosG3iEoDt4rGe5NKpm3BvmFJUrU70LVsstGM8dUDbS7xw1L9tGylvqAIN4kG3YMbDmh8jVu1Xjl7ov7ptjXa096tL/74Dd378l4tm9mglSUG3z++5gJNrI3pr362JXf4eAQPvH5AKcfqs5fOGf3NQ1y9eIqe/7MPsmLRJ7xNLm+1nNBX79uk94526x9vXa3fWd2sibVRPbOttcwnrBBdbdLL35PyXTQHoKIR0P0oZ0g0NbyCXjtQQU+lR6ugu4EmlOzUTatm6oWd7Wrr7CvuFlFJCoWkSLXU331qPxeU1Ymefv37liP67RUzslXrtQsa9Q+fWam3DpzQrrYu3Xbp3MI30RYwviaqb1y/SBv3Hdejmw6O+v5U2tG61/fr/ec3al6Rw6GDGWM0Z3LpX4czY86kWo2riuhv/m27fr3rqP7HTct1+fxGRcIhXbVoqtZvbxu+2hWle+0u6Zd/IbW8Ue6TADjLCOh+5KSkUEiptKOuRJ6APnhI1CluSFR9cX1iVbMcKz3x5qHibhH1RGupoAfU428eVH/K0aeGDIBet2yavvvpFfrgwib91kWjzCEU8MlVzVoxa4Lu/MX2US+teWZbm47E+3TrKVTP4T+hkNHi6fXqTKT0lSvn5/zzdc2SKTrZm9Qbe4+V8YQV4t1n3cfdT5f3HADOOgK6H1m3gh7vVuYNbwAAIABJREFUy1xSVBPJebkuFlY4ZDIV9NFvEpUkJeKa3zROK2ZN0CObWoq7RdRDQA8ka60e3NCiZTMbsi0Jg318xUz9+IuXqCZWWj+4JxQy+uuPL1VHd0J//8yuEd/7L6/u04zx1boqz551BNPnLp2jL10xT1+/dmHO8+8/v0mxSEjPbG0r08kqRHeHdGiz++1dBHTgXENA96PMkGg8c1vo0Aq6MUYN1RE3oI96k6gX0N391zevmqn9R9qlRLyECnqNlBzU4uKkpY53i/7p4Mzq7U8rlaed4J2DcW07HNen1xS/0rBUFzZP0C0Xz9aPX96rnXl2rEvubuwXdx/Vf3jf7JHnJRAoH18xU//lxiUKDWmxq6uK6IoFjXp625Gi5xOQx55fSbLSwo9Khze7/eh+0b4z+2cKgDODPy39KNODfjIT0IcOiUruqsV4b0opxyo8Ug96KCxF66Q+d2fxjRfOUHM4c2FRsRX02JAK+tsPS//7koFVjSgba61u/v7Luvpvn9eOI7l/YD64Yb+qIiF9bMXMM3qGP7t2oeqrI/rLJ97JG8jue3W/omEzrM0GlevqxVN14FivdhT4SxuK8O56qWai9IE/Hfi+H6RT0j99SHrx78p9EqCiEdD9KHNRkdfX6w2FDja+JjrQ4jJaVbK6wa2YS5pYF9P1s90QlR5X5C7qaG3umsXWd9wqf+uW4r4eZ8wrezq09XBch0726bf/90t68i33L019ybSe2HxIH10+fdh/gRlrk+pi+tOPLNSre47pTx56a2DXvtzq/sMbD+japdM0pb76jJ4Dp6G/Rzr05ph93NWL3VamZ7YWt83FWstQ6WDWuoH8vA9K01e663D90uYSb5H6u6T20u9BAFA8ArpfDK5QZy4qOlmgxcV7Lt6XVHK0LS6S2+bSN7Cv+upZboB6vWP0i2kkuS0u/YMC+vH33Mf2HcV9Pc6Y+17dr/E1UT3zx1dq6YwG/eG6N/Xff75VT751SJ19KX3qDLa3DPbZ983WH199gR5786A+8X9ezt5W+uRbhxTvSzEc6ndv/LP0Tx+WesZmsHNKQ7VWzJqgp0cJ6AeO9eh/PbtLH/7O83rf/3hWJ3r6x+THD7y2re6c0Pyr3E1aC652B0ad9Ohfe6Ydy/z+f3xvWY8BVDoCuh888VXp+5cPfN9Jjd7iUp2poI+2xUWSmi6QDrzm/qdJSYvr3H7yx98tsmI1dEj02F73kQpKWbV19unftxzRJ1c3a/bkWt3/Hy/VFy6fq39+8T1989G3NWdyrS49b9JZOYsxRn909fn64ecvVsvxHt34v17UczvadO+re3XB1HG6ZN7ZOQdOUft2tzAwhrMl1yyZqrdaTqo1c2utJ5l29OAb+/Wpf3xF7//2r/Sdp3dqYl1Mx7r7df/r+8fsxw+03ZntLfM/7D4uuFrqPS4d3FS+M3m8YH58r1vpB3BGENB9oNVOlD22V0ol3CecdGZI1A3U+Srobg96UmnHKjLSFhdJWv4pqatVeu95SVKk+4h6Q3V65UCiuAMObnGxdqCCfnRncV+PM+Knb2Qu/nnfbElSLBLSf/3YUv3tpy5SNGz0hctL329+uj60aIqe/MMrNH18tb7wozf0zsG4br10zlk/B0rUsTv3cQxcs2SqJOVcWtRyvEefuusVfeORt9XRndCfXbtQL37jQ3rkjst1xYJG3fPyXvWnaHXRu89KTYuk8Zn5kflXSSYk7X6mvOeSBn7/7++Suo+W9yxABSOg+8D6jokycuQczVSvBg2JRsNG1dHhv0wNNRHFe1Nui8toFfQLrpWqJ0i/edD9fvyQ+qqnaP+xHh3tKiKkR2sGAnpPh/sbcyiaqbpRQSmHtGO17vUDWrtgss5rGpfz2idWNeutv/qIvnD53LKcbc7kOj32+2v1iVUz1TyxRr+98swOqWIMnIGAfv6UcZozuTbb5vLLLUd0wz+8qF2tXfqHz6zUM//5Sn31QwvUPLFWkvSl989Tazyhf327TMPnD3xW+revl+fHHqy/R9r3ykD1XJJqJ0kzV/tjH7rX4iINhHUAY46A7gNT5i6XJB3e8xv3CSeVHRIdXxPNW30cXxNVf+Yio1F70CNV0tKbpG1PSokuqfOwQuPdDS6b958Y/YCxuoEWF+8357lXuH3tflr9dQ55bkebDp7o1Wffl7+3uyoSLmvVuiYW1t9+aoV+/fUPqT5PixZ8pPe4+xdvaUwDujFGVy+eqpd3d+gvn3hHt/9ko2ZPqtW/fu0KfeyiGQP/fHa1S30ndeX5TVowZZz+6YX3zv56xnTSHcJ85xHJKXMFf9/LUjohLbgq9/kF17gtLuWuWh9/T5qQ+X3nGAEdOFMI6D6wcOlKSVLbnrfdJwYNiTYU2MDhtb2c6EkWt1v6olvcKvj2n0vxw6prnK1wyOjNA8dH/LKjXYncCrpXMVl4vfs4xn3o8b6kthw6OfobzwHtnQn94/Pvqqc/Ney1f3l1n6bUV2XbCPyK1pYyO7F/9L7ljj3uY7hKOja29xtcs2Sq+tOO3nvtSf3v8zfq4Tsu05zJdblvuue3pH/9E4VCRl++Yp62Ho7rlT0dY3qOUbXvcENxz1F3QLOc3n1WilRLc9bmPn/+1ZJsedct2v/L3p3HR1Wdjx//nOwrkAXCEkISEgiLIYRdUUEEVCwuPxX3famVamlt1S5qtf1qq22te92qVgURFRRFAdn3RRIIEEiAQAIhCYHs68yc3x/nTjKBBBKIZBKe9+s1r5m5c+fOmXtnee5zn3OuNn2QYi8GlGTQhfgJSYDuBnpFhHOYcGrzrVFRXE5U1FgHUWjYcdT7VBl0gN6jICQaUj6Gsjy8uvRkQI9gtpwkg75ubyEj/rqY3Apl2mSrqc+YxE80161ch/7vxRn87JVVEqQD767ax/ML0rnprXUUlNaXImUfrWDZ7gJuHNH71ENsinPblz+HdydBzqam53FmzWMuNJ1EWzF7PbxPCHeeH81LkcuYkvsavhy3s1l6GAp2QvYGAK4e2ouwQB/eWXmWA7/c1PrbVl+dNrN3GUSNMYkRVz2GQlB32D63TZoFmFF+akqh6wDo1FNGchHiJyT/7m5AKcWxgGiCSveaQ7tWDXpJZW2TY1i7Tvc8VSdR8yKQOA32rTAZ+uAeJEeFkJpdhN3R+B/y99sPozXsOGL9qdZWmIxJcE8IiTHDN7ZyBn3prnwcGv40Nw1HE+06V6zYXUBkiD+788q45vXVZOabk77M3HAABdw4MqptGygaOvhjfUdvd1CaZ8olHLUw+/amSyMKM00HxL4TzHe8NLfVmuDl6cHTPxtIWMlOsFXBwc0NZ9i/xlwX7YfKIvy8PbltTB+WpOeTmV/WYNYDhRXc8d4GPl6/v9XaVyc31ZzQLTTWBMhtpbII8ndCn/NPfMzDAxKvh4zvW1bmkr0RPry65UNo7lkKWasbTnNmzENjzH+AlLgI8ZORAN1NqPB4ohwHySksA3RdJ9GmSlxcp3ufqpOoU+I0lwX0ZGhUF8pr7GTkN362v+W7CgDYXuAM0CvND3JojAn4u/Zv1bHQs49WsLegnOF9QvjxQBGfbsputWW7m5Kq2iZ3jMCUt+zILeGmkVF8+sBoqmodXPv6GlbsLmD2pmwuSYigZxf/Jp8vzsCxLMhrYZlDUTa8fQlsePsnadJpSf8a0HDNWyagm3N34+NoF2ZClyjoNsC6f5plLsUHzfs/PgNftL/+PAxZqxo+dmBt/e28NABuHd0HHy8P3ltdH/zN33qIKS+vZPnuAt5Ytqf1a9RzU6FHIsSOt2rAa898mXnb4Yv7Gw5ReyoHNwEaIkc0/viQm83RzG2fNW95WsOiP8HepbDixea3A+DrR2DBYw2nOQPykBgIjZYSFyF+QhKgu4kuUYMIUlWk7bQCAw8PSqpsdPb3anR+1wz6KUdxcQrrC72Gm9vBPRjaOwSg0TKXA4UV7D1STmSIP3uKrE5Tzgx6SIy538oB+vLdZofgb9clMjImlOcXpFPYnFFm2pnyahsX/X0pry9tukPeygyzLi6K70piZBe+/MX5dOvkx+3vbeBIWQ23jpbs+RnZs8RklRvrEPjVL2HmjS1b3v7VgG4YcLa1HfMgvD8MmQZX/tOUbiz5y4nzHd0DYXHmAqffUXT9G/DtoyeWvTnLR7wDTwzQ96+FbgOt+Uwn+fAgX64d2ovPN+dwqKiS33+5jemfbCEuIohfXRpPzrFKUnNasQTOYYfD26DHEHPmzpqyEzP9pyPlEzNy1tbZzX9O9kZzNKPXsMYfjxgIPZJMqWJzZK0yn8lOkbDhrebvfJXlmx2r/O1Q7ZLAcQbkIX1MyWRZHtSUN2+ZQogWkQDdTXSLSQTgcIbp0KWVyaA3p8TllOOguxp+l+mAFBJNn7AAQgK82XLgxI6iy3ab0Vl+f8UAqrDOOFp+xPwgh0ab++H9oTy/dc4+aK9l+a58eof6ExseyF+uHkx5tY3nF5xYQrNh31HGv7iM15e13ogTZ9OiHXkUVdTy2eacJjOBK3YXEBbow6CenQDoHRrA5z8/nwvjwxnYoxMXxXc9m002nOVX7UVtpTlDpv3ETrZs/sAEsPnbG063VZt66KL9UJzT/Nfab5UCZK93j6FHy4+Y4GzgVHN/6K2QfAes+iccSqmfT2sTtIXFQade5rfhdAP0/dbOibNsxSk3FTy8IPEGs25t1tlCK4tM1nzgVRAUYYJkyz1jY6i2OZj4z+V8sv4AP7+4L7MfGMNd58fg7amYn3qSoRhL82DTe80fjaVwD9SWmwA9eiygWqfM5cA6c73ujeZ/JrLXQ7dB4Nep6XmSbjHrymV9NWnFC2bd3vUNePrA4qeb2Q7TJwDtaNjJ+Og+CO5h6uOdiRqpQxfiJyEBupvw6NYfANthEzDUaIXdoZvsJBrsV59ZP+Uwi66SboHfpENAKEophkaFNJpBX76rgD5hAVw+uDs+/taoC87RDeoy6Alm3jWreHvF3ua34Xi2GvRLifTb8z4X9+uKUop+EcHce2Esn23OYWOW2QGwOzT/XpzBjW+tJauwnP8s30tlTTsKGC1fWcHFgaMVbG0kE+hwaFZmHGFsfDgeLtu2c4A3/7tnFPN/ObbB9Gax2+rLDE7X2tfgpfPqAyx3lzoLvvmNqdl1pXV9Jvf4jO6hFFMrDfUBZ3PsX2uC0PIC9zjsnz7fBFcDr6qfdunTgIKMhfXTyvJMxjgsztQ4h8aeXolLTTnkWoH/8UcRclNNp8K4CWCrhENWwJezEdCmQ2T38+Dw1rqnxEcEM3lQBH7enrx/1wgevzwBb08POgd4c1F8V77dloujvInEwLrXYf4MSJvTvLY7M/w9hpjxxnsmwd4z7ChaU2HWR0iM6QTbnIDfYTedeXs3Ud7idN515jwUKTNPPl/2BnPU5PyHTbZ77K9g51fN+1znbDCfZ+dtJ9cjqKHWtdShi3ZEa332h3E9TRKgu4ugCKo9gwivNIFulc0EYE1l0L09PQj08QRo3jCLTkqBf0jd3aG9u5CRX0ZxZX3NZVWtnTV7CuuC5f69uwPgyLOyjc4f5q79AFi+ehV/+y79hFN6H6/JL8WBtajSQ0xmNRf361Y3+eEJcfTq4s8fv0wj51gFN7+9jn8t3s3UIT15947hFFfW8lXqwea/dzdwrLyGFbsLuGlkb7w9VV2w7mpHbgmF5TVNZslbHJwDrPg7vDwUak++jU4qbQ6UHITsdae/jLNpj3W6dOdp050Kdpnh9KCRkgsrE+4dUH/7VMryoTADzrve3M/ecPL5z4Yd80ywHTG4flpAqKmzdg0WndnysL7116cz1GLOJlMb7R/aMADU2uz09BhSP2xg1kpzvX+NCQIjR0D3RNPh3KWT7Ss3DmX9mHWM69KwQ+SUxB50LUlDvdgX9q08sS3O7b3oqeaVX+SmmCMH4SZJQszFZuehuuzkzzuZg9b6mPQsBHY1WfRTKUg3I6T0HnXy+QJCof9lsG32yWvll/8dAsLMUVOAMdNNB/+FfzBHF6qKzWe1sf4W2RtNKU14P3Pb6VhW/e+/ZNBFO/ToZ1u5/s217SJIlwDdXSiFLaQv/ZU5rF5pJYab6iTq+lizO4k2YmiUCdZTs+uz6BuzjlJZa2dcfxMgJsaYAL0828pwOX+YO0dh8/CjZ+0BbA7NJ+sPNPk6h4oqGfLnhczd0khAbZ0dL9FjH+dH1JcjBPh48dTPBrIrr5TxLy5j28FiXrx+CP+alsT4/t1I6B7M+2v2t4svmtOCtMPYHJpbRvXh4n7dmL/10Amj1Thr8S/sF946L6q16VRWUXj6Q8iV5cOhLeZ2hhuczfBU7LX1WdDMxQ1LDJwBYsxFJkB3LYXYv8YEatFjTyzVaIpzvuF3m5GNstefefvPRMVR894HXmV2yF3FjjNBmTP4dAbooc4APc5kRBsrCzqZA2sBBaMegOID9eVBJYfMzpAzO91tUP3IIAfWmiDQJ8Bk0B02M4KJxSc/Ba9VL5qyHBcTB0Zwlfd6lHaYEwu5Ks2DvG3Q7zIoPQRrXjl123NTIWIQeFoZ49iLzcg3Z9Kf4MA6QJnP0fB7zFGcUx2ZcH5ueo889fKH3GyO1hy/8+l08EfzuzpmujnRHJj1POFPpr7+xXh4PgrenQjvTW7YkdVea77rvUdC5Eizs6K1mac0t/73PyAU/Dq7xxEjIZoh+2gFX27JYdP+Y6zb2wqluT8xCdDdiF+PAfRVJqNaaf0/NpVBd33M83QyqpYhvTujVMOOost3FeDj5cHo2DAAhsaaU7V7HdkJvp3rM/AeHuxXvRjie5iL+3Xlkw0HqLE1Xvf55vI9lFTZ+MeiXdTaj5snYxH5yuwMBGY3DCAnDerO1Uk9GdSzM1//cizXDYtEKYVSijvOj2Znbgmb9jd9sqWj5VY5RsEutyjN+Cr1ILFdAxnUsxNTk3qSV1LNhqyGPxQrdhcwoEcnugX7tc6LFuyCo1YJUvr801uGMxDo1Kt9BOg5m6C6xAThRfvr3z+YAL1zb1PuVVVUN3oIDrsJkvqcby5HdjVvOLv9a0zGvedQiBze9hn0Xd+aoVQHTD3xsdjxDYPPwj3mBEWdI839sDjzeHHTO9uN2r/GZOv7TbbuW8t3LR8BiL7ArOPqUhMo9hnT8HHXuuod86z3812DIz/Bvl78zMfqq7Pr24Y7WM6T+Ix7AgZeDateOnlfAq1N51Tn64MpufH0PbM69ANW51f/ELPj5ukD6988+XOyN5hsuzMAPpn4iRAQDqtfMuOi52wy23LHPPju9zDnLvDrAiPva/i8xBth2F0Qd6kpebr0afM9cS17OrzNlCJFjjDlNpVHzbKdmfKQ6Pp5Q6KlxEW0G/9dnYWHUnQJ8ObdVWdQlnuWSIDuRjy79cNXmUOWFacocYH6DHqLSlyOE+znTb9uwQ3OKLpsdwGjYkIJ8DEZpeBOpsOSv63EdBC1snIp2UVsrenOAO/D3Hl+NAWl1Xy//fAJr3G4uIpZG7LpHxFM9tHKhln0Y/uhIJ23aiZR7hPe8I/C8tKNQ5n70AX07RrUYPrVSb3o5OfF+2uyGn1vry7JYPhfFrFtzQJ4bSS8Msx0HmujsaoPF1exft/RutOcXzqgG/7eng3KXMqqbWzef4yLWit7DvVBeZ+xsGvB6XX0zFhoTpIy+kFTU1vUwgDubNvzAyhPmPisue/cwXDWn0ePtToEUl/mkpdmgpU+F9SXYzQni7p/jck2enqb8oS87VBV0rrvpyV2zIPOUWaH4XhRo03wuWepuV+4x5TCeJhyubpMekvq0O21JsvaZwxEnAc+QXDAOqqQmwoo6G6V2kSPNaNBbXwX7DUQZY33HRJjnucM0LU27yMgzJR97F1a/3r5O+hqy2WtfSCqLK/hiCt7fjCBa/dEmPiMqcNf/Oem234sC6qLGwbo3v5me+5dfnrfFbvNBNtRo8394AgYfB1s+dis1+wNsPWzE+vcs9ebjHVzzr7r6Q0j7jGfz8/ugHcmwCvJZmSiTe+aHelr3gTf4IbP8/CAn70E1/4Hxs6AMb80OwVpX9TPk2OVtDgz6GDq0J2BeKjLDkRIjGTQhVvQWrM7r7TJI+olVbV8uvEAPxvSkzvGRLN4Zz57C86gjO0skADdnYT3q7t5zKpxaaqTqOtjzTqT6EkMjerClgNFaK3JOVZBZn4ZF/dzqX/2Dqi7WR3cp+72h2uzOKAiCarK5eJof6JCA/jf2hNPIvKfFXuwa83btw9nYI9OvLY0E5szi26Vtyx1JFEbMwEylzT78Lq/jyfTRvTmu7TDHC5uWFu9fm8h/1y0G4eGsh9eRAeEmT/K+TPg5WTY8VUz107L7C0oY/GOvEYfm7/1EFrD1CE9AVPCc+nACBZsy607qrBuTyE2h+bi1hylJf0bM7zmiLvNYfGcjad+jiu7zQQ+8ZdCvJUhdfcseuYPJpvdM8kEEc569PydptQneqzJGofE1AfozlKVPmNM6YWX/6nLXCqPmcDeGdD3HgloazzrNlCWb4LvgVMbD/S8/U3g6MwOF2bW15+Dy1CLLQjQc7eaoDtqjCkT6T2yYQY9vF99mYVzPa191Vw7g1gPD5OBd3YUPbzVBM/jf2/KKJzZdID0b9AonuFe7HjW74A6HCaD3vcSs7yQPnD+dFOrvcclwG/Q9oYZ/rpzE/Qdb0pl/tIN/jkI3rkUPr8Xlv4fpH4K27+Epc/B7DvgnYlQ4DK0ZP520/E2akz9tNE/NyPFvJJsykq+uBc+urZ+h6T8iDnK05zyFqfxv4ff7YOfr4KbPoWpr8I9i+HxbLjrW+h/+amX4ellSqF2f19f9pS9wdSqd440AwH4djLT6oZYdAnQQ2PMznpLS6KEaGVvrdjLpH+t4MNGYhCATzdkU15j556xMY2ea8EdSYDuTpydlIAdhyuAn77EBUyAXlxZy74j5XX1z+P613fWdD3ldJbDTD9aXsP8rbl0jTV/bB6FGdw6OooNWUfZmVufPcwvreKT9Qe4ZmgvosICeHhCPFmFFczfap2tMGMxR7x7UBoYQ+fEK0w2K6f5JQK3jY7GoTWfuJxd8Gh5DQ/P2kJUaADvXxnEGPsmNnS7Hu5ZBLd9CYFh5qQtOa0w1rGLwrJqbn57Pfd+uInv0k48G+NXqYcY3KsTsS5HAn6W2INjFbWsyjSlFCsyCvD39mRYdMgJzz8txQfNqBkJUyBuohn94fgylyMZsPwF+OxOeG0UvBDfcHz7nI2mQ1ncRAiPhy593DtALy80NbR9J5j7cRNMZ0JbdX0wHn2hdT3WdAZ1OMx1lygTmHj5mAD/VB1FD6wHdP2ZH3sNB1TblbmseslkjYff3fQ8seNMEFmSa4IuZ1AOEBhuythaMtSiM1vuXAdR55ujLBVHrRMAuWSnA8PNiC7lBSb4Cwitf6z7eXA4zWyLHfPMEZCB10D/KZD+bX2JWvp8VO+RxCQksVENRqd/Y6YfToWKQlL9hnPN66tJP1xissShsfDxdbD65QblMGXVNvanrcWuPJmxtIaJ/1xOvz8u4Ma31pIddytM+acZASXmQvMbmL3edLz88n7zXVn+N/P+8tIaDl/o3Dnp4xKg9xgCV78Bl78AN8+G+5aa8pd50+sz7nDqDqLHCwg1663/ZZB8mylJ8fJp2TIGXWtKWpyjHeVsqB9JxsMakz1no9lh8u3UcJuFxJi+AyXtq7O+aF9KqmqbLJ8F2HW4lH8s3I2PlwfPLdh5QmbcZnfw39X7GB0byuBeneka7Ms1Sb2YszmHY+VtX/raFAnQ3UloDNoa2mpXvgnQg1yGUzxeJ+skRt5nUOIC9R1FtxwoYtmuAnp18adv18D6Gbz80JidgB/LugDw6cZsamwOxoy2/pS3zeGWwE3c6L2CpYu/qXvqOyv3UWt38NB4EwRMGhhB/4hgXlmSgb2mEr1vOYtrE7m4fzdU3/FmVIdGylyaEhUWwISEbnyy4QDVNjtaax79LJVj5bW8enMy4458SrXy45E9w8gpqjTZtdvmQqce5tBwxVHSDhaTdvDUQxBm5pc1efZPu0Pz8KwtHK2oIaF7ML+ZndrgVOX7jpSzNae4LnvudHH/rgT7efG1VeayYncBY/qG4evl2ex1cFK7vjXXCVeasZVjL4ad8+s7TVYchfenwNK/mKA2NBbs1eaEM855MheZYKnveJOVjZ9kOpu602ntXe1dCmhTZwvmurbcdNzLWmnKP0KsI0ExF5k69MNbTWDlzPCCuX1428nLVfavNvXFzhPL+HUyHQ7boqNo6WFT3pA4rWFW/Hix48x1ykemzMR1XqXM/ZYE6PvXmkAt2HQmrwtMd35tOmq6Buhg6tChYYYZzAgzNaVmp2H7XBMYB4aZDG91sfnMFWWboDhhClPO68n8mmRUYQYU7EZbZUz3rApmy4Ei7v7vRvKrvU0w3P8Kc0bNmdOgvJC8kiqufHkl+9LWssseyYbscvqEBXD7mD5sP1jC5De28D/7pegJT5pSkTu+hl9tgz/mwUMb4YGV8IdceCQFxv4adn1Tv8N/YK3p4+Cs63dKuhlG3W/q9HslwxUvmhFk1r1mPi8e3uaIz9kWNdqUr6V9YTrZFh2oL20Bk9XP32G+CyHRDY/MOMtd2mmZS/bRCl74Pp38U4xAVm2zt8shfTuCXYdLGffCMq56bTVFFScG0zU2BzM+TaGTvxfzHroAXy9Pfj07tf4oPWZwhkPFVdw7NrZu2j0XxlBV6+CTDe5brikBujvx9EZZhw+rHYpgP6+TZsc719Wgn1kGPa5rEMG+XmzYd5Q1mUcY198Mr1hHKZRV5rIkL5Bau4OP1u1ndGwoMfGDTVZl3WsEfn0/z3u+yQOZD1K+YyGFZdX8b+1+rkrqRUx4IJTk4lFxhF9OiGNPQTkbl81H1VbwfU0iF/fvag5l9x7d4uzs7WOiOVJWw7fbcnl31T6WpOfz+yubyQIFAAAgAElEQVQSGBxcDltnUzvkForpxDNfW8OJBYTC9R9AWR65/72Na15byTWvr+bzzY13Jqu1O/jT3DQu/edy7v1gIyVVJw5t9tLi3azOLOQvVw3mv3eNwM/bk59/tJmyanPo1xmAX5nYMED39fLkskHdWbg9j4y8UrIKK7govpXrz8Pi64bEJGGK+TMtsE4AteAxU/Jx/3J4JBVumgmX/An2rTCH8cHsMEWNMdsHTIBeW9H8YQjPtswfTHbSGexEX2iCn8zF9fXnTs6AfPN/zWgjrkFjnzEmG1130hZtzg7p7IgIpgSm17AGR5noPdIadrCZJ8ppLateMvXgF//25PP1GGLWz6b3zX3XDDpYAXozS1wcDhOQOrPnYNaHhzes/0/967lyrn/X54DJBIM5++bRPfVjuPcdDz7BsGNugx3OSxK6sdrTZHrtO79m//qvSHNEM2JQfz5/cAxFlbXc88EmKjyD4IYPTUC8dxmOl4ey/JUH8CvL5vyAbOKGXMDqxy/hnTtG8NTPBvHdjItIjgrhT3PTuO3dDRwsqsRmd7CnoIxvdx7lXynw9EZPDjlHcBz9c1Mrv+RZ8xk5sI7aXiN5bM5WHpuzldJGfi8A8/4SrjRlMzu/Njsorp8jF3aHpqLmJyoj8fCkst/PcGQswpG52ExzLbWJHGm+BwfWNuwgCvX327ij6NL0fOZszmkygdKYtIPFXPvGGl5buocpr6xiw77GR/VYnXmE8S8sY/yLy0hxGe1MnBmHQ/Pm8j1c8/rqBqPIucrML+WWd9bhoRR7Csq49d31DYaEBnhlSQY7ckv4v2vOY0CPTjx79WBSsov4j3VuFq0176zcS0x4IJck1FcG9IsI5qJ+XXl/TRbVNvfc+erwAbpS6jKl1C6lVKZS6vG2bs8pWXXodjxOWt4CLgH6GZa4eHgohvTuwtyUg5TX2BuWtzhZfxzbK8P49+IMDhZVcvuYaNNZ6RfrTEbpoY3svn4Zu3VvvL64iy8WLaPKZjfZ873LTUfN10dxeZcc4roFcWjTV9iUD+v1QMbGWUFp/ERzyLjE6jhZVQzr36q/34ixceHEdg3kX4sy+Nt36UwaGMEd50dbZ/CzE3Txw/xyQhwLd+SxNN2cIVX3HMqiPr+mR8Eq/i/8e0ZEh/Kbz1L556LdDTqZFFXUcMd7G/jfuv1cOiCClRlHuPb1NWQdqR9feUl6Hq8syWTa8N7cMKI3PTr788pNQ9lbUMbv5qSitear1EOMjA6lZ5cT/4CnJvWkrNrGn60diIv6tVL9eeUxE5AmTKmf1v8Kc50+39Smb5sNFz7aMHM3/G7Tye77P8CRTJM5i59Y/3j0WDNutDuUudRWwY//q++0qrUJoGPH13d89A0yWcIfPzQjUsRcWP/8zr3MUYMt1qnTXTPokSPMEZ39q81yFz8Ncx+E/11jShNKD5sM6PGBZu9RprOpcyeoqgS2zflpT4lekms6QA+5ybyfk/HwNEcOSqwd0hMC9Dgozm7emPlHdpt16rpj4+1vMsTOs7Q6A2+n/lNMqYfrSZTAlL54eJkTYikPE7wCePmaeur0b0xmvWsChPXF38eTwQMGkkYch1d+SK+yNEp6XcRrNyczrE8or9w0lO2HinlkVgp2DYy8j2O3LmSF/TyurfmKBephfKqP4hPZsDNtry7+/O+ekTx79WB+PHCMCf9YxsCnvmfCP5bzi49/5OUlGXy8fj+TX1rBvJSDpiPm2F+bIzdbPoKyw7ySGc6cH3P4bHM2V76yim2NnJAMpWDKP8z7O7qnyfKWFbsLmPjP5Qz580J+NWtLk8HM6bA7NB+uzeK+zVF42KvJnfc0NuXNstIe9UFL5LD6J7h0EK2qtfPGlkpq8WLByrW8u2ofOw6VnDBsbHM5HJptOcX1I281Q3FFLTM+TeGu9zfy6GepXPP6arbmnHr9LNuVzw3/WYuPpwdv3ppMkK8XN729jndW7q37/a+osfHkvDRueWc9ft6eeHkqbvjPWj7blH1a78+pvNrGoaLKU8/YSrKPVvCnuWk88/WOU56r5IQR1hpRVm0jv7Tp5dTaHXy4NouL/r6UX3y8mQOFFSfMc7S8hrs/2MjzC9LZdbiU695cw3ur9jX4791TUMZNb69HKcWnD4zmP7cOY/fhMm5/b0NdkmzLgWO8vmwP/y85kkmDzBG8qUN6MiWxBy8t3s2OQyVs3n+M1Jxi7h4bc8I5RO4dG0NBaTXzU08sSXUHTddPdABKKU/gNWAikANsVEp9pbVu5MwMbiI8HnaZAP1kHUShvpOol8eZ72cNjerCqswjeHsqxvQNO3EGnwB0tQ95KpTXl2US0cmXiQMjzGOde5kL0K8r3N/1z/yt8BEmpjzC7oH/IS53vglowuLAVoXnh1P5a/LfCF+3kbVqIAm9I+gSYNVNxk+CxU+Z4C+om+nUWZprslOTnjWnK3dm9+02KM/HI7gHt4/uw9Nf76BXF3/+fl0iqroENv3XDLUWEs29Yx18vjmHp77aTnKfEJ6cl8a8HefxeffJXF/0IddGlvH3827g5R8yOFBYzt+uS+RAYQX3friJ3KIqXrx+CNcNi2TNniP84uMfueq11bxxSzK9QwP41awUBvXsxJ+vGlS3us6PC+exyxJ4bkE6v5uzlcz8Mp69ejCNGRMbRniQD6syjxAZ4m+ONrSUs4Y6Z6Opue6eaNahw1Yf6IApQ4gcYQLGymNm1I0Lf9NwWR6eJnB4dyLMvLF+u7h8Foi+0GTWL3vOlMnsmGdea+DVENSKHVzBnBI+c7HJ+sZcXD9eddZq+PphU47hHQgTnjTBctlhsw5c9b2kfvxz1yAczA7Hjx9CYLeG5R4+gWYklKxVsOB3sOEts/Pi18UMb+d8z1HHB+hW9jF7nWnbgt+Zz3CXKLjiH9DPWpe1lSboPLLbDInY/bjPR+EeM0/EoIZlBbZqs619O0HPZFMnvOpfZmjFix5t3jqNHWfa79vJjOLhKiwO0OZIi5efCfwL90CX3qb/QWiMWc9+nU+sP3eKGmPKNkJiwL9Lw8e8fEypx/G8rZMF5W83n68gl0TBwKvMzuSBNQ0+r1PO68G3acn8jtmg4PxJN4D1JzxhQAR/unIgf/56B899u5NfjI/j5nmlZFVP5+Npf2dY/lxTvuW682lRSnHb6D5cHN+VN5ZnmtGuIoLpHxFMXLcgCkqrmTE7hUdmpbB4Zz5/ueJ2Oq19Ffv83+AF/KgTmHW/6QD78MwtXPvGap64fAB3XRCNUooam4Nlu/KZl3KI0KqbeZY3WGvrz3nVNoJ8zec7+2gFf/lmB99vzyMmPJAbhvdmXsoh5qYcIjmqC3deEMPkQRGnXQ63M7eEJ77YRkp2EWP7DqeisDu9qg6T4ojnzg+3EuS7g3H9uzJpUHemhPXDs3A3hMRgd2i+3HKQfyzcRW5xFVcFRuBfdoBn55u/1ZAAbwb36ozNrqmy2amqdVBVayc00IfJgyK4fHAPeofWDzxQXFnLnM05fLxuP3uPlOOhYHifUCYOjGDiwAiim/g9XLYrn8c+38qRshoenhBPbHggf/12J1e9tpqbR0bx28n96/9XXMzemM0TX26jf0Qw/71rBBGd/Dg/LpxHZ6fyl292siW7iBtH9OZPc9PIKqzg7gti+O3k/lTW2pn+yY/8ds5Wth8q4Q9TBtSVlx4pq2ZlRgGbso4R1y2Icf27ER0W0OBIdNrBYmZuOMC8lEOUVdsYGR3KzaOiuGxwd/y867eh1prM/DI27z9GaKAPI6JDCQk88X0cKKxgeUYBe/LLGBrVhQvjuxLqMt/h4ipeXZrBpxuzUUrhcGg+Xr+f20b34efj+hIe5AuYHZHv0g4zZ3MOa/cWMrR3F/7fsEiuTOxZlwTUWpOaU8wn6/fzdWouVTY7Y+PCuWVUFBMGRODt6YHWmqW78vnrNzvZU1DOkN5dWLargMU78rnzgmgeGh9HZ39vNmYd5ZefbOFoeQ3PXj2YnyX24NHPUnlm/g7W7S3kheuGcKyihpvfXofDoZl1/2j6dg2ib9cgXr8lmZ9/tJk739vAW7cP5zezU4kI9uWpqQMbrJu/XDWYDfuO8uvZKfTq4k+XAG/+X3KvE9bhhfHh9IsI4p1V+7g2uVfDygE3oNrTSV5aSik1Bnhaaz3Zuv8EgNb6uaaeM3z4cL1pUxuNwAAmkzfvF9xT8xsqoicx0/qRb8ziHXnc++Em3r9rRONZ7xZYkp7H3e9v4vy+YXxyXyOv+doocNi52uPfpGQXMePSfjxyaXyjy5q75SAfzZ7FJz5/hc6R+JTsNxm7G/5nAppPpqEPbkahebr2drqM/yW/utQqwdAa/jXY1KJWFZsTm4z/vRlDOGul+eOOn2SCpv1rzHxB3amNGsuXx/pwQXQnetmyTYlBbgrcv6xuuLk1mUe4+Z31hAX6UFhew28n9+cXY3uhVv4D1ryKdthI7XE9/97Xi5jwAA4XV+PtqZh+SRzx3eqHK8svqeLlHzIoLCmjV4Adait4aGwkIV06m6HivAPAXoOuKuHLdTvIzC2iXAXw6NThBAd3MSM8VBSawBYgIJTP0ytZmFHKFTEeXBWDCei8/MxwaZ16msxiXpoZwq8ww0yPGGQuxTmwdbbJejp1s36wKgrh1+kmiHNa9S+TDfbwMvW5PRIb/1DMfcjUKXfqBTO2NwwS178FC35rTgizZ4mpZQazzLiJ9dnRqmJzQZtMoZe/ufa2rj28rKE2d0J+ugkyuyaYi3+IGRZyzw/1yw8IM8GstpuguksUTHgKUmeaIN6vi6kp//VOs96ccrfCfy408//KZaxtMOvui/tMm2/4sOFji56E1f82t8dMh0l/MeshZxN8+XNzZOc36ab23ElrcyIYW42pnY44zwxPufrfZmz1gVeZoHjbZ9a6sfRIMmcjLc4xHfacY7cHdTdBZK9k87nfvdB87gGCe5js8paPIfEGuOrVxrfl8Y7uNWeW7ZEEDxw33N+hLfDWOPPdy99hstlhfU2H49ry+u0cNca0v/QwPLq74edj90L45Hqzw3bDB81rE5h1mjrTZNhdg/jaSnghznx37ltSV/NfbbPz6qz5/CbzdvPd+92+EzpJPv3Vdt5fk0X3Tn4cq6jhvTtHcEHcmZeR2ewO3ly+h5cWZ9A12Jf7A5Zz17F/U+ERSPWv9xISZM5jcKy8ht/OSWXxznwuHdCNbp38+HZbLkUVtYQG+nBRXBilWZtYUtwDX28vLh0QQWRIAP9dvQ8PZX5/7r0wBl8vT0qrTDD7wZossgorUAoigv2IDPG3LgF09vfGz8cTPy8P/H088fPy5Pgczvp9R3l35T46+XvzpysHcHVSL9TCP8LaV7GNepCVMb9m4Y7DLNqRx5GyGv7u8zY3eCxlftIbvJoVSfrhUoZEduaJKwYwes0DUHaYQzcuYu2eQtbuLSQjrxQfLw/8vD3x9fLEz9uDrMJy0g6a/hyJkZ2ZPKg72UcrmJtykKpaB8lRXZg2ojcHi6pYtCOvbrCB2K6BxIQFEtHZj+6dzOXHA8eYtTGbfhFB/OP6JM6LNOV3pVW1/GtRBh+szaKzvzcTB0QQ7OdFoK8XwX5e5Byr5P01WVwYH84btw6r2xkCE4T+Z8Ve/v5dOg4NkSH+vHDdkAYJK5vdwfML0nln1T5GxYQyMiaUZbsK2Gb1YQrw8aTCqlWPCg3g4n5diQkPZF7KQVJzivH18mBKYg9iwwP5bHMO+wsrrOAxkp5d/Nmwr5CNWcdOOIqQ0D2YkTGhDO7ZmbRDxazYXUCWlZn28fKgxuZAKUjs1ZmL+nWlrNrGx+sPoLVm2ojeTB8fT43NwctLMvjixxz8vD25bXQfCstrWLAtl/IaO1GhAVyS0I01e46wO68MHy8PJg6MILFXZ+amHGJnbgkBPp5MHdKTbp38+GxTNrnFVYQH+XLdsEi2HypmZcYRYsID+f0VA7h0QDfyS6t58ftdzPkxh5AAHyYNjOCzzTlEhvjz2s3JDO7VuW7dv7tqH88vSKd7Zz8cDk1lrZ2Z948moXunBuviu7RcHvpkCwE+npRW2fj43lGNfp+dMQ3AL8b15XeXJTT6PZ69MZvffb6VT+4dxfmt8LvQUkqpzVrr4Y0+1sED9OuAy7TW91r3bwNGaa2nHzff/YDz36A/sIuzLxxoxllRxE9MtoP7kG3hHmQ7uAfZDu5DtoV76AjboY/WutHDzh26xAVo7HjFCXskWuu3gLd++uY0TSm1qam9KHH2yHZwH7It3INsB/cg28F9yLZwDx19O3T0TqI5QG+X+5FA070NhRBCCCGEaGMdPUDfCMQrpWKUUj7AjcBPcwpJIYQQQgghWkGHLnHRWtuUUtOB7wFP4D2t9fY2blZT2rTERtSR7eA+ZFu4B9kO7kG2g/uQbeEeOvR26NCdRIUQQgghhGhvOnqJixBCCCGEEO2KBOhCCCGEEEK4EQnQ3YBS6jKl1C6lVKZS6vG2bk9HoZTKUkptU0qlKKU2WdNClVKLlFIZ1nWINV0ppV62tsFWpVSyy3LusObPUErd4TJ9mLX8TOu57nUasjailHpPKZWvlEpzmfaTr/emXuNc1cR2eFopddD6TqQopa5weewJa53uUkpNdpne6O+T1fl+vbW+P7U64qOU8rXuZ1qPR5+dd+yelFK9lVJLlVI7lVLblVKPWNPlO3GWnWRbyPfiLFJK+SmlNiilUq3t8GdreovXXWttH7ektZZLG14wnVf3ALGAD5AKDGzrdnWEC5AFhB837e/A49btx4G/WbevABZgxs4fDay3pocCe63rEOt2iPXYBmCM9ZwFwOVt/Z7d4QJcBCQDaWdzvTf1GufqpYnt8DTwaCPzDrR+e3yBGOs3yfNkv0/AbOBG6/abwIPW7V8Ab1q3bwQ+bet10cbboQeQbN0OBnZb61u+E+6zLeR7cXa3gwKCrNvewHrrs96iddea28cdL5JBb3sjgUyt9V6tdQ0wC7iqjdvUkV0FOM89/gFwtcv0D7WxDuiilOoBTAYWaa2Paq2PAYuAy6zHOmmt12rzTf/QZVnnNK31CuDocZPPxnpv6jXOSU1sh6ZcBczSWldrrfcBmZjfpkZ/n6wM7SXAHOv5x29T53aYA0xwZnTPRVrrXK31j9btUmAn0Av5Tpx1J9kWTZHvxU/A+myXWXe9rYum5euuNbeP25EAve31ArJd7udw8h8M0XwaWKiU2qyUut+aFqG1zgXzYw10s6Y3tR1ONj2nkemicWdjvTf1GqKh6VbpxHsuJQ8t3Q5hQJHW2nbc9AbLsh4vtuY/51mH5odiMobynWhDx20LkO/FWaWU8lRKpQD5mJ3NPbR83bXm9nE7EqC3vcb2oGXsy9ZxgdY6GbgceEgpddFJ5m1qO7R0umgZWe9n1xtAXyAJyAX+YU1vze0g26gRSqkg4HPgV1rrkpPN2sg0+U60oka2hXwvzjKttV1rnYQ5w/tIYEBjs1nXrbUd2tU2kAC97eUAvV3uRwKH2qgtHYrW+pB1nQ98ifkRyLMOCWNd51uzN7UdTjY9spHponFnY7039RrCorXOs/4YHcDbmO8EtHw7HMGUXngdN73BsqzHO9P8UpsOSSnljQkIP9Zaf2FNlu9EG2hsW8j3ou1orYuAZZga9Jauu9bcPm5HAvS2txGIt3oW+2A6QHzVxm1q95RSgUqpYOdtYBKQhlm3ztEP7gDmWbe/Am5Xxmig2Dok/D0wSSkVYh32nAR8bz1WqpQabdW13e6yLHGis7Hem3oNYXEGa5ZrMN8JMOvuRmu0hBggHtPxsNHfJ6vWeSlwnfX847epcztcByyx5j8nWZ/Td4GdWut/ujwk34mzrKltId+Ls0sp1VUp1cW67Q9ciukP0NJ115rbx/20Vm9TuZz+BdNrfzemBusPbd2ejnDB9N5OtS7bnesVU4P2A5BhXYda0xXwmrUNtgHDXZZ1N6bzSSZwl8v04Zgf8j3Aq1hn5j3XL8BMzGHiWkwm456zsd6beo1z9dLEdviftZ63Yv7cerjM/wdrne7CZUSipn6frO/YBmv7fAb4WtP9rPuZ1uOxbb0u2ng7jMUcRt8KpFiXK+Q74VbbQr4XZ3c7JAJbrPWdBjx5uuuutbaPO16cX2IhhBBCCCGEG5ASFyGEEEIIIdyIBOhCCCGEEEK4EQnQhRBCCCGEcCMSoAshhBBCCOFGJEAXQgghhBDCjUiALoQQQgghhBuRAF0IIToApVQXpdQvTjFPtFLq5mYsa6ZSaqtSasZptKOspc9p6nnNba8QQnQ0EqALIUTH0AU4aYAORAMnDXiVUt2B87XWiVrrf7VS205XNKdorxBCdEQSoAshRMfwPNBXKZWilHrBuqQppbYppaa5zHOhNU9T2fGFQDdrnouVUpsBlFJDlFJaKRVl3d+jlAqwTqe9Vim1USn17MkaqJQap5RaoZT6Uim1Qyn1plLK47h5wq3lTTm+vUqpQUqpDdb9rUqp+DNYX0II4ba82roBQgghWsXjwGCtdZJS6v8BPweGAOHARqXUCmueR7XWV55kOVOB+VrrJACllJ9SqhNwIbAJEzCvAvK11hVKqX8Db2itP1RKPdSMdo4EBgL7ge+Aa4E51mtFYE61/ket9SKlVLlre5VSrwD/1lp/rJTyATxbsH6EEKLdkAy6EEJ0PGOBmVpru9Y6D1gOjDjNZa0BLgAuAv7Pur4QWGk9fgEw07r9v2Ysb4PWeq/W2m49b6w13Rv4Afid1npRE89dC/xeKfUY0EdrXdnSNyOEEO2BBOhCCNHxqFZc1kpMQN4HmIfJyo8FVrjMo1uwvOPndd63AZuByU0+UetPMBn+SuB7pdQlLXhdIYRoNyRAF0KIjqEUCLZurwCmKaU8lVJdMVnvDcfN01wrgFuBDK21AzgKXAGsth5fDdxo3b6lGcsbadWtewDTgFXWdA3cDSQopR5v5D2hlIoF9mqtX8aUwiS28L0IIUS7IAG6EEJ0AFrrQmC1UioNGANsBVKBJZiykcPWNJtSKrW5QyhqrbOsm86M+SqgSGt9zLr/CPCQUmoj0LkZi1yL6fyZBuwDvnR5LTsm2B9vDRl5fHunAWlKqRQgAfiwOe9BCCHaG6V1S45MCiGEEKdHKTWOU3dSFUKIc55k0IUQQgghhHAjkkEXQohzkFJqMvC34ybv01pf0wrLPo8TR3Sp1lqPOtNlCyHEuUACdCGEEEIIIdyIlLgIIYQQQgjhRiRAF0IIIYQQwo1IgC6EEEIIIYQbkQBdCCGEEEIINyIBuhBCCCGEEG5EAnQhhBBCCCHciAToQgghhBBCuBEJ0IUQQgghhHAjEqALIYQQQgjhRiRAF0IIIYQQwo1IgC6EEEIIIYQbkQBdCCGEEEIINyIBuhBCCCGEEG5EAnQhhBBCCCHciAToQgghhBBCuBEJ0IUQQgghhHAjEqALIYQQQgjhRiRAF0IIIYQQwo1IgC6EEEIIIYQbkQC9EUqp95RS+UqptFZa3t+VUtuVUjuVUi8rpVRrLFcIIYQQQnQ8EqA37n3gstZYkFLqfOACIBEYDIwALm6NZQshhBBCiI5HAvRGaK1XAEddpyml+iqlvlNKbVZKrVRKJTR3cYAf4AP4At5AXqs2WAghhBBCdBgSoDffW8AvtdbDgEeB15vzJK31WmApkGtdvtda7/zJWimEEEIIIdo1r7ZuQHuglAoCzgc+cykf97UeuxZ4ppGnHdRaT1ZKxQEDgEhr+iKl1EVWll4IIYQQQogGJEBvHg+gSGuddPwDWusvgC9O8txrgHVa6zIApdQCYDQgAboQQgghhDiBlLg0g9a6BNinlLoeQBlDmvn0A8DFSikvpZQ3poOolLgIIYQQQohGSYDeCKXUTGAt0F8plaOUuge4BbhHKZUKbAeuaubi5gB7gG1AKpCqtf76J2i2EEIIIYToAJTWuq3bIIQQQgghhLBIBl0IIYQQQgg3Ip1EjxMeHq6jo6PbuhlCCCGEEKID27x58xGtddfGHpMA/TjR0dFs2rSprZshhBBCCCE6MKXU/qYekxIXIYQQQggh3IgE6EIIIYQQQrgRCdCFEEIIIYRwI1KDLoQQQgjRAdXW1pKTk0NVVVVbN+Wc5ufnR2RkJN7e3s1+jgToQgghhBAdUE5ODsHBwURHR6OUauvmnJO01hQWFpKTk0NMTEyznyclLkIIIYQQHVBVVRVhYWESnLchpRRhYWEtPoohAboQQgghRAclwXnbO51tIAG6EEIIIYQQbkQCdCGEEEII8ZPw9PQkKSmJIUOGkJyczJo1a057WU8++SSLFy8+4zZVVFQwZcoUEhISGDRoEI8//njdY9XV1UybNo24uDhGjRpFVlYWAIWFhYwfP56goCCmT5/eYHkzZ87kvPPOIzExkcsuu4wjR46ccRslQBdCCCGEED8Jf39/UlJSSE1N5bnnnuOJJ5447WU988wzXHrppa3SrkcffZT09HS2bNnC6tWrWbBgAQDvvvsuISEhZGZmMmPGDB577DHAjMTy7LPP8uKLLzZYjs1m45FHHmHp0qVs3bqVxMREXn311TNunwToQgghhBDiJ1dSUkJISEjd/RdeeIERI0aQmJjIU089BUBWVhYDBgzgvvvuY9CgQUyaNInKykoA7rzzTubMmQPAt99+S0JCAmPHjuXhhx/myiuvBODpp5/m7rvvZty4ccTGxvLyyy+f0I6AgADGjx8PgI+PD8nJyeTk5AAwb9487rjjDgCuu+46fvjhB7TWBAYGMnbsWPz8/BosS2uN1pry8nK01pSUlNCzZ88zXlcyzKIQQgghRAf356+3s+NQSasuc2DPTjz1s0EnnaeyspKkpCSqqqrIzc1lyZIlACxcuJCMjAw2bNiA1pqpU6eyYsUKoqKiyMjIYObMmbz99tvccMMNfP7559x66611y6yqquKBBx5gxYoVxMTEcNNNN+lEUr0AACAASURBVDV4zfT0dJYuXUppaSn9+/fnwQcfbHIM8qKiIr7++mseeeQRAA4ePEjv3r0B8PLyonPnzhQWFhIeHt7o8729vXnjjTc477zzCAwMJD4+ntdee615K/AkJIPeDjgcGodDt3UzhBBCCCFaxFnikp6eznfffcftt9+O1pqFCxeycOFChg4dSnJyMunp6WRkZAAQExNDUlISAMOGDaurA3dKT08nNja2blzx4wP0KVOm4OvrS3h4ON26dSMvL6/RttlsNm666SYefvhhYmNjAZMRP97JRmGpra3ljTfeYMuWLRw6dIjExESee+655q2ck5AMejvw7Dc7yDpSzn/vGtnWTRFCCCFEO3SqTPfZMGbMGI4cOUJBQQFaa5544gkeeOCBBvNkZWXh6+tbd9/T07OuxMWpsSDa1fHPt9lsjc53//33Ex8fz69+9au6aZGRkWRnZxMZGYnNZqO4uJjQ0NAmXyslJQWAvn37AnDDDTfw/PPPn7R9zSEZ9HYg+2glOccqTz2jEEIIIYSbSk9Px263ExYWxuTJk3nvvfcoKysDTGlJfn5+s5aTkJDA3r176zLrn376aYvb8sc//pHi4mJeeumlBtOnTp3KBx98AMCcOXO45JJLTppB79WrFzt27KCgoACARYsWMWDAgBa353iSQW8H7A4HjlPsLQohhBBCuBtnDTqYzPcHH3yAp6cnkyZNYufOnYwZMwaAoKAgPvroIzw9PU+5TH9/f15//XUuu+wywsPDGTmyZRUGOTk5/PWvfyUhIYHk5GQApk+fzr333ss999zDbbfdRlxcHKGhocyaNavuedHR0ZSUlFBTU8PcuXNZuHAhAwcO5KmnnuKiiy7C29ubPn368P7777eoPY1RpzpMcK4ZPny43rRpU1s3o4Hb3l3PwWOVLHl0XFs3RQghhBDtxM6dO1slm+uOysrKCAoKQmvNQw89RHx8PDNmzGjrZjWpsW2hlNqstR7e2PxS4tIO2B0au+xICSGEEEIA8Pbbb5OUlMSgQYMoLi4+oZa9vZMSl3bA7tBS4iKEEEIIYZkxY4ZbZ8zPlGTQ2wG7Q+NwtHUrhBBCCCHE2SABejtgkwy6EEIIIcQ5QwL0dkBKXIQQQgghzh0dPkBXSs1QSm1XSqUppWYqpfzauk0tZXNo7FLiIoQQQghxTujQAbpSqhfwMDBcaz0Y8ARubNtWtZzd4TjlWbOEEEIIIdyNp6cnSUlJDBkyhOTkZNasWXPay3ryySdZvHjxGbepoqKCKVOmkJCQwKBBg3j88cfrHquurmbatGnExcUxatSoupMhFRYWMn78eIKCgpg+fXrd/KWlpSQlJdVdwsPDG5yZ9HSdC6O4eAH+SqlaIAA41MbtaTGpQRdCCCFEe+Tv709KSgoA33//PU888QTLly8/rWU988wzrdauRx99lPHjx1NTU8OECRNYsGABl19+Oe+++y4hISFkZmYya9YsHnvsMT799FP8/Px49tlnSUtLIy0trW45wcHBde8PYNiwYVx77bVn3L4OnUHXWh8EXgQOALlAsdZ6Ydu2quXsDo3dIQG6EEIIIdqvkpISQkJC6u6/8MILjBgxgsTERJ566ikAsrKyGDBgAPfddx+DBg1i0qRJVFZWAnDnnXcyZ84cAL799lsSEhIYO3YsDz/8MFdeeSUATz/9NHfffTfjxo0jNjaWl19++YR2BAQEMH78eAB8fHxITk4mJycHgHnz5nHHHXcAcN111/HDDz+gtSYwMJCxY8fi59d0pXRGRgb5+flceOGFZ7qqOnYGXSkVAlwFxABFwGdKqVu11h8dN9/9wP0AUVFRZ72dp2J3aCSBLoQQQojTtuBxOLytdZfZ/Ty4/PmTzlJZWUlSUhJVVVXk5uayZMkSABYuXEhGRgYbNmxAa83UqVNZsWIFUVFRZGRkMHPmTN5++21uuOEGPv/8c2699da6ZVZVVfHAAw+wYsUKYmJiuOmmmxq8Znp6OkuXLqW0tJT+/fvz4IMP4u3t3Wj7ioqK+Prrr3nkkUcAOHjwIL179wbAy8uLzp07U1hYSHh4+ClXx8yZM5k2bRpKqVPOeyodOoMOXArs01oXaK1rgS+A84+fSWv9ltZ6uNZ6eNeuXc96I09FRnERQgghRHvkLHFJT0/nu+++4/bbb0drzcKFC1m4cCFDhw4lOTmZ9PR0MjIyAIiJiSEpKQkwJSPOOnCn9PR0YmNjiYmJATghQJ8yZQq+vr6Eh4fTrVs38vLyGm2bzWbjpptu4uGHHyY2Nhag0T5/zQ24Z82adUJbTleHzqBjSltGK6UCgEpgArCpbZvUcjaHxi4BuhBCCCFO1yky3WfDmDFjOHLkCAUFBWiteeKJJ3jggQcazJOVlYWvr2/dfU9Pz7oSF6dTDZxx/PNtNluj891///3Ex8c36NQZGRlJdnY2kZGR2Gw2iouLCQ0NPeV7S01NxWazMWzYsFPO2xwdOoOutV4PzAF+BLZh3u9bbdqo02Ay6G3dCiGEEEKI05eeno7dbicsLIzJkyfz3nvvUVZWBpjSkvz8/GYtJyEhgb1799Zl1j/99NMWt+WPf/wjxcXFvPTSSw2mT506lQ8++ACAOXPmcMkllzQrgz5z5sxWy55Dx8+go7V+CniqrdtxJmx2GWZRCCGEEO2PswYdTOb7gw8+wNPTk0mTJrFz507GjBkDQFBQEB999BGenp6nXKa/vz+vv/46l112GeHh4YwcObJFbcrJyeGvf/0rCQkJJCcnAzB9+nTuvfde7rnnHm677Tbi4uIIDQ1l1qxZdc+Ljo6mpKSEmpoa5s6dy8KFCxk4cCAAs2fP5ttvv21RO05GSeDX0PDhw/WmTe5VBTPoye+orLWz97kpbd0UIYQQQrQTO3fuZMCAAW3djJ9EWVkZQUFBaK156KGHiI+PZ8aMGW3drCY1ti2UUpu11sMbm79Dl7h0FDYpcRFCCCGEqPP222+TlJTEoEGDKC4uPqGWvb3r8CUuHYFzDHStdasM3SOEEEII0Z7NmDHDrTPmZ0oy6O2AcwQXOVmREEIIIVpCSpnb3ulsAwnQ3ZzD5SRFEp8LIYQQorn8/PwoLCyUIL0Naa0pLCw86RlIGyMlLm7O5hKVy8mKhBBCCNFckZGR5OTkUFBQ0NZNOaf5+fkRGRnZoudIgO7m7BKgCyGEEOI0eHt7151tU7QvUuLi5mwOR91tKXERQgghhOj4JEB3c64ZdOkkKoQQQgjR8UmA7uZca9Clk4cQQgghRMcnAbqbczSoQW/DhgghhBBCiLNCAnQ3Z5MSFyGEEEKIc4oE6G7OLiUuQgghhBDnFAnQ3ZxNSlyEEEIIIc4pEqC7ObvLMIt2yaALIYQQQnR4EqC7uQYZdEmhCyGEEEJ0eBKguzmb3bUGvQ0bIoQQQgghzgoJ0N1cgxMVSYQuhBBCCNHhSYDu5lyDcocE6EIIIYQQHZ4E6G5OhlkUQgghhDi3SIDu5lxr0O2Ok8wohBBCCCE6BAnQ3ZzdISUuQgghhBDnEgnQ3ZzNZRx0CdCFEEIIITo+CdDdXIMMupS4CCGEEEJ0eBKguzmblLgIIYQQQpxTJEB3czIOuhBCCCHEuUUCdDcnwywKIYQQQpxbJEB3cw1HcWnDhgghhBBCiLOiQwfoSqn+SqkUl0uJUupXbd2ulnCtQbdLhC6EEEII0eF5tXUDfkpa611AEoBSyhM4CHzZpo1qIbsMsyiEEEIIcU7p0Bn040wA9mit97d1Q1rC1qAGvQ0bIoQQQgghzopzKUC/EZjZ2ANKqfuVUpuUUpsKCgrOcrNOzi4lLkIIIYQQ55RzIkBXSvkAU4HPGntca/2W1nq41np4165dz27jTsFml3HQhRBCCCHOJedEgA5cDvyotc5r64a0lF1KXIQQQgghzinnSoB+E02Ut7g715MTSYmLEEIIIUTH1+EDdKVUADAR+KKt23I6Go6DLgG6EEIIIURH16GHWQTQWlcAYW3djtPVsAa9DRsihBBCCCHOig6fQW/vZBx0IYQQQohziwTobs4mJS5CCCGEEOcUCdDdXMMa9DZsiBBCCCGEOCskQHdzDTLoEqELIYQQQnR4EqC7ORnFRQghhBDi3CIBuptzDdBlHHQhhPj/7N15nF1lnefxz3PvrSVLZQ9bEkggASQQQgygBGiDPRC1G1BoAW2hFYdx0BF1GERGbUdbG21asXFhEBzcCNg4CDLiwmIDUcRAQoAAEkiAsGWvVFLbXZ7545yqVFWqkkokVSe3Pu/X677q3nPP8tS9p25966nfeR5Jqn4G9IwrOZOoJEnSkGJAzziHWZQkSRpaDOgZV6pEavPJ21Q2oEuSJFU9A3rGlSuRmnwAHGZRkiRpKDCgZ1ypEqktJG9TtAddkiSp6hnQM65cjtR0lLjYhS5JklT1DOgZV47betDN55IkSdXPgJ5xSQ26JS6SJElDhQE940pdLhK1xEWSJKn6GdAzrlypWOIiSZI0hBjQM67U5SJRJyqSJEmqfgb0jOtag16xC12SJKnqGdAzrlSJ1FniIkmSNGQY0DOuXIkUch0ziZrQJUmSqp0BPePKlUg+lyMEA7okSdJQYEDPuI4e9HwIBnRJkqQhwICecaVKhXw+kAuBcmWwWyNJkqQ9zYCecR096CE4k6gkSdJQYEDPuFIlks8F8jlLXCRJkoYCA3rGdfSgW+IiSZI0NBjQM66UjuKScxQXSZKkIaHqA3oIYUwI4dYQwtMhhKdCCG8d7Dbtis4e9FywBl2SJGkIKAx2AwbAN4FfxRjPDiHUAsMHu0G7opzWoOdCoGxAlyRJqnpVHdBDCKOAk4F/AIgxtgPtg9mmXdU1oFfM55IkSVWv2ktcDgbWAv8nhLAkhHB9CGHEYDdqV5QqlfQiUYdZlCRJGgqqPaAXgDnAd2OMxwBbgct7rhRCuCiEsDiEsHjt2rUD3cYd6lbiYhe6JElS1av2gL4aWB1j/GP6+FaSwN5NjPG6GOPcGOPciRMnDmgDd6aUXiSajIM+2K2RJEnSnlbVAT3G+BrwUgjhsHTR24Hlg9ikXVKpRGKEfC5HcJhFSZKkIaGqLxJN/TfgJ+kILs8DHxzk9vRbKe0yL+TTi0TtQpckSap6VR/QY4xLgbmD3Y7d0VFznrfERZIkacio6hKXvV3HuOf5ECxxkSRJGiIM6BlWLm/rQU/GQTegS5IkVTsDeoaVKhUgqUHPh0D6UJIkSVXMgJ5hXWvQQ9hW8iJJkqTqZUDPsM5RXNISF2cSlSRJqn4G9Azb1oOecxQXSZKkIcKAnmHde9C3BXZJkiRVLwN6hnUE8lwukMs5ioskSdJQYEDPsPJ2NeiD3CBJkiTtcQb0DOsYZjFviYskSdKQYUDPsJ496Ja4SJIkVT8DeoaVKt1nEjWfS5IkVT8DeoZt60HPkcs5UZEkSdJQYEDPsFK5ew+6JS6SJEnVz4CeYZ096PmOgD7IDZIkSdIeZ0DPsI6SllxIRnGpmNAlSZKqngE9w8rpMIuFXCDvREWSJElDggE9w7rWoAdLXCRJkoYEA3qGda9Bt8RFkiRpKDCgZ1ipy0RFlrhIkiQNDQb0DCt3TlSUI4TgOOiSJElDgAE9w7r2oDuTqCRJ0tBgQM+wjlFc8rlAPmCJiyRJ0hBgQM+wcpLPO2cSLXuRqCRJUtUzoGdY1x70XM4SF0mSpKHAgJ5h3WvQLXGRJEkaCgzoGbZtFBdLXCRJkoYKA3qGbetBz5HLOZOoJEnSUFAY7AbsaSGEVUATUAZKMca5g9ui/uvegw7REhdJkqSqV/UBPTU/xrhusBuxq0rl7uOgO1GRJElS9bPEJcM6RnHJpQG9Yo2LJElS1RsKAT0CvwkhPBJCuKi3FUIIF4UQFocQFq9du3aAm9e3cowUcgHAmUQlSZKGiKEQ0OfFGOcA7wA+GkI4uecKMcbrYoxzY4xzJ06cOPAt7EOpEsl3BnQscZEkSRoCqj6gxxhfSb+uAW4DjhvcFvVfubytBz2fC46DLkmSNARUdUAPIYwIITR03AdOBZ4Y3Fb1X9ce9BAcZlGSJGkoqPZRXPYFbgshQPK93hRj/NXgNqn/ypVIIZ/8DZULeJGoJEnSEFDVAT3G+Dxw9GC3Y3d17UG3xEWSJGloqOoSl71duVLprEHvKHFxsiJJkqTqZkDPsFIlkgvbRnEBHGpRkiSpyhnQM6xSiRTyaYlLGtQtc5EkSapuBvQM6zYOevrVsdAlSZKqmwE9w8qV7jOJgiUukiRJ1c6AnmFJD/q2YRbBEhdJkqRqZ0DPsN560MuOhS5JklTVDOgZ1lsNuvlckiSpuhnQM6xcqWwL6J3DLJrQJUmSqpkBPcPKXXvQLXGRJEkaEgzoGdatBt0SF0mSpCHBgJ5h3WrQLXGRJEkaEgzoGdbrKC4GdEmSpKpmQM+wUnnbOOj5YImLJEnSUGBAz7CuPeihY6IiE7okSVJVM6BnWKlSIZ/vXuLiTKKSJEnVzYCeYeVK7CxtyTuKiyRJ0pBgQM+wcty+xMVx0CVJkqqbAT3DyuVtwyx2fHWYRUmSpOpmQM+wUiVS2K4GfTBbJEmSpD3NgJ5h5V4mKrLERZIkqboZ0DOsVIkU0nHQHcVFkiRpaDCgZ1j3HvSOGvTBbJEkSZL2NAN6hpUqlc5RXNKOdMomdEmSpKpmQM+wciWSyzlRkSRJ0lBiQM+wcmXbOOjbSlwM6JIkSdXMgJ5RlUqkEtmuBr1cGcxWSZIkaU8zoGdUR615zxp0S1wkSZKq25AI6CGEfAhhSQjhzsFuS391jHeed5hFSZKkIWVIBHTgEuCpwW7ErihVevSgdwR0S1wkSZKqWtUH9BDCZOBdwPWD3ZZdUS539KCH9Guy3B50SZKk6lb1AR24GrgM6LPvOYRwUQhhcQhh8dq1aweuZTtQSrvKOwJ6sMRFkiRpSKjqgB5C+BtgTYzxkR2tF2O8LsY4N8Y4d+LEiQPUuh3bVoPuOOiSJElDSVUHdGAecHoIYRVwM3BKCOHHg9uk/uk5ikveGnRJkqQhoaoDeozxMzHGyTHGqcC5wL0xxr8f5Gb1S6lHDXqazzuDuyRJkqpTVQf0vVlHiUsh33GRqDOJSpIkDQWFwW7AQIkx/g743SA3o99KfY6DPmhNkiRJ0gCwBz2jytuNg959uSRJkqqTAT2jeg6zmMs5ioskSdJQYEDPqM5hFkP3YRbN55IkSdXNgJ5RnQE9b4mLJEnSUGJAz6jta9AtcZEkSRoKDOgZVeo5k2jOEhdJkqShwICeUdt60DuGWUyXm9AlSZKqmgE9o3r2oOctcZEkSRoSDOgZVU6HWeyoQQ9OVCRJkjQkGNAzqlTuUYOelrhUTOiSJElVzYCeUeWeJS5OVCRJkjQkGNAzqpgG9Jq8JS6SJElDiQE9o0rljhr07qO4WOIiSZJU3QzoGVVMA3pNIXmLLHGRJEkaGgzoGVVMLxKt6TGTqOOgS5IkVTcDekZ1lrjkO0pcnElUkiRpKDCgZ1THREWFvMMsSpIkDSUG9IxqT3vQa3v0oFviIkmSVN0M6BnVMVFRx0yiuZzDLEqSJA0FBvSM6qhB7xi9BZIyl2gPuiRJUlUzoGdUsRKpyYfOCYogKXMp24UuSZJU1QzoGVUqV6jJd397crlgiYskSVKVM6BnVLEcO+vPO1jiIkmSVP0M6BlV7K0H3RIXSZKkqmdAz6hSOXaOgd4hHyxxkSRJqnYG9IwqVioUct3fnhCgYomLJElSVTOgZ1SxHKkt9HaRqAFdkiSpmlV1QA8h1IcQHg4hPBZCeDKE8L8Gu039VSpXtrtINClxMaBLkiRVs8JgN2APawNOiTFuCSHUAA+GEO6KMT402A3bmWI5Usj3LHGxBl2SJKnaVXVAj8mYhFvShzXpba+IuKVKhZr89sMsVkzokiRJVa2qS1wAQgj5EMJSYA3w2xjjHwe7Tf3R2zCLeWvQJUmSql7VB/QYYznGOBuYDBwXQjiy5zohhItCCItDCIvXrl078I3sRe8TFQXKlUFqkCRJkgZE1Qf0DjHGTcDvgAW9PHddjHFujHHuxIkTB7xtvSn1NlFRzplEJUmSql1VB/QQwsQQwpj0/jDgr4GnB7dV/VOqbD9RUc5RXCRJkqpeVV8kCuwP/CCEkCf5Y+SnMcY7B7lN/ZKUuPToQQ+BsvlckiSpqlV1QI8xLgOOGex27I5iuUJtoZdRXOxBlyRJqmpVXeKyN0smKtq+B90adEmSpOpmQM+oZKKi3kZxMaBLkiRVMwN6RpUqFWp69qDnnElUkiSp2hnQM6pYjtT0UoNuiYskSVJ1M6BnVLGPGnRLXCRJkqqbAT2jSuVITc8adEtcJEmSqp4BPaNKlQqFnjOJOsyiJElS1TOgZ1CMMalB3y6gO5OoJElStTOgZ1AprWOpyXUvccmHQKUyGC2SJEnSQDGgZ1CpnAT0niUuwRIXSZKkqmdAz6Bi2k3e8yLRfM4SF0mSpGpnQM+gzh703PYziTqKiyRJUnUzoGdQsZz2oBe2L3FxHHRJkqTqZkDPoM6A3mOionwuOJOoJElSlTOgZ9C2i0QtcZEkSRpqDOgZVEovEu1toiJLXCRJkqqbAT2D2ktJCK/ttQfdgC5JklTNDOgZ1NmDntt+JlHzuSRJUnUzoGdQsa8a9ByUTeiSJElVzYCeQaWOUVy2q0G3xEWSJKnaGdAzqKMHvbeAbj6XJEmqbgb0DCp2juLS8yJRR3GRJEmqdgb0DOoYB73nREW5nCUukiRJ1c6AnkEdNei9TVRkPpckSapuBvQMKlY6atAtcZEkSRpqDOgZVCz1PopL3hIXSZKkqmdAz6DOiYp6BPQQAnagS5IkVTcDegZ1DrOY617iknccdEmSpKpX1QE9hDAlhHBfCOGpEMKTIYRLBrtN/bHtItGe46BjQJckSapyhcFuwB5WAv57jPHREEID8EgI4bcxxuWD3bAd2TZRUfce9BCCF4lKkiRVuaruQY8xvhpjfDS93wQ8BUwa3FbtXMdERb1dJGoHuiRJUnWr6oDeVQhhKnAM8MdenrsohLA4hLB47dq1A9207XRMVFTIbT/MoiUukiRJ1W1IBPQQwkjgZ8AnYoybez4fY7wuxjg3xjh34sSJA9/AHjpq0PPbBXRLXCRJkqpd1Qf0EEINSTj/SYzx/w52e/qjWInU5AMh9AjolrhIkiRVvaoO6CFJuDcAT8UYvz7Y7emvYqmyXf05WOIiSZI0FFR1QAfmAR8ATgkhLE1v7xzsRu1MqRK3qz+HtMTFgC5JklTVqnqYxRjjg8D2STfjiuW+etCTEpcY43blL5IkSaoO1d6DvlcqlSOFfO896IB16JIkSVXMgJ5BffegJ18tc5EkSapeBvQMSkZx6SWgpwndC0UlSZKqlwE9g0rlSp8XiUJS4lIsV3h8deNAN02SJEl7mAE9g4rlSGFHJS6VyP9b9ipnfPtB1ja1DXDrJEmStCcZ0DOoWK5Q28tFovkuJS5rmlqpRGhsaR/o5kmSJGkPMqBnUKlS6bUHvWNoxUqELa0lAJrbywPaNkmSJO1ZBvQMKpZ7n6ioo1O9UolsNqBLkiRVJQN6BpX6GmaxS4lLUxrQW4oGdEmSpGpiQM+gUqX3iYq6lrg0tRYBaLEHXZIkqaoY0DOovdR7D3o+bOtB39JmiYskSVI1MqBnUKkSqemlB72jLL1biUt7aSCbJkmSpD3MgJ5ByURFvY2DniT0ciV2lrjYgy5JklRdDOgZlExU1EsPem7bTKJeJCpJklSdDOgZlExU1PdMot1LXAzokiRJ1cSAnkF9jeLSUeLSUizTXq4AlrhIkiRVGwN6BhX7qkFPu9Abm4udywzokiRJ1cWAnkGl8o5HcWls2RbQW4qO4iJJklRNDOgZVOxrJtG0xKVbQLcHXZIkqaoY0DMmxpjWoO88oOfC7pe4rFq3lZ8ufmn3GypJkqQ9woCeMaVKBKAm13eJy+Y0oE8YWbfbwywu/NOLXHbrMkrpxaaSJEnKBgN6xpTKSUDvTw/6vqPqd7sHfePWdgC2tFnDLkmSlCUG9IwpVpIe7d4uEs2nXeibOgN63W7XoG9MR4LpGE9dkiRJ2WBAz5hiqSOgb//WhB6juExs2P0Sl03N7d32JUmSpGwwoGdMRw16bxMVdfSgN7YUGVaTp6G+hub23esB32QPuiRJUiYZ0DOmmF60WdPbREVdatBH1hcYVpOntVihkob6XbGtxMUedEmSpCwxoGfMtotEt+9BD11GcWmoLzC8Ng+wy2UuMcbOEhd70CVJkrKlqgN6COH7IYQ1IYQnBrst/dXZg95LDXq+Sw96Q33Nbgf0LW2lzlKazfagS5IkZUpVB3TgRmDBYDdiVxTTHvTeRnHJpTXoxXJkVH2BYbUFYNdnE+2oPwd70CVJkrKmqgN6jPF+YMNgt2NXlNJhFgu91qBvuz+yLqlBh12fTbR7QLcHXZIkKUuqOqDvjYo7qEHvuEgU6FaDvqsjuWxM688BNrfYgy5JkpQlBnQghHBRCGFxCGHx2rVrB7UtO6pB7x7QaxjWUYO+iz3oHQE9nws0tdmDLkmSlCUGdCDGeF2McW6Mce7EiRMHtS2lzhr0nQX03R/FpWNyokljhlmDLkmSlDEG9IwpdtSg93qR6Lb7I+sKDM+XOSt3Py2tLbt0jI1bk4A+ZdwwNhvQJUmSMqWqA3oIYSHwB+CwEMLqEMKFg92mnensQd/BREUAo+pr2Gfpt/nX2mvZd+Xtu3SMjc3tNNQVGDO8lqYWS1wkSZKypDDYDdiTYoznDXYbdlWpvIMe9C4Bfd/iSzQsvgaA/V/+NXBJv4+xqbmdMSNqGFVfYw+6JElSxlR1D/reqH1HExV1LorMWvZPUKjj1vLJ7L/hIWju/2iSm1qKjB1ey6j6gsMsSpIkZYwBPWNKO5ioKKQ96Kfn/sDY1xbB2/+RG8unkY9lePrO8clTHQAAIABJREFUfh9jY3OR0cNqaKgv0Faq0FbatYtMJUmStOcY0DOmc6Ki3nrQQ2AUW/lszY9p3edowtwPsbJwCBvqJsGTP+/3MTY1tzN2eC0N9TWAs4lKkiRliQE9Y4qdF4n2XoP+3ws/ZTyNNJ/6L5DLM6y2wLLR8+H53/W7zGXj1nbGDq9h1LDkEgQDuiRJUnYY0DNmRxMV1a1Zygfyd/PD8qkMP2guAMNq8ywe8VcQy/DUL3a6/3Ilsrm1xJjhtTTUdfSgW4cuSZKUFQb0jOmoQd9uFJcYGfu7y1nLaL5ZeS91heStG15T4NkwDcYdDE/ettP9d0xSNGZ4UoMOsLnFHnRJkqSsMKBnTMdERdv1oG96kdrXH+O7pdMJ9aM6LxgdVpunpRRh5rth5f2wdd0O97+xuR2gRw26PeiSJElZYUDPmM4e9J416K8tA2BJZXpnsAYYXpunpb2UBPR+lLlsSgP6GGvQJUmSMsmAnjEdNej5ngH91WXEkOOZOKWzNAVgWE2e5vYy7HskjJ++0zKXTc1Jb3nXHvTN9qBLkiRlhgE9Y4rlSG0+11nC0um1ZZTHzaCVuu4BvTZPS3sZQoAjzoRVD8CWtX3uf2Pzthr0kXVpDbo96JIkSZlhQM+YUrmy/QWiAK8uo7LvUQCMrOte4tLcnk40NPPdECvw1B197n9biUst+Vygoc7ZRCVJkrLEgJ4xpUrcvv586zpoeqUzoI/q0oM+vLZASzEN6PvOhPEzdljmsrG5nXwudO6job5gDbokSVKGGNAzpliubD+CS3qBaNxvFkC3Epf6mrTEBWhqK/G9DUcTX1gE65/rdf8bm4uMGVbTWULTUF/D5hZ70CVJkrLCgJ4xvQb0VzsCetKD3nMUl/ZyhVK5wp9f38J1LfMphRr4j6/1uv/G5iKjh2/b3h50SZKkbDGgZ0ypHLevQX9tGYw+kLqGCRw4bjiH79/Q+dTw2jwAzcUyL6zfylrGct+oM2HZLbDm6e32v7G5nbHDazsfjxpWQ1ObPeiSJElZYUDPmGIl9t6Dvv8s8rnA/ZfN529mHdD51LA0oLe0l1m1bisA11f+FmpHwO++st3+NzYXGdujB92ZRCVJkrLDgJ4xpXKl+0WibVtg/QpIy1t6Gt41oK9vBuDxjTXEt/xXWH47vPpYt/U3NbczpksPelLiYg+6JElSVhjQM2a7GvTXnwQipBeI9jSsJi1xaS+zan3Sg95SLLP2yP8M9WPgvq/w0PPrWbRiHZBMVDRmWNce9BqaWkvEGPfMNyRJkqRdUtj5KhpIxXKkpmsNejqCC/v3EdBrk7ewpVhi5bqtTJswgpXrtvL8lgL7zPs43PNFvr/qp/xm84F8aN40Woplxo7oUoNeX0OpEmktVjrLZSRJkjR47EHPmFKlQqFrD/qrj8GwcTBqUq/rd5S4rN7YQlNrib86dCJAUo9+/EeoDJ/AB1p+zMETR/D9RSuBZBbRDh1DNm62zEWSJCkTDOgZUyz3mKjoteQCUUIvs4uyrcTlqVebADjhkPHU5ENSj147gucOu4iT8k9wzVu2cvU5s5k0ZhhHTx7TuX1HQH/D69DLRahU3th9SpIkDQGWuGRMsVxhRFq2QrkIa56C4z/S5/odPejLX90MwCH7jGTKuOGdI7r8Zvi7GBmv5Yjf/WdmDh/HmcMKcFsN5Gth7ocY1fC3AGx+I8dCf+1x+PHZcPg74W++8cbtV5IkaQiwBz1jSl1r0Nc+A+V22P/oPtfvqBt/6tXN5AJMGTucaeNHdF4wuuSVVv5pxOWEo86GaSfD5GNhnyMg5OCXl7L/5iUAb9xsoi89DDe+C7augUduhHUr3pj9SpIkDREG9IwplrvUoHdcINrHCC4Aw2uS3va1TW1MGjuM2kKOg9KAHmNk2epN1B50PJz+b3Dmd+Cs78F7fwAf/CWMOZBDHvgUo9hKU2uJxpYi/3TncjZubd+9xj93H/zwDBg+Hi68G/J1cP+/7N6+JEmShigDesaUKl160F9dBjXDYfwhfa7fdeSVqeNHADBtwnBaixWWrW5kTVMbsyaP3n7DugY46wYKW17lyzU30NRS5KpfP8P1D67kJ398Ydcb/tQv4Kb3wriD4YO/gslvhmMvhMd/Cuue3fX9SZIkDVHWoGdMMlFRlx70fWdCru/hD2sLOQq5QKkSmTYhCehT0693PPYKQO8BHWDyXNpPvpy//Y8v86OlN/GTF2aRC/CzR1/mo/OnE3q7MPWpX8CjP0pKZPJpLTvAk7fBpDnw/n+HYWOTZfM+AYu/D//xtaTnXpIkSTtlD3rGJOOg55IRUF57fIflLR06etEPSnvQO3rS71z2CrkAR+zfR0AHak/+FA9V3sR7Xv0Gs4av53++6whWrtvKoy9u7Fxnw9Z2iu1t8Ksr4Ja/Ty5c3bwa1v0ZXlmS1J2/6W/gAz/fFs4BRk6EYz8MT9wKa/+8Oy+HJEnSkGMPesYkM4kG2LQK2jb3OUFRV8Nq8jS1lpg2YTgAB4wZRm0+x+ub2zh8v4YdTkAU8gU+n/s4/x7/O99v+N/UzTmbf/3NM9z6yGrefNA4Xli/lQ/92x18t/4aDm17Eo67CE79Ms9tbGfN5jbeesj4HTdu3iXwp+vh/q/BWdd3f65lE7ywKLl4ta5hp9+nJGkXbV2XXBfUx1C90naaXoNnfwN//nXSEXfQCTDjVJj2V1A3crBb95fZshbKbTB68mC3ZKeqPqCHEBYA3wTywPUxxisHuUnb+8N34JVH4a0fo1SJFPIhqT+HfvWgdwy12NFzns8FpowbxnNrt/Zd3tJFGD2Z7/EJLt30FfjGdG4feRj3PDaNrYefww9+tZxb+DrDWtu4d+ZXOOWdH2XFmibOvvYPbG4p8q33zeGdR+0PwI2LVnLtfzzPlWcdxdsO2yfZ+YgJcNx/hkX/Bif/D5h4WPLfgaU/gbu/AM3rkl73E/5bEv4N6lJ1adsCtSP6DIjFcoV1jU3sX9MCDfsB0Nhc5CcPv8DpRx/A5LFJx8OGre1cdusypu8zkv9x2mHk0/kiFq1Yxx9XbuBT/+nQbcdb+3Ty2Vmo7fWYe40YYdOLSUfG5pdh5H4wan9oSG/DxvYdvF97HH77j/DcPTD5ODj1S3DgW97Y9lUq0PgijD4Qcn38Q77UlpRGbl0Hhy2AsVN72U8ZXvwDrLgnGbXs8L+B/F4UT9qaYNlP4ak7YMJhyfd50Il7/vwrF2HD8zDmIKip/8v2tfaZpFT1mbvg1aXJslGTYZ/D4fGfJaOy5WuTsH7wfJj0Zjhg9u7/zm7fCutXJOfF6Mkw5kCoGbb77S8Xk+vdJhza+7nTtgUWfRN+f00yOt6b/wHmX5FklIwKMcbBbsMeE0LIA38G/hOwGvgTcF6McXlf28ydOzcuXrx4gFqYWP2Lr7DfY9+iUNrKw8zk2UP+gffv/2pyMl3xyk5/8N7xzQd45rXNPP2ld1BbSD4kL7zxT9zz9Bq+dOaRfOAtB+1w+1cbWxhZV6Dhxfvg2d+w5bnfM2z9cvIhOTe2NBzMv475LD9+fhjXnHcMX7hjOeUYmTRmGE++0sj3/+FYHnp+Pd++7zlG1OZpK1X41/cezX86Yl+uf2AlP1+0jF/Gi2k/5DRGzb+ELbd9kpHrHmNF3UzCW/8rh7x8Bzz7G9prR7P4gL9n31Mu5pADJkKMvLppK4+8sJ7DJ+/D9P2SCZZK5QpPvLKZhvoCB08Y0Vkrv3FrO02tJaaMG9a5rFKJrN/SyoQtzxCeuzf5BbB1DeWpJ5Gb/nZC2nv/zGtN/J9FKymWIx+cN5UjJ237wybG2Hs9fj+81tjK+q1tHL7fKPIB2LIG1jyZlPxUSskHUudtBIzcB0YdAMMn9P0Lbw9pLZapr+n7vy17lVI7vPwIrLwfXvx9EmSmnZz0AI07ePtQ09YETa9DpZi8L5VycivUwdiDtv8l1LYFVj2QnE+rHoARE2H62+GQt8N+Rw14b2Vv52ipXCGE0BlkIQnDzW1lRneZTbhUrrB6Y0vyn7f086NUrvD0a02MGV7TGZDLlcjSF9aRW/0wR7U8TOG5u4ntW1gzcR6P1R/LQce+g8Om7EeMkSWPPcarD/87xzY/yMRNjxFGTeLVA/6a768/kj/XH8nF8w/l+AMbePr3d/DiAz/h+PaHGB2aKe97FK9PeQeffHwqf2wcw+hhNVz1d0czY5+RfPDGP/HihmbKlcipR+zL18+ZzXX3P8819z7LzAl5bj2lifo/3wHP/hZKLVA/BmaeCUe9Fw5864D/PO2W1s1JyHj9cXjh97BqUVJO2JeGA+DQ0+CwdyTnd80w2PQS3PdleOxmqB8Ns98HT/xf2PIaHPYu+OsvwMRDd7+NMSY/W0/eBk/+PGlfw/5wxJkw893JUL65HGxYCY/8H1jyY2hev237/WbBm06HN/0ttGxI9rP8dtjy+rZ1Rk2CuR+EOf+QlEpC8vP5wqLkddn8CkyeCwfNgwOO2RaEy8Wk7HLlfyTr1Y2CaSclP/fjp2/7uVz/HKy4O+klfmVJ0v6xU5PPhnHTkj84ho1Jtq8fDfWjoFC//c/1a0/A4huScN6+Jdl+8ytQak22PeSU5FY3MrluK+STr7l8l8dh23VmlTLEyrbbiH1gwvSkDR3KJVh1f/KePn0ntGxM9jN+enLN2r4zk8CevFnb9lXXkATh0QfC8HHJcRtXwxM/g8f/PfmDLuSS9+/Q0+DQBemQzCH5PH3pobRX/Tew7pl0/yEJxJPeDKMnQa4m+V7yNZArJJ+lpbbkVm5LQvmG55Phl3s7r0ful3zejkjf8xCSY4QAo6fAlONgylugYd/09aokn++P35qcQy0bkt+dM8+EI8+GKccnr8FjC+GeLyU/A0eelczOvvj7ScfByZcmc80U6vr7E/CGCiE8EmOc2+tzVR7Q3wp8IcZ4Wvr4MwAxxn/ua5vBCOgfvelR7l/2HB+o/R1/H37JAWFD8oMy8U1w8e93uv1Z3/09a5vauP+y+Z3LvnTncm54cCW3f3QeR08Zs4Ott1epRE772i/ZZ/PjvGdahbPO/29sKtfxzm8+wCuNrTTUF/jpf3krB4wexjnX/YE/v95EJcJ5x03h0wsO5yM/foSHnt/A+BG1rN/azkkzJvBXL32HD/NzKgTWxdF8K/8B/h8ns765yDEHjmHE2qV8sPTvvD2/pPc2xUBjbhRNNRN4sa2BV8qjyYcK+9a0cEBtC4X2TQwrb6E51rElNwqGjWUjI9nc3MaxPMnE0AjAS3XTebk0mqNKTzAitFGiwDN1M1m2dRyjc62MDK3Ux2b2rW2nHApsKNWxsVxPqBtJ3fBRECs0t7TS3t7KiEJkVF2OfKGGTe2wqTVSDjU0DKtlZG1g05YWWtrayVFhSn4Dh+dW01DZ3K/3oBQKbMqPZ10cQ0uoJ9YMJ9SOoDnW0VTK0VaK1NfkOv970lKs0FosU5NPlhVygdZShZb2MrkA9TV56mtytKXLiuVIbSFHfSFHa6lCY0uR1mKZYbV5xg2vZVhtntZimZZimXwIDKvNU1dItm8tVqjEmOyzkKNcibSWKhRLFepqctQVcoQQaC2WaStVKOQC9TV5avKBtmKF1lIZCNQXctTV5CiWI63FMqVKTJfliTHSVqrQVqxQk7azkINysZVKeyt1ociIfInaHLRUCmwt52mjhlxNPQfE1zlo6+PUxVYqBF6qmcbo2MSY0loA1uXG80ztkTTki0wor2F0+2uMqGzZ4fvRlB/DhpoDeDnsw/D2DRxZXk6BEu2hnqfrjqShfR3TKquSdQvjWF43m42lArlyO6NqyowsVAjlEk2lHFvKeXKFemrr6yFXw9a2Iltbi9TkAyPrkveppb1EU2uJGCMNdXlG1uVpL5XZ0lqkrVhmRG2eUfXJe7+5tUhzW5G6Qp4R9bXU1NSysaXMhpYKuRCZOCwwti7S1tZCa0sL5Qj5mjpGDB9BcyXPa1sr5MvtjMq1sl99kZGhlVLbVrZWatka64m1IykMH01L0yaOi8sYFZopxjwr6o9kXbGWOeVljAhttMUCT9QcyehKI9MrKwF4snIQvw/HcFTda8xue4T6UGQDo3i0fAjH5/9MA1tpYjjPjDmJ+9aN4bTCEmaRXK/SOOYIHm45gNi8kQm5LYwNTUypa6FYgdfba2lmOJtjPeMaRjCj7UlCqQVG7puEvynHJUH96Tuh2Jz8cp/2V0lIaNuShKm2ZPZl6hqgdmQSompHAjENFa3bwkW+JvkFXqhPvuZrkzBYLia9ceX2JAR1XDjf9QL6Unty3HJ7sn7IpevUJsEy5GDjquSP9qZXtp10I/ZJeiunnph8HTstmV+i6TVoejUJgi8+BM/dm3w/NcOTcPXiQ8n2x/8XOOlTyR+n7Vvhoe/Ag99MXo83/W3yvZZaoNiafK+VUhKqOsJVxy2fhq5cIfken/td0mueq4Hpf50E4Bd+n7ze5bYkXI87OPnDNeSTPx6OvTAJwE//P1h+B6x+eNv3WahPyidmvjvZ36oH4eHr4Pn7ktfo4Pmw4bmktxWSjoyG/ZJlAIVhMOXYZN0XH0peC0jCZWtj8p8HSMLflGPh9eXbth13SPLHW/O65A+KjauS76E3Ib/t/a8ZlrxvjS8ly2a+B+Z+KPmjodiS/IHwzC+TEpGuf3jsrhH7wIQZyfm98v6kvbUj4bB3Jn+YbXoRXn8SXn8CNvVjFLaa4Uln0MZVyeNJc+Gov0veg47wuyNb1yV/2Lz8aPLf/5cfha1rgT7yZMilr119ch5MmJHcxs9Iwvjml5O2bHwh+dqyMdlXjHT+kbHxhW3vzZiDkv+0rP5T8rNQMzw5z6aelL72v0rO7VGTkz+u1ixPfjZO+0ry2QDJz9tvPgvP/jrZ32lfTn4uBthQDuhnAwtijB9OH38AOD7G+LEe610EXJQ+PAx4hoE3AVg3CMfV3sdzRf3heaL+8lxRf3ievPEOijFO7O2JvajIa7f09n/m7f4iiTFeB1y355vTtxDC4r7+ipK68lxRf3ieqL88V9QfnicDay8oyvuLrAamdHk8GXilj3UlSZKkQVftAf1PwIwQwrQQQi1wLnDHILdJkiRJ6lNVl7jEGEshhI8BvyYZZvH7McYnB7lZfRnUEhvtVTxX1B+eJ+ovzxX1h+fJAKrqi0QlSZKkvU21l7hIkiRJexUDuiRJkpQhBvQMCCEsCCE8E0JYEUK4fLDbo4ERQlgVQng8hLA0hLA4XTYuhPDbEMKz6dex6fIQQvi39BxZFkKY02U/F6TrPxtCuKDL8jen+1+Rbjuw01tqt4QQvh9CWBNCeKLLsj1+XvR1DGVXH+fKF0IIL6efK0tDCO/s8txn0vf9mRDCaV2W9/o7KB1g4Y/pOXFLOtgCIYS69PGK9PmpA/Mda3eEEKaEEO4LITwVQngyhHBJutzPlSyLMXobxBvJxavPAQcDtcBjwBGD3S5vA/LerwIm9Fj2NeDy9P7lwFfT++8E7iIZ2/8twB/T5eOA59OvY9P7Y9PnHgbemm5zF/COwf6evfXrvDgZmAM8MZDnRV/H8JbdWx/nyheAS3tZ94j090sdMC39vZPf0e8g4KfAuen9a4H/mt6/GLg2vX8ucMtgvxbednie7A/MSe83AH9Ozwc/VzJ8swd98B0HrIgxPh9jbAduBs4Y5DZp8JwB/CC9/wPgzC7LfxgTDwFjQgj7A6cBv40xbogxbgR+CyxInxsVY/xDTD4Zf9hlX8qwGOP9wIYeiwfivOjrGMqoPs6VvpwB3BxjbIsxrgRWkPz+6fV3UNoDegpwa7p9z/Ou41y5FXi7/6HLrhjjqzHGR9P7TcBTwCT8XMk0A/rgmwS81OXx6nSZql8EfhNCeCSEcFG6bN8Y46uQfKgC+6TL+zpPdrR8dS/LtXcaiPOir2No7/OxtDTh+11KCnb1XBkPbIoxlnos77av9PnGdH1lXFqOdAzwR/xcyTQD+uDrrdfBsS+HhnkxxjnAO4CPhhBO3sG6fZ0nu7pc1cXzQj19FzgEmA28CvxruvyNPFc8j/ZCIYSRwM+AT8QYN+9o1V6W+bkywAzog281MKXL48nAK4PUFg2gGOMr6dc1wG0k/2p+Pf13IenXNenqfZ0nO1o+uZfl2jsNxHnR1zG0F4kxvh5jLMcYK8D3SD5XYNfPlXUkpQ2FHsu77St9fjT9L7XRIAgh1JCE85/EGP9vutjPlQwzoA++PwEz0qvla0kuuLljkNukPSyEMCKE0NBxHzgVeILkve+4Mv4C4Pb0/h3A+enV9W8BGtN/F/4aODWEMDb9V/apwK/T55pCCG9Ja0PP77Iv7X0G4rzo6xjai3SEodS7ST5XIHl/z01HYJkGzCC5sK/X30FpLfF9wNnp9j3Pu45z5Wzg3nR9ZVD6s34D8FSM8etdnvJzJcsG+ypVb51XTP+Z5Er6/znY7fE2IO/5wSSjJTwGPNnxvpPUcd4DPJt+HZcuD8C303PkcWBul319iOSCrxXAB7ssn0vyy/k54FukMwd7y/YNWEhSmlAk6Zm6cCDOi76O4S27tz7OlR+l58IyknC0f5f1/2f6vj9Dl1Gd+vodlH5OPZyeQ/8O1KXL69PHK9LnDx7s18LbDs+TE0lKTpYBS9PbO/1cyfat4wWUJEmSlAGWuEiSJEkZYkCXJEmSMsSALkmSJGWIAV2SJEnKEAO6JEmSlCEGdEmSJClDDOiSVAVCCGNCCBfvZJ2pIYT39WNfC0MIy0IIn9yNdmzZ1W362q6/7ZWkamNAl6TqMAbYYUAHpgI7DLwhhP2AE2KMs2KM33iD2ra7prKT9kpSNTKgS1J1uBI4JISwNITwL+ntiRDC4yGEc7qsc1K6Tl+9478B9knX+asQwiMAIYSjQwgxhHBg+vi5EMLwdIr4P4QQ/hRC+NKOGhhCeFsI4f4Qwm0hhOUhhGtDCLke60xI9/eunu0NIcwMITycPl4WQpjxF7xekpRZhcFugCTpDXE5cGSMcXYI4SzgI8DRwATgTyGE+9N1Lo0x/s0O9nM6cGeMcTZACKE+hDAKOAlYTBKYHwTWxBibQwjfBL4bY/xhCOGj/WjnccARwAvAr4D3ALemx9qXZHr6z8YYfxtC2Nq1vSGEa4Bvxhh/EkKoBfK78PpI0l7DHnRJqj4nAgtjjOUY4+vAfwDH7ua+fg/MA04GvpJ+PQl4IH1+HrAwvf+jfuzv4Rjj8zHGcrrdienyGuAe4LIY42/72PYPwBUhhE8DB8UYW3b1m5GkvYEBXZKqT3gD9/UASSA/CLidpFf+ROD+LuvEXdhfz3U7HpeAR4DT+twwxptIevhbgF+HEE7ZheNK0l7DgC5J1aEJaEjv3w+cE0LIhxAmkvR6P9xjnf66H/h74NkYYwXYALwTWJQ+vwg4N73//n7s77i0bj0HnAM8mC6PwIeAw0MIl/fyPRFCOBh4Psb4bySlMLN28XuRpL2CAV2SqkCMcT2wKITwBPBWYBnwGHAvSdnIa+myUgjhsf4OoRhjXJXe7egxfxDYFGPcmD6+BPhoCOFPwOh+7PIPJBd/PgGsBG7rcqwySdifnw4Z2bO95wBPhBCWAocDP+zP9yBJe5sQ4678Z1KSpN0TQngbO79IVZKGPHvQJUmSpAyxB12ShqAQwmnAV3ssXhljfPcbsO+j2H5El7YY4/F/6b4laSgwoEuSJEkZYomLJEmSlCEGdEmSJClDDOiSJElShhjQJUmSpAwxoEuSJEkZYkCXJEmSMsSALkmSJGWIAV2SJEnKEAO6JEmSlCEGdEmSJClDDOiSJElShhjQJUmSpAwxoEuSJEkZYkCXJEmSMsSALkmSJGWIAV2SJEnKEAO6JEmSlCEGdEmSJClDDOiSJElShhjQJUmSpAwxoEuSJEkZYkCXJEmSMsSALkmSJGWIAV2SJEnKEAO6JEmSlCEGdEmSJClDDOiSJElShhjQJUmSpAwxoEuSJEkZUhjsBmTNhAkT4tSpUwe7GZIkSapijzzyyLoY48TenjOg9zB16lQWL1482M2QJElSFQshvNDXc5a4SJIkSRliQJckSZIyxIAuSZIkZYg16JIkSVWuWCyyevVqWltbB7spQ059fT2TJ0+mpqam39sY0CVJkqrc6tWraWhoYOrUqYQQBrs5Q0aMkfXr17N69WqmTZvW7+0scZEkSapyra2tjB8/3nA+wEIIjB8/fpf/c2FAlyRJGgIM54Njd153A7okSZKUIQZ0SZIkDYjbbruNEAJPP/1057JVq1Zx0003dT5eunQpv/zlL3f7GFOnTmXdunV9Pt/c3My73vUuDj/8cGbOnMnll1/e+VxbWxvnnHMO06dP5/jjj2fVqlUArF+/nvnz5zNy5Eg+9rGPddvfwoULOeqoo5g1axYLFizY4bH7y4AuSZKkAbFw4UJOPPFEbr755s5lb3RA749LL72Up59+miVLlrBo0SLuuusuAG644QbGjh3LihUr+OQnP8mnP/1pIBmJ5Utf+hJXXXVVt/2USiUuueQS7rvvPpYtW8asWbP41re+9Re3z4AuSZKkPW7Lli0sWrSIG264oVtAv/zyy3nggQeYPXs2X/3qV/n85z/PLbfcwuzZs7nlllt4+OGHOeGEEzjmmGM44YQTeOaZZwAol8tceumlnb3X11xzTbfjtbS0sGDBAr73ve91Wz58+HDmz58PQG1tLXPmzGH16tUA3H777VxwwQUAnH322dxzzz3EGBkxYgQnnngi9fX13fYVYyTGyNatW4kxsnnzZg444IC/+LVymEVJkqQh5H/94kmWv7L5Dd3nEQeM4h//duYO1/n5z3/OggULOPTQQxk3bhyPPvooc+bM4corr+Sqq67izjvvBGDfffdl8eLFnT3Rmzdv5v7776dQKHD33XdzxRVX8LOf/YzrrruOlStXsmTJEgqFAhs2bOg81pYtWzj33HM5//zzOf/88/vWkYDrAAAgAElEQVRs06ZNm/jFL37BJZdcAsDLL7/MlClTACgUCowePZr169czYcKEXrevqanhu9/9LkcddRQjRoxgxowZfPvb3+7/C9cHe9Cz4vnfQaU82K2QJEnaIxYuXMi5554LwLnnnsvChQv7tV1jYyN/93d/x5FHHsknP/lJnnzySQDuvvtuPvKRj1AoJP3N48aN69zmjDPO4IMf/OAOw3mpVOK8887j4x//OAcffDCQ9Ij3tKNRWIrFIt/97ndZsmQJr7zyCrNmzeKf//mf+/V97Yg96Fmw4m748Vlwyufg5EsHuzWSJKmK7ayne09Yv3499957L0888QQhBMrlMiEEvva1r+1028997nPMnz+f2267jVWrVvG2t70NSMJ0X+F53rx53HXXXbzvfe/rc52LLrqIGTNm8IlPfKJz2eTJk3nppZeYPHkypVKJxsbGbsG/p6VLlwJwyCGHAPDe976XK6+8cqff087Yg54FTa8lXzc8P7jtkCRJ2gNuvfVWzj//fF544QVWrVrFSy+9xLRp03jwwQdpaGigqampc92ejxsbG5k0aRIAN954Y+fyU089lWuvvZZSqQTQrcTli1/8IuPHj+fiiy/utT2f/exnaWxs5Oqrr+62/PTTT+cHP/hBZ5tPOeWUHfagT5o0ieXLl7N27VoAfvvb3/KmN72pPy/JDhnQJUmStEctXLiQd7/73d2WnXXWWdx0003MmjWLQqHA0UcfzTe+8Q3mz5/P8uXLOy8Sveyyy/jMZz7DvHnzKJe3lQN/+MMf5sADD2TWrFkcffTR3UaCAbj66qtpbW3lsssu67Z89erVfPnLX2b58uXMmTOH2bNnc/311wNw4YUXsn79eqZPn87Xv/71br3hU6dO5VOf+hQ33ngjkydPZvny5RxwwAH84z/+IyeffDKzZs1i6dKlXHHFFX/x6xV6q7UZyubOnRsXL148sAdd8mO4/aMw+/1w5ncG9tiSJKnqPfXUU29Iz652T2+vfwjhkRjj3N7WtwddkiRJyhADuiRJkpQhBnRJkiQpQwzokiRJUoYY0CVJkqQMMaBLkiRJGWJAzwKHupQkSUPAbbfdRgiBp59+unPZqlWruo1hvnTpUn75y1/u9jGmTp3KunXr+ny+ubmZd73rXRx++OHMnDmTyy+/vPO5trY2zjnnHKZPn87xxx/PqlWrgGQm1Pnz5zNy5Eg+9rGPda7f1NTE7NmzO28TJkzoNjPp7jKgZ0rfM1VJkiTt7RYuXMiJJ57IzTff3LnsjQ7o/XHppZfy9NNPs2TJEhYtWsRdd90FwA033MDYsWNZsWIFn/zkJ/n0pz8NQH19PV/60pe46qqruu2noaGBpUuXdt4OOugg3vOe9/zF7TOgS5IkaY/bsmULixYt4oYbbugW0C+//HIeeOABZs+ezVe/+lU+//nPc8stt3TOJPrwww9zwgkncMwxx3DCCSfwzDPPAFAul7n00ks56qijmDVrFtdcc02347W0tLBgwQK+973vdVs+fPhw5s+fD0BtbS1z5sxh9erVANx+++1ccMEFAJx99tncc889xBgZMWIEJ554IvX19X1+f88++yxr1qzhpJNO+otfq8JfvAdJkiTtPe66HF57/I3d535HwTuu3OEqP//5z1mwYAGHHnoo48aN49FHH2XOnDlceeWVXHXVVdx5550A7LvvvixevJhvfetbAGzevJn777+fQqHA3XffzRVXXMHPfvYzrrvuOlauXMmSJUsoFAps2LCh81hbtmzh3HPP5fzzz+f888/vs02bNm3iF7/4BZdccgkAL7/8MlOmTAGgUCgwevRo1q9fz4QJE3b6EixcuJBzzjmHEP7yiggDeqZYiy5JkqrTwoULO+uzzz33XBYuXMicOXN2ul1jYyMXXHABzz77LCEEisUiAHfffTcf+chHKBSSODtu3LjObc444wwuu+wy3v/+9/e531KpxHnnncfHP/5xDj74YABiL9cF9jdw33zzzfzoRz/q17o7Y0DPgjfgLy1JkqR+2UlP956wfv167r33Xp544glCCJTLZUIIfO1rX9vptp/73OeYP38+t912G6tWreJtb3sbkITpvsLzvHnzuOuuu3jf+97X5zoXXXQRM2bM6HZR5+TJk3nppZeYPHkypVKJxsbGbsG/L4899hilUok3v/nNO123P6xBlyRJ0h516623cv755/PCCy+watUqXnrpJaZNm8aDDz5IQ0MDTU1Nnev2fNzY2MikSZMAuPHGGzuXn3rqqVx77bWUSiWAbiUuX/ziFxk/fjwXX3xxr+357Gc/S2NjI1dffXW35aeffjo/+MEPOtt8yimn9KsHfeHChZx33nk7Xa+/DOiSJEnaoxYuXMi73/3ubsvOOussbrrpJmbNmkWhUODoo4/mG9/4BvPnz2f58uWdF4ledtllfOYzn2HevHmUy+XO7T/84Q9z4IEHMmvWLI4++uhuI8EAXH311bS2tnLZZZd1W7569Wq+/OUvs3z5cubMmcPs2bO5/vrrAbjwwgtZv34906dP5+tf/zpXXrntvw1Tp07lU5/6FDfeeCOTJ09m+fLlnc/99Kc/fUMDeuit1mYomzt3bly8ePHAHnTJj+H2j8Ls98OZ3xnYY0uSpKr31FNP8aY3vWmwmzFk9fb6hxAeiTHO7W19e9AlSZKkDDGgS5IkSRliQM8Cy4wkSdIeZlnz4Nid192AnikOtyhJkt549fX1rF+/3pA+wGKMrF+/foczkPbGcdAlSZKq3OTJk1m9ejVr164d7KYMOfX19UyePHmXtulXQA8hLAC+CeSB62OMV/Z4vg74IfBmYD1wToxxVfrcZ4ALgTLw8Rjjr3e0zxDCNOBmYBzwKPCBGGN7X8cIIRwHXNfRFOALMcbb+tNuSZKkoaCmpoZp06YNdjPUTzstcQkh5IFvA+8AjgDOCyEc0WO1C4GNMcbpwDeAr6bbHgGcC8wEFgDfCSHkd7LPrwLfiDHOADam++7zGMATwNwY4+z0GP87hFDoZ7slSZKkTOlPDfpxwIoY4/MxxnaS3u0zeqxzBvCD9P6twNtDMu3SGcDNMca2GONKYEW6v173mW5zSroP0n2euaNjxBibY4yldHk90FFc1Z92S5IkSZnSn4A+CXipy+PV6bJe10nDciMwfgfb9rV8PLCpS+Dueqy+jkEI4fgQwpPA48BH0uf7025JkiQpU/oT0HsbWqTnJcB9rfNGLd9hO2KMf4wxzgSOBT4TQqjvZ7uTHYdwUQhhcQhh8eBePOGV1ZIkSUNdfwL6amBKl8eTgVf6WieEUABGAxt2sG1fy9cBY9J99DxWX8foFGN8CtgKHNnPdndsd12McW6Mce7EiRN7W2XPCg6vKEmSpER/AvqfgBkhhGkhhFqSiz7v6LHOHcAF6f2zgXtjMtDmHcC5IYS6dHSWGcDDfe0z3ea+dB+k+7x9R8dI91EACCEcBBwGrOpnuyVJkqRM2ekwizHGUgjhY8CvSYYr/H6M8ckQwheBxTHGO4AbgB+FEFaQ9Gqfm277ZAjhp8ByoAR8NMZYBuhtn+khPw3cHEL4J2BJum/6OgZwInB5CKEIVICLY4zrdnKMbHHSAEmSJKWCM0p1N3fu3Lh48eKBPeijP4I7Pgaz/x7O/PbAHluSJEkDLoTwSIxxbm/P9afERZIkSdIAMaBLkiRJGWJAlyRJkjLEgC5JkiRliAFdkiRJyhADuiRJkpQhBnRJkiQpQwzokiRJUoYY0CVJkqQMMaBLkiRJGWJAlyRJkjLEgC5JkiRliAFdkiRJyhADuiRJkpQhBnRJkiQpQwzokiRJUoYY0CVJkqQMMaBL+v/t3X+QXeV93/H3d+/uSvwWCOE6gIMc1ElE7RC8JU5tpwluQLhORGpSy3FipmGGNIE2TcZTIC4Zl7o/lDRl4hrjIYUEmNiC0DKoSWxMDC6xS4AlYINwFJYfKTKOkSwhg0DaH/fbP84jcVnu3b2LV7sP2vdr5s499znP+T5nz9G9+uzZc8+RJEkVMaBLkiRJFTGgVyEXewUkSZJUCQN6TWKxV0CSJEmLzYAuSZIkVcSAXhPPdJEkSVryDOhV8NwWSZIkNQzokiRJUkUM6JIkSVJFDOiSJElSRQzokiRJUkUM6JIkSVJFDOhV8PqKkiRJahjQa+LVFiVJkpY8A7okSZJUEQO6JEmSVBEDuiRJklQRA7okSZJUEQO6JEmSVBEDek282qIkSdKSZ0CvgtdXlCRJUqOvgB4R6yJia0SMRcRlXeYvi4iby/z7IuKUjnmXl/atEXHObDUjYnWp8XipOTzTGBHxUxHxYEQ8Up7P6qj15TLGw+Vxwtw3kSRJkrRwZg3oEdECrgbOBdYCH4qItdO6XQjsysxTgauAjWXZtcAG4DRgHfDpiGjNUnMjcFVmrgF2ldo9xwB2AD+dmW8DLgBumrZuH87M08vjuVm3yKLw3BZJkiQ1+jmCfiYwlplPZuY4sAlYP63PeuCGMn0r8N6IiNK+KTP3ZeZTwFip17VmWeasUoNS87yZxsjMhzLz2dK+BVgeEcv63QBV8UwXSZKkJa+fgH4i8EzH622lrWufzJwEdgMrZ1i2V/tK4PlSY/pYvcbo9AHgoczc19H2B+X0livKLwCvEREXRcRoRIxu3769WxdJkiRpQfQT0LuF2unnZPTqM1/ts65HRJxGc9rLL3fM/3A59eU95fGLXWqQmddm5khmjqxatapbF0mSJGlB9BPQtwEnd7w+CXi2V5+IGASOAXbOsGyv9h3AilJj+li9xiAiTgJuAz6SmU/sL5qZ3yzPLwCfpTm1RpIkSapWPwH9AWBNubrKMM2XPjdP67OZ5guaAOcDd2VmlvYN5Qosq4E1wP29apZl7i41KDVvn2mMiFgB/ClweWZ+df8KRcRgRBxfpoeA9wOP9vHzSpIkSYtmcLYOmTkZEZcAdwAt4PrM3BIRVwKjmbkZuA64KSLGaI5qbyjLbomIW4DHgEng4sycAuhWswx5KbApIj4BPFRq02sM4BLgVOCKiLiitJ0N7AHuKOG8Bfw58Ptz3kKSJEnSAormoLX2GxkZydHR0YUd9K9uhM3/Cn7kF2D91Qs7tiRJkhZcRDyYmSPd5nknUUmSJKkiBnRJkiSpIgZ0SZIkqSIGdEmSJKkiBnRJkiSpIgZ0SZIkqSIGdEmSJKkiBnRJkiSpIgZ0SZIkqSIGdEmSJKkiBnRJkiSpIgZ0SZIkqSIGdEmSJKkiBvQaZC72GkiSJKkSBvSqxGKvgCRJkhaZAV2SJEmqiAFdkiRJqogBvSqeiy5JkrTUGdBrEJ57LkmSpIYBXZIkSaqIAV2SJEmqiAFdkiRJqogBXZIkSaqIAb0G3klUkiRJhQG9Kl7NRZIkaakzoEuSJEkVMaBLkiRJFTGgS5IkSRUxoEuSJEkVMaBLkiRJFTGgS5IkSRUxoFfF66FLkiQtdQb0GoTXP5ckSVLDgC5JkiRVxIBeg/TUFkmSJDUM6FXxVBdJkqSlzoAuSZIkVaSvgB4R6yJia0SMRcRlXeYvi4iby/z7IuKUjnmXl/atEXHObDUjYnWp8XipOTzTGBHxUxHxYEQ8Up7P6qj1jtI+FhGfjPDbmJIkSarbrAE9IlrA1cC5wFrgQxGxdlq3C4FdmXkqcBWwsSy7FtgAnAasAz4dEa1Zam4ErsrMNcCuUrvnGMAO4Kcz823ABcBNHet1DXARsKY81s26RSRJkqRF1M8R9DOBscx8MjPHgU3A+ml91gM3lOlbgfeWo9XrgU2ZuS8znwLGSr2uNcsyZ5UalJrnzTRGZj6Umc+W9i3A8nK0/c3A0Zl5b2YmcGNHLUmSJKlK/QT0E4FnOl5vK21d+2TmJLAbWDnDsr3aVwLPlxrTx+o1RqcPAA9l5r7Sf9ss6w1ARFwUEaMRMbp9+/ZuXSRJkqQF0U9A73be9vTrAvbqM1/ts65HRJxGc9rLL/fT/1WNmddm5khmjqxatapbF0mSJGlB9BPQtwEnd7w+CXi2V5+IGASOAXbOsGyv9h3AilJj+li9xiAiTgJuAz6SmU909D9plvWWJEmSqtJPQH8AWFOurjJM86XPzdP6bKb5gibA+cBd5bzvzcCGck74apovat7fq2ZZ5u5Sg1Lz9pnGiIgVwJ8Cl2fmV/evUGZ+C3ghIt5Zzm3/SEctSZIkqUqzBvRyvvclwB3AN4BbMnNLRFwZET9Tul0HrIyIMeA3gMvKsluAW4DHgC8AF2fmVK+apdalwG+UWitL7Z5jlDqnAldExMPlcUKZ9yvA/6D5cuoTwOfntnkkSZKkhRXpbeZfZWRkJEdHRxd20AdvgP/9r+FHfhHWf2phx5YkSdKCi4gHM3Ok2zzvJCpJkiRVxIAuSZIkVcSALkmSJFXEgC5JkiRVxIAuSZIkVcSALkmSJFXEgC5JkiRVxIAuSZIkVcSAXgVvFiVJkqSGAb0mEYu9BpIkSVpkBnRJkiSpIgZ0SZIkqSIG9Jqk56JLkiQtdQb0KnjuuSRJkhoGdEmSJKkiBnRJkiSpIgZ0SZIkqSIGdEmSJKkiBnRJkiSpIgb0Knh5RUmSJDUM6DUJL7coSZK01BnQJUmSpIoY0CVJkqSKGNAlSZKkihjQJUmSpIoY0CVJkqSKGNBrkl5uUZIkaakzoFfByytKkiSpYUCXJEmSKmJAr4KntkiSJKlhQK+JdxKVJEla8gzokiRJUkUM6JIkSVJFDOiSJElSRQzokiRJUkUM6JIkSVJFDOiSJElSRfoK6BGxLiK2RsRYRFzWZf6yiLi5zL8vIk7pmHd5ad8aEefMVjMiVpcaj5eawzONERErI+LuiHgxIj41bb2+XMZ4uDxOmNvmkSRJkhbWrAE9IlrA1cC5wFrgQxGxdlq3C4FdmXkqcBWwsSy7FtgAnAasAz4dEa1Zam4ErsrMNcCuUrvnGMBe4Argoz1+hA9n5unl8dxsP68kSZK0mPo5gn4mMJaZT2bmOLAJWD+tz3rghjJ9K/DeiIjSvikz92XmU8BYqde1ZlnmrFKDUvO8mcbIzD2Z+RWaoP7GlN5JVJIkSY1+AvqJwDMdr7eVtq59MnMS2A2snGHZXu0rgedLjelj9RpjNn9QTm+5ovwC8BoRcVFEjEbE6Pbt2/soKUmSJB0c/QT0bqF2+iHfXn3mq73f9Zjuw5n5NuA95fGL3Tpl5rWZOZKZI6tWrZql5EHQ/fcGSZIkLUH9BPRtwMkdr08Cnu3VJyIGgWOAnTMs26t9B7Ci1Jg+Vq8xesrMb5bnF4DP0pxaI0mSJFWrn4D+ALCmXF1lmOZLn5un9dkMXFCmzwfuysws7RvKFVhWA2uA+3vVLMvcXWpQat4+yxhdRcRgRBxfpoeA9wOP9vHzSpIkSYtmcLYOmTkZEZcAdwAt4PrM3BIRVwKjmbkZuA64KSLGaI5qbyjLbomIW4DHgEng4sycAuhWswx5KbApIj4BPFRq02uMUutp4GhgOCLOA84G/ha4o4TzFvDnwO+/jm0kSZIkLZhZAzpAZv4Z8GfT2n6rY3ov8HM9lv2PwH/sp2Zpf5Iup6LMMsYpPVb9HT3aJUmSpCp5J1FJkiSpIgZ0SZIkqSIGdEmSJKkiBnRJkiSpIgb0GvS+WqQkSZKWGAN6VbyjqCRJ0lJnQJckSZIqYkCXJEmSKmJAr4rnokuSJC11BvQahOeeS5IkqWFAlyRJkipiQJckSZIqYkCXJEmSKmJAlyRJkipiQK+BdxKVJElSYUCvildzkSRJWuoM6JIkSVJFDOiSJElSRQzokiRJUkUM6JIkSVJFDOiSJElSRQzokiRJUkUM6FXxeuiSJElLnQG9BuH1zyVJktQwoNfAO4lKkiSpMKBXxSPpkiRJS50BXZIkSaqIAV2SJEmqiAFdkiRJqogBXZIkSaqIAV2SJEmqiAFdkiRJqogBXZIkSaqIAV2SJEmqiAFdkiRJqogBXZIkSaqIAV2SJEmqSF8BPSLWRcTWiBiLiMu6zF8WETeX+fdFxCkd8y4v7Vsj4pzZakbE6lLj8VJzeKYxImJlRNwdES9GxKemrdc7IuKRsswnIyLmtnkkSZKkhTVrQI+IFnA1cC6wFvhQRKyd1u1CYFdmngpcBWwsy64FNgCnAeuAT0dEa5aaG4GrMnMNsKvU7jkGsBe4Avhol9W/BrgIWFMe62b7eSVJkqTF1M8R9DOBscx8MjPHgU3A+ml91gM3lOlbgfeWo9XrgU2ZuS8znwLGSr2uNcsyZ5UalJrnzTRGZu7JzK/QBPUDIuLNwNGZeW9mJnBjRy1JkiSpSv0E9BOBZzpebyttXftk5iSwG1g5w7K92lcCz5ca08fqNcZM671tlvWWJEmSqtJPQO923nb22We+2vtdj37W6bUdIy6KiNGIGN2+ffsMJSVJkqSDq5+Avg04ueP1ScCzvfpExCBwDLBzhmV7te8AVpQa08fqNcZM633SLOsNQGZem5kjmTmyatWqGUpKkiRJB1c/Af0BYE25usowzZc+N0/rsxm4oEyfD9xVzvveDGwoV2BZTfNFzft71SzL3F1qUGrePssYXWXmt4AXIuKd5dz2j3TUkiRJkqo0OFuHzJyMiEuAO4AWcH1mbomIK4HRzNwMXAfcFBFjNEe1N5Rlt0TELcBjwCRwcWZOAXSrWYa8FNgUEZ8AHiq16TVGqfU0cDQwHBHnAWdn5mPArwB/CBwGfL48JEmSpGrFDAehl6SRkZEcHR1d2EFHr4c/+XU44wL4mU8u7NiSJElacBHxYGaOdJvnnURr4n2UJEmSljwDuiRJklQRA7okSZJUEQN6Tfw+gCRJ0pJnQK+C555LkiSpYUCXJEmSKmJAlyRJkipiQJckSZIqYkCXJEmSKmJAr4JXb5EkSVLDgF4T7yQqSZK05BnQJUmSpIoY0GvgDYokSZJUGNCr4ikukiRJS50BXZIkSaqIAV2SJEmqiAG9Cp6DLkmSpIYBXZIkSaqIAb0KfjlUkiRJDQN6FTzFRZIkSQ0Dek28k6gkSdKSZ0CXJEmSKmJAr4F3EpUkSVJhQK+Kp7hIkiQtdQZ0SZIkqSIGdEmSJKkiBvSaeBUXSZKkJc+ALkmSJFXEgF4Dr+IiSZKkwoBeFU9xkSRJWuoM6JIkSVJFDOiSJElSRQzokiRJUkUM6JIkSVJFDOhV8CoukiRJahjQa+KNiiRJkpY8A7okSZJUkb4CekSsi4itETEWEZd1mb8sIm4u8++LiFM65l1e2rdGxDmz1YyI1aXG46Xm8PcwxtMR8UhEPBwRo3PbNAvIGxVJkiSpmDWgR0QLuBo4F1gLfCgi1k7rdiGwKzNPBa4CNpZl1wIbgNOAdcCnI6I1S82NwFWZuQbYVWrPeYyOdfvJzDw9M0f63CaLyFNcJEmSlrp+jqCfCYxl5pOZOQ5sAtZP67MeuKFM3wq8NyKitG/KzH2Z+RQwVup1rVmWOavUoNQ873WOIUmSJL3h9BPQTwSe6Xi9rbR17ZOZk8BuYOUMy/ZqXwk8X2pMH2uuY0BzeZQvRsSDEXFRHz/rIvEUF0mSJDUG++jT7byL6YmyV59e7d1+MZip/+sZA+BdmflsRJwA3BkRf52Z90zvXML7RQBvectbupRbIF7FRZIkacnr5wj6NuDkjtcnAc/26hMRg8AxwM4Zlu3VvgNYUWpMH2uuY5CZ+5+fA26jx6kvmXltZo5k5siqVat6bAZJkiTp4OsnoD8ArClXVxmm+ULm5ml9NgMXlOnzgbsyM0v7hnIFltXAGuD+XjXLMneXGpSat7+eMSLiiIg4CiAijgDOBh7tb7NIkiRJi2PWU1wyczIiLgHuAFrA9Zm5JSKuBEYzczNwHXBTRIzRHNXeUJbdEhG3AI8Bk8DFmTkF0K1mGfJSYFNEfAJ4qNRmrmNExJuA25rvkTIIfDYzv/C6t9TBlO3m+ckvL+pqSJIkafFFeg3uVxkZGcnR0QW+ZPpf/C586cpm+uO7F3ZsSZIkLbiIeLDXZcC9k2gV/HKoJEmSGgb0KvT4K8Zf/xlM7F3YVZEkSdKiMqDXatsobPoQfPFji70mkiRJWkAG9Fq9vKt53vX0oq6GJEmSFpYBvQZ+UVeSJEmFAV2SJEmqiAFdkiRJqogBvQqe4iJJkqSGAb0G5nNJkiQVBnRJkiSpIgb0KsxwCN0rvEiSJC0pBvQaGMIlSZJUGNCrMENAj1i41ZAkSdKiM6BLkiRJFTGg18BTXCRJklQY0CVJkqSKGNCr4BF0SZIkNQzoNcj2DPMM75IkSUuJAV2SJEmqiAG9BjMdJfcyi5IkSUuKAb0KnsYiSZKkhgG9Bp5nLkmSpMKAXoUZArrhXZIkaUkxoNfAEC5JkqTCgC5JkiRVxIBeBY+gS5IkqWFAr4GXWZQkSVJhQH+j+drN8PU/Xuy1kCRJ0kEyuNgroDl68A9hoAVv/7nFXhNJkiQdBB5Br8HURO95009/yTaEu02SJOlQZdKrwX3XvLZtarx732x7XrokSdIhzIBeqy//5+b5iS9Nm5EeQZckSTqEmfRq9d1vdW/3FBdJkqRDmkmvNrPdVdSALkmSdEgz6dUm269te3H7K9PPPgTP/7+FWx9JkiQtKAN6bdpT8Pid8NKOV9r+66nw0s5XXm//64VfL0mSJC0IA3ptPvvP4Y/Of237b6+GPd9Z+PWRJEnSguoroEfEuojYGhFjEXFZl/nLIuLmMv++iDilY97lpX1rRJwzW82IWF1qPF5qDs/3GFV78u7e837nrQu3HpIkSVoUs95JNCJawNXATwHbgAciYnNmPtbR7UJgV2aeGhEbgI3AByNiLbABOA34PuDPI+Lvl2V61dwIXJWZmyLiM6X2NfM8hiSxd2KKv/3OSzy140X+bvdejlo+xHFHDnPc4cMcd8Qwy4YG2P3SBLtemmDXS+PsfmmCiXabw4ZaHD7cYvlQi8OGWuydbLP75Ql2vzTO7ig4zWgAAA1eSURBVJcneGHvJEOtAQ4bbrFssHk+rPRdXh6HDbdoRbB3cop9E232Tkyxd3KKTBhqDTDUCgZbAwwNBPum2ry0b4o9+ybZMz7J3ok2xx0xxAlHL+dNRy3nTUcv49jDh5lsJxNTbSam2oxPtmkNBMcePszAgPdOkKQ3klkDOnAmMJaZTwJExCZgPdAZdNcDHy/TtwKfiogo7Zsycx/wVESMlXp0qxkR3wDOAn6+9Lmh1L1mvsaYtt5veJlJeOOiN4zMZKqdtBMGB6JncMpMXp5oglsEBEEMQADthPHJNuMlhI1PtmlnMhBR+jb3spqYSsYn2+ybbLNvcoqJqXZTJ6A1EAyUfzf7++yvN9VuMxBBa6B5RAStCFoDHGgfGAgGy/zBgQEGW91fBx0BdHKKvROd08167Z1oMznVZrKdtNvJZDvJTJYNtThy2SBHLBvkyGUtlg222P3yBN/ZM86uPeN8Z884eyemOGJZiyOXDXHU8kGOWj7IVDvZuWecHS+Os3PPPnbuGWdiKg/8PK0IkmTbrpf55vMvz3rhpNdjeHCAyak27YNQe64GAo47YhnHHznMqqOWcfhwi4GIA/9emmkYKP8mBkpbdEy3yv4+5rAhjjl8qHk+bIjDhlpMtZOpzAP7b7A1cOAXmMOGm+cgaGfSziSTMk15nQe207LBAZYPtQ48D0Sz3GS7vG/aydBgU7/lLx2SDmH9BPQTgWc6Xm8DfrRXn8ycjIjdwMrS/pfTlj2xTHeruRJ4PjMnu/SfrzEOCWt/6wuMTzahBvYHro4AVf5z3R9KmtjWy8wpYtYrP8647Cy1gXa723/aHPgPvTPkDAQHgsP+sLk/ZATNMyWkDgxECatxILQ2Xh1ko2yb/W2UZTrtH6csfaANOBAcJtvN+k61Xwnh+8NLuzxP3xzDrQGWDTVhZLg1wL7JKV4an+LliamDEhwPFc2R4SGWD7V4aXyKF/ZOMDH1ygZrQunwgcfyoda0fZOc8ZZj+cAZJ/HWVUfw1uOP5M0rlvPi3skDvwDsfGmcfRNTHHP4MMcePsSxhw+z4vAhhloDvDz+yn56eXyK5UMDrDh8iKNLcF022CIzmZhqftHaWx77++8tR8wn28nyoQGWDbYOPDe/XLWZnEom220mppLhwQGOXDbI4cPNLy3DgwPs3DPOt7+7j+e+u5dvf3cvu16aYHiwOfLeHIFvfknY8eI439mzj+0vjLPjxX1sf2Hfq95r7Xbv9127/cr0+GSblyemFnGvv1rnXyaAA/t2quzn5hfGZnvs/xxsfl4O/FKw/2fLLp81r3xGRMfnAnR+frzSb3rbqz8jOvtImlm3//tyWtLo2qfP/zO75ZL3//D38Zvv+6H+CiyQfgJ6t4+U6T9drz692rud+z5T//kc4zUi4iLgovLyxYjY2q3fQXY8sGPWXgece9BWZImb437QQfQ97Yun5nFFljjfE3VwP9TDfVGHedsP9wIfm49Cc/f9vWb0E9C3ASd3vD4JeLZHn20RMQgcA+ycZdlu7TuAFRExWI6id/afrzFeIzOvBa7tNm+hRMRoZo4s5jrI/VAT90Ud3A91cD/Uw31Rh0N9P/RzFZcHgDXl6irDNF/I3Dytz2bggjJ9PnBXNn9D2AxsKFdgWQ2sAe7vVbMsc3epQal5+3yO0d9mkSRJkhbHrEfQy/nelwB3AC3g+szcEhFXAqOZuRm4DripfEFzJ00YpvS7heaLmZPAxZk5BdCtZhnyUmBTRHwCeKjUZp7HkCRJkqoUs32JTwsjIi4qp9poEbkf6uG+qIP7oQ7uh3q4L+pwqO8HA7okSZJUkb7uJCpJkiRpYRjQKxAR6yJia0SMRcRli70+h4qIeDoiHomIhyNitLQdFxF3RsTj5fnY0h4R8cmyD74eEWd01Lmg9H88Ii7oaH9HqT9WlvUqx0BEXB8Rz0XEox1tB3279xpjqeqxHz4eEd8s74mHI+J9HfMuL9t0a0Sc09He9fOpfAH/vrK9by5fxqd8Yf/m0v++iDhlYX7iOkXEyRFxd0R8IyK2RMSvlXbfEwtshn3h+2IBRcTyiLg/Ir5W9sO/L+1z3nbztX+qlOVObj4W50HzBdYngLcCw8DXgLWLvV6HwgN4Gjh+WttvA5eV6cuAjWX6fcDnaa6r/07gvtJ+HPBkeT62TB9b5t0P/FhZ5vPAuYv9M9fwAH4cOAN4dCG3e68xluqjx374OPDRLn3Xls+eZcDq8pnUmunzCbgF2FCmPwP8Spn+VeAzZXoDcPNib4tF3g9vBs4o00cBf1O2t++JevaF74uF3Q8BHFmmh4D7yr/1OW27+dw/NT48gr74zgTGMvPJzBwHNgHrF3mdDmXrgRvK9A3AeR3tN2bjL2mux/9m4BzgzszcmZm7gDuBdWXe0Zl5bzbv9Bs7ai1pmXkPzZWWOi3Edu81xpLUYz/0sh7YlJn7MvMpYIzms6nr51M5QnsWcGtZfvo+3b8fbgXeu/+I7lKUmd/KzL8q0y8A36C527XviQU2w77oxffFQVD+bb9YXg6VRzL3bTef+6c6BvTFdyLwTMfrbcz8gaH+JfDFiHgwmrvFArwpM78FzYc1cEJp77UfZmrf1qVd3S3Edu81hl7tknLqxPUdpzzMdT+sBJ7P5oZyne2vqlXm7y79l7zyp/kfoTli6HtiEU3bF+D7YkFFRCsiHgaeo/ll8wnmvu3mc/9Ux4C++Lr9Bu2ldebHuzLzDOBc4OKI+PEZ+vbaD3Nt19y43RfWNcAPAKcD3wJ+t7TP535wH3UREUcC/xP4N5n53Zm6dmnzPTGPuuwL3xcLLDOnMvN0mru8nwn8ULdu5Xm+9sMbah8Y0BffNuDkjtcnAc8u0rocUjLz2fL8HHAbzYfAt8ufhCnPz5XuvfbDTO0ndWlXdwux3XuNoSIzv13+Y2wDv0/znoC574cdNKdeDE5rf1WtMv8Y+j/V5pAUEUM0gfCPMvN/lWbfE4ug277wfbF4MvN54Ms056DPddvN5/6pjgF98T0ArCnfLB6m+QLE5kVepze8iDgiIo7aPw2cDTxKs233X/3gAuD2Mr0Z+Eg03gnsLn8SvgM4OyKOLX/2PBu4o8x7ISLeWc5r+0hHLb3WQmz3XmOo2B/Wip+leU9As+02lKslrAbW0HzxsOvnUznX+W7g/LL89H26fz+cD9xV+i9J5d/pdcA3MvO/dczyPbHAeu0L3xcLKyJWRcSKMn0Y8E9ovg8w1203n/unPvP1bVMfr/9B8639v6E5B+tji70+h8KD5tvbXyuPLfu3K805aF8CHi/Px5X2AK4u++ARYKSj1i/RfPlkDPgXHe0jNB/kTwCfotz4a6k/gM/R/Jl4guZIxoULsd17jbFUHz32w01lO3+d5j+3N3f0/1jZplvpuCJRr8+n8h67v+yfPwaWlfbl5fVYmf/Wxd4Wi7wf3k3zZ/SvAw+Xx/t8T1S1L3xfLOx+eDvwUNnejwK/9Xq33Xztnxof3klUkiRJqoinuEiSJEkVMaBLkiRJFTGgS5IkSRUxoEuSJEkVMaBLkiRJFTGgS5IkSRUxoEvSISAiVkTEr87S55SI+Pk+an0uIr4eEb/+Otbjxbku02u5ftdXkg41BnRJOjSsAGYM6MApwIyBNyL+HvCPMvPtmXnVPK3b63UKs6yvJB2KDOiSdGj4L8APRMTDEfE75fFoRDwSER/s6POe0qfX0fEvAieUPv84Ih4EiIgfjoiMiLeU109ExOHldtr3RsQDEfEfZlrBiPiJiLgnIm6LiMci4jMRMTCtz/Gl3j+dvr4RcVpE3F9efz0i1nwP20uSqjW42CsgSZoXlwH/IDNPj4gPAP8S+GHgeOCBiLin9PloZr5/hjo/A/xJZp4OEBHLI+Jo4D3AKE1g/grwXGa+FBG/B1yTmTdGxMV9rOeZwFrgb4EvAP8MuLWM9SaaW63/u8y8MyL2dK5vRPx34Pcy848iYhhozWH7SNIbhkfQJenQ827gc5k5lZnfBv4P8A9fZ63/C7wL+HHgP5Xn9wB/Uea/C/hcmb6pj3r3Z+aTmTlVlnt3aR8CvgT828y8s8ey9wK/GRGXAt+fmS/P9YeRpDcCA7okHXpiHmv9BU0g/37gdpqj8u8G7unok3OoN73v/teTwIPAOT0XzPwszRH+l4E7IuKsOYwrSW8YBnRJOjS8ABxVpu8BPhgRrYhYRXPU+/5pffp1D/ALwOOZ2QZ2Au8DvlrmfxXYUKY/3Ee9M8t56wPAB4GvlPYEfgn4wYi4rMvPRES8FXgyMz9JcyrM2+f4s0jSG4IBXZIOAZn5HeCrEfEo8GPA14GvAXfRnDbyd6VtMiK+1u8lFDPz6TK5/4j5V4DnM3NXef1rwMUR8QBwTB8l76X58uejwFPAbR1jTdGE/Z8sl4ycvr4fBB6NiIeBHwRu7OdnkKQ3msicy18mJUl6fSLiJ5j9S6qStOR5BF2SJEmqiEfQJWkJiohzgI3Tmp/KzJ+dh9pv47VXdNmXmT/6vdaWpKXAgC5JkiRVxFNcJEmSpIoY0CVJkqSKGNAlSZKkihjQJUmSpIoY0CVJkqSK/H/OzdHliaooHAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"K2 All:\\n\",\n \"Ks_2sampResult(statistic=0.08538442029661275, pvalue=0.0)\\n\",\n \"K2 Benign:\\n\",\n \"Ks_2sampResult(statistic=0.1638849062280222, pvalue=0.0)\\n\",\n \"K2 Attack:\\n\",\n \"Ks_2sampResult(statistic=0.3708222970259676, pvalue=0.0)\\n\",\n \"\\n\",\n \" All 2018 All 2017 Benign 2018 Benign 2017 \\\\\\n\",\n \" 0 0 0 0 \\n\",\n \"nobs 1.623294e+07 2.830743e+06 1.348471e+07 2.273097e+06 \\n\",\n \"min 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 \\n\",\n \"max 3.096290e+05 2.197590e+05 1.213090e+05 2.197590e+05 \\n\",\n \"mean 2.353319e+01 9.361160e+00 6.051862e+00 1.065417e+01 \\n\",\n \"median 2.000000e+00 2.000000e+00 2.000000e+00 2.000000e+00 \\n\",\n \"std 1.521137e+03 7.496727e+02 7.640556e+01 8.365354e+02 \\n\",\n \"variance 2.313857e+06 5.620093e+05 5.837811e+03 6.997918e+05 \\n\",\n \"skewness 8.855637e+01 2.443804e+02 4.677944e+02 2.190286e+02 \\n\",\n \"kurtosis 8.271322e+03 6.175432e+04 5.486641e+05 4.960005e+04 \\n\",\n \"\\n\",\n \" Attack 2018 Attack 2017 \\n\",\n \" 0 0 \\n\",\n \"nobs 2.748235e+06 557646.000000 \\n\",\n \"min 1.000000e+00 1.000000 \\n\",\n \"max 3.096290e+05 5523.000000 \\n\",\n \"mean 1.093085e+02 4.090532 \\n\",\n \"median 2.000000e+00 3.000000 \\n\",\n \"std 3.691844e+03 18.457361 \\n\",\n \"variance 1.362972e+07 340.674803 \\n\",\n \"skewness 3.649792e+01 182.268030 \\n\",\n \"kurtosis 1.401672e+03 38252.596037 \\n\",\n \"\\n\",\n \"\\n\",\n \"Distribution for field 'fwd_iat_tot'\\n\",\n \"===================================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"K2 All:\\n\",\n \"Ks_2sampResult(statistic=0.24261434033321583, pvalue=0.0)\\n\",\n \"K2 Benign:\\n\",\n \"Ks_2sampResult(statistic=0.2600580236392939, pvalue=0.0)\\n\",\n \"K2 Attack:\\n\",\n \"Ks_2sampResult(statistic=0.29382240416452743, pvalue=0.0)\\n\",\n \"\\n\",\n \" All 2018 All 2017 Benign 2018 Benign 2017 \\\\\\n\",\n \" 0 0 0 0 \\n\",\n \"nobs 1.623294e+07 2.830743e+06 1.348471e+07 2.273097e+06 \\n\",\n \"min -9.190110e+11 0.000000e+00 -9.190110e+11 0.000000e+00 \\n\",\n \"max 1.200000e+08 1.200000e+08 1.200000e+08 1.200000e+08 \\n\",\n \"mean 1.152313e+07 1.448296e+07 1.314415e+07 1.089969e+07 \\n\",\n \"median 5.140000e+03 4.300000e+01 1.766500e+04 4.000000e+00 \\n\",\n \"std 4.940071e+08 3.357581e+07 5.419665e+08 2.999284e+07 \\n\",\n \"variance 2.440431e+17 1.127335e+15 2.937277e+17 8.995711e+14 \\n\",\n \"skewness -1.434865e+03 2.176929e+00 -1.308134e+03 2.814193e+00 \\n\",\n \"kurtosis 2.172232e+06 3.107538e+00 1.805130e+06 6.495182e+00 \\n\",\n \"\\n\",\n \" Attack 2018 Attack 2017 \\n\",\n \" 0 0 \\n\",\n \"nobs 2.748235e+06 5.576460e+05 \\n\",\n \"min 0.000000e+00 0.000000e+00 \\n\",\n \"max 1.200000e+08 1.200000e+08 \\n\",\n \"mean 3.569303e+06 2.908923e+07 \\n\",\n \"median 1.988000e+03 8.015000e+03 \\n\",\n \"std 1.342350e+07 4.230926e+07 \\n\",\n \"variance 1.801903e+14 1.790077e+15 \\n\",\n \"skewness 5.723194e+00 8.823353e-01 \\n\",\n \"kurtosis 3.871126e+01 -1.129873e+00 \\n\",\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for f in features_to_analyze:\\n\",\n \" print_distributions(X, y, X_novel, y_novel, f, '2018', '2017')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The plots and the Kolmogorov-Smirnov statistics suggest that all features come from a different distribution in both datasets which poses a problem for our estimator as the estimator assumes that train, test and real-worl data stems from the same distribution.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 5.5. Statistical tests\\n\",\n \"\\n\",\n \"To rule out that the estimator uses features that are only relevant for the training dataset and not relevant for the novel dataset the next step performs the [Kolmogorov-Smirnov statistic](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html) on all the features of the datasets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from scipy.stats import ks_2samp\\n\",\n \"\\n\",\n \"def ks2(x1, x2, f):\\n\",\n \" return ks_2samp(x1[f], x2[f]).pvalue\\n\",\n \"\\n\",\n \"\\n\",\n \"df = load_dataset(csv_files, types)\\n\",\n \"X_2018 = df.drop(columns=['label', 'label_cat', 'label_is_attack', 'timestamp'])\\n\",\n \"y_2018 = df[['label_is_attack', 'label_cat', 'label']]\\n\",\n \"\\n\",\n \"df_2017 = load_dataset(novel_files, types)\\n\",\n \"X_2017 = df_2017.drop(columns=['label', 'label_cat', 'label_is_attack', 'timestamp'])\\n\",\n \"y_2017 = df_2017[['label_is_attack', 'label_cat', 'label']]\\n\",\n \"\\n\",\n \"X_2018_attack = X_2018[(y.label_is_attack == 1)]\\n\",\n \"X_2018_benign = X_2018[(y.label_is_attack == 0)]\\n\",\n \"\\n\",\n \"X_2017_attack = X_2017[(y_2017.label_is_attack == 1)]\\n\",\n \"X_2017_benign = X_2017[(y_2017.label_is_attack == 0)]\\n\",\n \"\\n\",\n \"k2_results = []\\n\",\n \"\\n\",\n \"for c in X_2018.columns: \\n\",\n \" print(f\\\"Processing {c}...\\\")\\n\",\n \" r = pd.DataFrame.from_dict(data = { \\n\",\n \" 'all': ks2(X_2018, X_2017, c),\\n\",\n \" 'attack': ks2(X_2018_benign, X_2017_benign, c),\\n\",\n \" 'benign': ks2(X_2018_attack, X_2017_attack, c)\\n\",\n \" }, orient='index', columns=[c])\\n\",\n \" k2_results.append(r)\\n\",\n \" \\n\",\n \"k2_values = pd.concat(k2_results, axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
allattackbenign
fwd_urg_flags1.01.00.999988
cwe_flag_count1.01.00.999988
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" all attack benign\\n\",\n \"fwd_urg_flags 1.0 1.0 0.999988\\n\",\n \"cwe_flag_count 1.0 1.0 0.999988\"\n ]\n },\n \"execution_count\": 58,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"k2 = k2_values.drop(columns=features_no_variance).T\\n\",\n \"k2[(k2['all'] > 0.005) | (k2['attack'] > 0.005) | (k2['benign'] > 0.005)]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"After removing the feature with no variance there are only 2 features left that stem from the same distribution in both dataset `fwd_urg_flags`, `cwe_flag_count` both not being good predictors. \\n\",\n \"This shows that data from different network environment is distributed differently.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6. Model from combined data\\n\",\n \"\\n\",\n \"In order to create a estimator that generalizes well on data from different network environments an estimator is created by using the combined [CIC-IDS-2017](https://www.unb.ca/cic/datasets/ids-2017.html) and [CIC-IDS-2018](https://www.unb.ca/cic/datasets/ids-2018.html) datasets.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.1. Load combined data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"combined_files = glob.glob(os.path.join(dataset_base_path, '*.csv')) + glob.glob(os.path.join(novel_base_path, '*.csv'))\\n\",\n \"\\n\",\n \"df = load_dataset(combined_files, types, cols=selected_features + ['label'])\\n\",\n \"X = df.drop(columns=['label', 'label_cat', 'label_is_attack'])\\n\",\n \"y = df[['label_is_attack', 'label_cat', 'label']]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y.label_cat)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.2. Synthetic Oversampling\\n\",\n \"\\n\",\n \"Both datasets contain attack classes with a low amount of occurrences. In order to get a better detection rate for these attacks `Synthetic Minority Oversampling` is used to push the occurrences of those classes to `100000`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Benign 12606243\\n\",\n \"DoS attacks-Hulk 554388\\n\",\n \"DDOS attack-HOIC 548809\\n\",\n \"DDoS attacks-LOIC-HTTP 460953\\n\",\n \"Bot 230526\\n\",\n \"FTP-BruteForce 161038\\n\",\n \"SSH-Bruteforce 154789\\n\",\n \"Infilteration 129576\\n\",\n \"PortScan 127144\\n\",\n \"DoS attacks-SlowHTTPTest 116311\\n\",\n \"DDOS LOIT 102422\\n\",\n \"DoS attacks-GoldenEye 41441\\n\",\n \"DoS attacks-Slowloris 13429\\n\",\n \"Brute Force -Web 1694\\n\",\n \"DDOS attack-LOIC-UDP 1384\\n\",\n \"Brute Force -XSS 706\\n\",\n \"SQL Injection 86\\n\",\n \"Heartbleed 9\\n\",\n \"Name: label, dtype: int64\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_train.label.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cnts = y_train.label_cat.value_counts()\\n\",\n \"sample_dict = {}\\n\",\n \"\\n\",\n \"for i in np.unique(y_train.label_cat):\\n\",\n \" sample_dict[i] = max(cnts[i], 100000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sm = SMOTENC(sampling_strategy=sample_dict, categorical_features=[0], n_jobs=24)\\n\",\n \"X_train_s, y_train_s = sm.fit_resample(X_train, y_train.label_cat)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Class occurrences:\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"Counter({12: 161038,\\n\",\n \" 0: 12606243,\\n\",\n \" 5: 548809,\\n\",\n \" 9: 554388,\\n\",\n \" 7: 460953,\\n\",\n \" 17: 154789,\\n\",\n \" 11: 100000,\\n\",\n \" 1: 230526,\\n\",\n \" 10: 116311,\\n\",\n \" 4: 102422,\\n\",\n \" 8: 100000,\\n\",\n \" 14: 129576,\\n\",\n \" 15: 127144,\\n\",\n \" 6: 100000,\\n\",\n \" 2: 100000,\\n\",\n \" 3: 100000,\\n\",\n \" 16: 100000,\\n\",\n \" 13: 100000})\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"print('Class occurrences:')\\n\",\n \"Counter(y_train_s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Binary label occurrences:\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"Counter({1: 3285956, 0: 12606243})\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_train_s = (y_train_s != 0).astype('int')\\n\",\n \"print('Binary label occurrences:')\\n\",\n \"Counter(y_train_s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 6.3. Training\\n\",\n \"\\n\",\n \"For the combined estimator a gradient boosting model is trained using grid-search to find the best set of hyperparameters.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def train_catboost_grid(train_pool, y_train):\\n\",\n \" minority_class_weight = len(y_train[y_train == 0]) / len(y_train[y_train == 1])\\n\",\n \" print(f\\\"Minority class weight: {minority_class_weight}\\\")\\n\",\n \" \\n\",\n \" param_grid = {\\n\",\n \" 'iterations': [500, 1000, 1500],\\n\",\n \" 'learning_rate': [0.3, 0.03, 0.003],\\n\",\n \" 'depth': [3, 6, 9],\\n\",\n \" 'l2_leaf_reg': [3, 6]\\n\",\n \" }\\n\",\n \"\\n\",\n \" cls = CatBoostClassifier(loss_function='Logloss',\\n\",\n \" eval_metric='Recall',\\n\",\n \" class_weights=[1, minority_class_weight],\\n\",\n \" task_type='GPU',\\n\",\n \" verbose=True)\\n\",\n \"\\n\",\n \" cb_results = cls.grid_search(param_grid, train_pool)\\n\",\n \"\\n\",\n \" print('\\\\nOptimal Parameters:')\\n\",\n \" print(cb_results['params'])\\n\",\n \" return cls\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_pool = Pool(X_train_s, y_train_s, cat_features=[0])\\n\",\n \"test_pool = Pool(X_test.values, cat_features=[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Minority class weight: 3.8364004265425344\\n\",\n \"0:\\tloss: 0.9676589\\tbest: 0.9676589 (0)\\ttotal: 2m 14s\\tremaining: 1h 58m 55s\\n\",\n \"1:\\tloss: 0.9634058\\tbest: 0.9676589 (0)\\ttotal: 4m 18s\\tremaining: 1h 52m 4s\\n\",\n \"2:\\tloss: 0.9516361\\tbest: 0.9676589 (0)\\ttotal: 6m 25s\\tremaining: 1h 49m 16s\\n\",\n \"3:\\tloss: 0.9674853\\tbest: 0.9676589 (0)\\ttotal: 8m 29s\\tremaining: 1h 46m 7s\\n\",\n \"4:\\tloss: 0.9636844\\tbest: 0.9676589 (0)\\ttotal: 10m 33s\\tremaining: 1h 43m 30s\\n\",\n \"5:\\tloss: 0.9516361\\tbest: 0.9676589 (0)\\ttotal: 12m 41s\\tremaining: 1h 41m 28s\\n\",\n \"6:\\tloss: 0.9687036\\tbest: 0.9687036 (6)\\ttotal: 16m 39s\\tremaining: 1h 51m 53s\\n\",\n \"7:\\tloss: 0.9658026\\tbest: 0.9687036 (6)\\ttotal: 20m 39s\\tremaining: 1h 58m 47s\\n\",\n \"8:\\tloss: 0.9516102\\tbest: 0.9687036 (6)\\ttotal: 24m 41s\\tremaining: 2h 3m 28s\\n\",\n \"9:\\tloss: 0.9685558\\tbest: 0.9687036 (6)\\ttotal: 28m 40s\\tremaining: 2h 6m 9s\\n\",\n \"10:\\tloss: 0.9656976\\tbest: 0.9687036 (6)\\ttotal: 32m 40s\\tremaining: 2h 7m 43s\\n\",\n \"11:\\tloss: 0.9516361\\tbest: 0.9687036 (6)\\ttotal: 36m 42s\\tremaining: 2h 8m 29s\\n\",\n \"12:\\tloss: 0.9689914\\tbest: 0.9689914 (12)\\ttotal: 42m 32s\\tremaining: 2h 14m 11s\\n\",\n \"13:\\tloss: 0.9659899\\tbest: 0.9689914 (12)\\ttotal: 48m 28s\\tremaining: 2h 18m 30s\\n\",\n \"14:\\tloss: 0.9516102\\tbest: 0.9689914 (12)\\ttotal: 54m 26s\\tremaining: 2h 21m 33s\\n\",\n \"15:\\tloss: 0.9689167\\tbest: 0.9689914 (12)\\ttotal: 1h 17s\\tremaining: 2h 23m 11s\\n\",\n \"16:\\tloss: 0.9659214\\tbest: 0.9689914 (12)\\ttotal: 1h 6m 14s\\tremaining: 2h 24m 10s\\n\",\n \"17:\\tloss: 0.9516346\\tbest: 0.9689914 (12)\\ttotal: 1h 12m 12s\\tremaining: 2h 24m 24s\\n\",\n \"18:\\tloss: 0.9696812\\tbest: 0.9696812 (18)\\ttotal: 1h 15m 42s\\tremaining: 2h 19m 28s\\n\",\n \"19:\\tloss: 0.9665199\\tbest: 0.9696812 (18)\\ttotal: 1h 19m 19s\\tremaining: 2h 14m 51s\\n\",\n \"20:\\tloss: 0.9584917\\tbest: 0.9696812 (18)\\ttotal: 1h 23m 8s\\tremaining: 2h 10m 38s\\n\",\n \"21:\\tloss: 0.9697147\\tbest: 0.9697147 (21)\\ttotal: 1h 26m 42s\\tremaining: 2h 6m 7s\\n\",\n \"22:\\tloss: 0.9665336\\tbest: 0.9697147 (21)\\ttotal: 1h 30m 19s\\tremaining: 2h 1m 44s\\n\",\n \"23:\\tloss: 0.9494265\\tbest: 0.9697147 (21)\\ttotal: 1h 34m 7s\\tremaining: 1h 57m 39s\\n\",\n \"24:\\tloss: 0.9698761\\tbest: 0.9698761 (24)\\ttotal: 1h 40m 41s\\tremaining: 1h 56m 48s\\n\",\n \"25:\\tloss: 0.9674335\\tbest: 0.9698761 (24)\\ttotal: 1h 47m 41s\\tremaining: 1h 55m 58s\\n\",\n \"26:\\tloss: 0.9629870\\tbest: 0.9698761 (24)\\ttotal: 1h 55m 4s\\tremaining: 1h 55m 4s\\n\",\n \"27:\\tloss: 0.9697589\\tbest: 0.9698761 (24)\\ttotal: 2h 1m 53s\\tremaining: 1h 53m 11s\\n\",\n \"28:\\tloss: 0.9673072\\tbest: 0.9698761 (24)\\ttotal: 2h 8m 53s\\tremaining: 1h 51m 6s\\n\",\n \"29:\\tloss: 0.9624281\\tbest: 0.9698761 (24)\\ttotal: 2h 16m 15s\\tremaining: 1h 49m\\n\",\n \"30:\\tloss: 0.9698868\\tbest: 0.9698868 (30)\\ttotal: 2h 26m 2s\\tremaining: 1h 48m 20s\\n\",\n \"31:\\tloss: 0.9683244\\tbest: 0.9698868 (30)\\ttotal: 2h 36m 29s\\tremaining: 1h 47m 35s\\n\",\n \"32:\\tloss: 0.9644397\\tbest: 0.9698868 (30)\\ttotal: 2h 47m 20s\\tremaining: 1h 46m 29s\\n\",\n \"33:\\tloss: 0.9698685\\tbest: 0.9698868 (30)\\ttotal: 2h 57m 21s\\tremaining: 1h 44m 19s\\n\",\n \"34:\\tloss: 0.9683472\\tbest: 0.9698868 (30)\\ttotal: 3h 7m 47s\\tremaining: 1h 41m 56s\\n\",\n \"35:\\tloss: 0.9645524\\tbest: 0.9698868 (30)\\ttotal: 3h 18m 38s\\tremaining: 1h 39m 19s\\n\",\n \"36:\\tloss: 0.9700238\\tbest: 0.9700238 (36)\\ttotal: 3h 25m 29s\\tremaining: 1h 34m 24s\\n\",\n \"37:\\tloss: 0.9678127\\tbest: 0.9700238 (36)\\ttotal: 3h 32m 38s\\tremaining: 1h 29m 31s\\n\",\n \"38:\\tloss: 0.9628971\\tbest: 0.9700238 (36)\\ttotal: 3h 40m 25s\\tremaining: 1h 24m 46s\\n\",\n \"39:\\tloss: 0.9701121\\tbest: 0.9701121 (39)\\ttotal: 3h 47m 24s\\tremaining: 1h 19m 35s\\n\",\n \"40:\\tloss: 0.9677960\\tbest: 0.9701121 (39)\\ttotal: 3h 54m 32s\\tremaining: 1h 14m 22s\\n\",\n \"41:\\tloss: 0.9620581\\tbest: 0.9701121 (39)\\ttotal: 4h 2m 18s\\tremaining: 1h 9m 13s\\n\",\n \"42:\\tloss: 0.9701319\\tbest: 0.9701319 (42)\\ttotal: 4h 14m 50s\\tremaining: 1h 5m 11s\\n\",\n \"43:\\tloss: 0.9691315\\tbest: 0.9701319 (42)\\ttotal: 4h 28m 58s\\tremaining: 1h 1m 7s\\n\",\n \"44:\\tloss: 0.9653747\\tbest: 0.9701319 (42)\\ttotal: 4h 44m 7s\\tremaining: 56m 49s\\n\",\n \"45:\\tloss: 0.9701091\\tbest: 0.9701319 (42)\\ttotal: 4h 57m 34s\\tremaining: 51m 45s\\n\",\n \"46:\\tloss: 0.9691893\\tbest: 0.9701319 (42)\\ttotal: 5h 11m 39s\\tremaining: 46m 25s\\n\",\n \"47:\\tloss: 0.9654905\\tbest: 0.9701319 (42)\\ttotal: 5h 26m 46s\\tremaining: 40m 50s\\n\",\n \"48:\\tloss: 0.9702081\\tbest: 0.9702081 (48)\\ttotal: 5h 44m 16s\\tremaining: 35m 7s\\n\",\n \"49:\\tloss: 0.9695304\\tbest: 0.9702081 (48)\\ttotal: 6h 5m 19s\\tremaining: 29m 13s\\n\",\n \"50:\\tloss: 0.9664422\\tbest: 0.9702081 (48)\\ttotal: 6h 27m 35s\\tremaining: 22m 47s\\n\",\n \"51:\\tloss: 0.9701243\\tbest: 0.9702081 (48)\\ttotal: 6h 47m 3s\\tremaining: 15m 39s\\n\",\n \"52:\\tloss: 0.9696553\\tbest: 0.9702081 (48)\\ttotal: 7h 8m 9s\\tremaining: 8m 4s\\n\",\n \"53:\\tloss: 0.9664727\\tbest: 0.9702081 (48)\\ttotal: 7h 30m 24s\\tremaining: 0us\\n\",\n \"Estimating final quality...\\n\",\n \"\\n\",\n \"Optimal Parameters:\\n\",\n \"{'depth': 9, 'l2_leaf_reg': 3, 'iterations': 1500, 'learning_rate': 0.3}\\n\"\n ]\n }\n ],\n \"source\": [\n \"comb_estimator = train_catboost_grid(train_pool, y_train_s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Conducting grid-search the following parameters were determined for an optimal estimator:\\n\",\n \"\\n\",\n \"|Parameter|Value|\\n\",\n \"|---------|-----|\\n\",\n \"|Depth|9|\\n\",\n \"|L2 Leaf Regularization|3|\\n\",\n \"|Iterations|1000|\\n\",\n \"|Learning Rate|0.3|\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Train):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.99 0.99 0.99 12606243\\n\",\n \" 1 0.98 0.97 0.97 3285956\\n\",\n \"\\n\",\n \" accuracy 0.99 15892199\\n\",\n \" macro avg 0.99 0.98 0.98 15892199\\n\",\n \"weighted avg 0.99 0.99 0.99 15892199\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.955579742683623\\n\",\n \"\\n\",\n \"\\n\",\n \"Classification Report (Test):\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.99 0.99 0.99 3151562\\n\",\n \" 1 0.97 0.96 0.97 661176\\n\",\n \"\\n\",\n \" accuracy 0.99 3812738\\n\",\n \" macro avg 0.98 0.98 0.98 3812738\\n\",\n \"weighted avg 0.99 0.99 0.99 3812738\\n\",\n \"\\n\",\n \"Avg Precision Score: 0.9425657531936107\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print_report('Train', comb_estimator, train_pool, y_train_s, comb_estimator.predict(train_pool))\\n\",\n \"print_report('Test', comb_estimator, test_pool, y_test.label_is_attack, comb_estimator.predict(test_pool), plot_pr=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The combined estimator shows promising performance on the test dataset with a high recall of `0.99`, precision of `0.99` and an attack detection rate (recall class 1) of `0.96`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"comb_estimator.save_model('models/gradient_boost_model')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7. Model analysis\\n\",\n \"\\n\",\n \"The following figures show the feature importance calculated with the SHAP values and illustrate the influence of the most important predictor features.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"shap.initjs()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"shap_values = comb_estimator.get_feature_importance(test_pool, type='ShapValues')\\n\",\n \"\\n\",\n \"expected_value = shap_values[0,-1]\\n\",\n \"shap_values = shap_values[:,:-1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.1. Feature importance\\n\",\n \"\\n\",\n \"The following listing shows the feature importances of the combined estimator calculated with permuation importance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"shap.summary_plot(shap_values, X_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"According to SHAP analysis the following features have the highest influence on the predictions of the model:\\n\",\n \"1. `init_fwd_win_byts`: Number of bytes sent in initial window in the forward direction\\n\",\n \"2. `fwd_pkt_len_mean`: Mean size of packet in forward direction\\n\",\n \"3. `protocol`: Protocol\\n\",\n \"4. `init_bwd_win_byts`: Number of bytes sent in initial window in the backward direction\\n\",\n \"5. `fwd_seg_size_min`: Minimum segment size observed in the forward direction\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.2 Force Plot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"
\\n\",\n \"
\\n\",\n \" Visualization omitted, Javascript library not loaded!
\\n\",\n \" Have you run `initjs()` in this notebook? If this notebook was from another\\n\",\n \" user you must also trust this notebook (File -> Trust notebook). If you are viewing\\n\",\n \" this notebook on github the Javascript has been stripped for security. If you are using\\n\",\n \" JupyterLab this error is because a JupyterLab extension has not yet been written.\\n\",\n \"
\\n\",\n \" \"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"idx = np.random.choice(np.arange(len(X_test)), 1000, replace=False)\\n\",\n \"shap.force_plot(expected_value, shap_values[idx], X_test.iloc[idx, :])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 7.3 Depedence Plots\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"shap.dependence_plot('init_fwd_win_byts', shap_values, X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"shap.dependence_plot('fwd_pkt_len_mean', shap_values, X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"shap.dependence_plot('protocol', shap_values, X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"shap.dependence_plot('init_bwd_win_byts', shap_values, X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"shap.dependence_plot('fwd_seg_size_min', shap_values, X_test)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 8. Conclusion\\n\",\n \"\\n\",\n \"Even though the performance of the combined estimator is compelling it can be assumed that the estimator will not generalize well to different network environments because of the observed differences in distributions. The statistical features recorded in the single datasets seem to be highly dependent on the network topology and the configurations of host and client machines in the network. [Machine Learning for Networking](https://arxiv.org/ftp/arxiv/papers/1709/1709.08339.pdf) discusses the difficulties imposed to machine learning systems by the heterogeneity of different network environments.\\n\",\n \"\\n\",\n \"In order to cope with this problem the following suggestions are proposed:\\n\",\n \"* an estimator has to be trained with more varied data originating from different network environments,\\n\",\n \"* an estimator has to be trained with data originating from the network environment it will be deployed in.\\n\",\n \"\\n\",\n \"The second option seems more promising as it is very difficult to obtain real-world network attack datasets of high quality. \\n\",\n \"The fact that data has to be collected in the target environment can be alleviated by only collecting benign network traffic and using an anomaly detection approach to detect network attacks. This option will be pursued in further work using neural networks.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/03_ml-prototype/models/gradient_boost_model.cbm", "content": "version https://git-lfs.github.com/spec/v1\noid sha256:f9ff34d59ef5e2a1040b921b0b1d7565c63e4fd8d9bf4d080cf31a5e9ee13fc0\nsize 14315968\n" }, { "path": "notebooks/04_ml-prototype-spark/ml-prototype-spark.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Scaled ML Prototype\\n\",\n \"\\n\",\n \"This notebook implements the scaled version of the ML Prototype created in the previous notebook. \\n\",\n \"\\n\",\n \"The scaled version of the ML Prototype facilitates PySpark to create a [Pipeline](https://spark.apache.org/docs/latest/api/java/index.html?org/apache/spark/ml/Pipeline.html) cleaning the dataset and to train a [GBTClassifier](https://spark.apache.org/docs/latest/api/java/index.html?org/apache/spark/ml/classification/GBTClassifier.html) using grid-search. \\n\",\n \"Both the pipeline and the classifier are serialized to disk for further usage. \\n\",\n \"\\n\",\n \"This notebook is separated into four parts:\\n\",\n \"1. Common code: Contains the common code used in all other parts of the notebook.\\n\",\n \"2. Dataset creation: The [CIC-IDS-2018](https://drive.google.com/open?id=1HrTPh0YRSZ4T9DLa_c47lubheKUcPl0r) and [CIC-IDS-2017](https://drive.google.com/open?id=1Q2J_pPB0K0PHjq0YO5BPwYQwrvoZgYqo) datasets are mixed and split into training and holdout datasets. The training dataset is used to train and evaluate a classifier whereas the holdout dataset may be used to utilise the classifier in practice in absence of further real-world data.\\n\",\n \"3. Model training: In this section the pipeline to clean and impute the data is created and a GBTClassifier is trained on the data via grid-search. The pipeline and the classifier are serialized to disk as a last step.\\n\",\n \"4. Model Usage: This part of the notebook demonstrates the usage of the classifier. Both the pipeline and the classifier are read from disk to perform predictions on the holdout dataset. This section can be used as a basis for a separate driver program.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Common code\\n\",\n \"\\n\",\n \"The following section contains the common code used in the later parts of the notebook.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Base Paths\\n\",\n \"dataset_path = r'/home/glados/Development/Projects/ids-201?/processed/*.csv'\\n\",\n \"spark_output_path = r'/home/glados/Development/Projects/ids-spark/'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from pyspark.sql.session import SparkSession\\n\",\n \"from pyspark.sql.types import StructType, StructField, ShortType, IntegerType, LongType, FloatType, DoubleType, TimestampType, StringType\\n\",\n \"from pyspark.sql.functions import count, when, col\\n\",\n \"from pyspark.ml import Pipeline, Transformer, PipelineModel\\n\",\n \"from pyspark.ml.param.shared import HasOutputCols, HasInputCols, Param, Params\\n\",\n \"from pyspark.ml.util import DefaultParamsReadable, DefaultParamsWritable\\n\",\n \"from pyspark.ml.feature import Imputer, OneHotEncoderEstimator, StringIndexer, VectorAssembler\\n\",\n \"from pyspark.ml.classification import GBTClassifier, GBTClassificationModel\\n\",\n \"from pyspark.ml.tuning import CrossValidatorModel\\n\",\n \"from pyspark.ml.evaluation import BinaryClassificationEvaluator, MulticlassClassificationEvaluator\\n\",\n \"from pyspark.mllib.evaluation import BinaryClassificationMetrics, MulticlassMetrics\\n\",\n \"from pyspark.ml.tuning import ParamGridBuilder, CrossValidator\\n\",\n \"from functools import reduce\\n\",\n \"import os\\n\",\n \"import findspark\\n\",\n \"\\n\",\n \"findspark.init()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"training_path = os.path.join(spark_output_path, 'training')\\n\",\n \"holdout_path = os.path.join(spark_output_path, 'holdout')\\n\",\n \"pipeline_model_path = 'models/spark/pipeline-model'\\n\",\n \"gb_model_path = 'models/spark/gb-model'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.1 Features\\n\",\n \"\\n\",\n \"The following features were used to train the ML Prototype (LINK) and are subsequently used to train the GBTClassifier.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"selected_features = [\\n\",\n \" 'protocol',\\n\",\n \" 'flow_duration',\\n\",\n \" 'tot_fwd_pkts',\\n\",\n \" 'tot_bwd_pkts',\\n\",\n \" 'totlen_fwd_pkts',\\n\",\n \" 'totlen_bwd_pkts',\\n\",\n \" 'fwd_pkt_len_mean',\\n\",\n \" 'fwd_pkt_len_std',\\n\",\n \" 'bwd_pkt_len_mean',\\n\",\n \" 'flow_byts_s',\\n\",\n \" 'flow_pkts_s',\\n\",\n \" 'flow_iat_std',\\n\",\n \" 'flow_iat_min',\\n\",\n \" 'fwd_iat_tot',\\n\",\n \" 'fwd_iat_min',\\n\",\n \" 'bwd_iat_tot',\\n\",\n \" 'bwd_iat_min',\\n\",\n \" 'fwd_psh_flags',\\n\",\n \" 'fwd_urg_flags',\\n\",\n \" 'bwd_pkts_s',\\n\",\n \" 'fin_flag_cnt',\\n\",\n \" 'rst_flag_cnt',\\n\",\n \" 'psh_flag_cnt',\\n\",\n \" 'ack_flag_cnt',\\n\",\n \" 'urg_flag_cnt',\\n\",\n \" 'down_up_ratio',\\n\",\n \" 'init_fwd_win_byts',\\n\",\n \" 'init_bwd_win_byts',\\n\",\n \" 'fwd_seg_size_min',\\n\",\n \" 'active_mean',\\n\",\n \" 'idle_mean'\\n\",\n \"]\\n\",\n \"\\n\",\n \"selected_columns = selected_features + ['label']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.2 Schema\\n\",\n \"\\n\",\n \"The following contains the schema of the complete dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"schema = StructType([\\n\",\n \" StructField('dst_port', IntegerType()),\\n\",\n \" StructField('protocol', IntegerType()),\\n\",\n \" StructField('timestamp', StringType()),\\n\",\n \" StructField('flow_duration', LongType()),\\n\",\n \" StructField('tot_fwd_pkts', IntegerType()),\\n\",\n \" StructField('tot_bwd_pkts', IntegerType()),\\n\",\n \" StructField('totlen_fwd_pkts', DoubleType()),\\n\",\n \" StructField('totlen_bwd_pkts', DoubleType()),\\n\",\n \" StructField('fwd_pkt_len_max', DoubleType()),\\n\",\n \" StructField('fwd_pkt_len_min', DoubleType()),\\n\",\n \" StructField('fwd_pkt_len_mean', DoubleType()),\\n\",\n \" StructField('fwd_pkt_len_std', DoubleType()),\\n\",\n \" StructField('bwd_pkt_len_max', DoubleType()),\\n\",\n \" StructField('bwd_pkt_len_min', DoubleType()),\\n\",\n \" StructField('bwd_pkt_len_mean', DoubleType()),\\n\",\n \" StructField('bwd_pkt_len_std', DoubleType()),\\n\",\n \" StructField('flow_byts_s', StringType()),\\n\",\n \" StructField('flow_pkts_s', StringType()),\\n\",\n \" StructField('flow_iat_mean', DoubleType()),\\n\",\n \" StructField('flow_iat_std', DoubleType()),\\n\",\n \" StructField('flow_iat_max', DoubleType()),\\n\",\n \" StructField('flow_iat_min', DoubleType()),\\n\",\n \" StructField('fwd_iat_tot', DoubleType()),\\n\",\n \" StructField('fwd_iat_mean', DoubleType()),\\n\",\n \" StructField('fwd_iat_std', DoubleType()),\\n\",\n \" StructField('fwd_iat_max', DoubleType()),\\n\",\n \" StructField('fwd_iat_min', DoubleType()),\\n\",\n \" StructField('bwd_iat_tot', DoubleType()),\\n\",\n \" StructField('bwd_iat_mean', DoubleType()),\\n\",\n \" StructField('bwd_iat_std', DoubleType()),\\n\",\n \" StructField('bwd_iat_max', DoubleType()),\\n\",\n \" StructField('bwd_iat_min', DoubleType()),\\n\",\n \" StructField('fwd_psh_flags', IntegerType()),\\n\",\n \" StructField('bwd_psh_flags', IntegerType()),\\n\",\n \" StructField('fwd_urg_flags', IntegerType()),\\n\",\n \" StructField('bwd_urg_flags', IntegerType()),\\n\",\n \" StructField('fwd_header_len', LongType()),\\n\",\n \" StructField('bwd_header_len', IntegerType()),\\n\",\n \" StructField('fwd_pkts_s', DoubleType()),\\n\",\n \" StructField('bwd_pkts_s', DoubleType()),\\n\",\n \" StructField('pkt_len_min', DoubleType()),\\n\",\n \" StructField('pkt_len_max', DoubleType()),\\n\",\n \" StructField('pkt_len_mean', DoubleType()),\\n\",\n \" StructField('pkt_len_std', DoubleType()),\\n\",\n \" StructField('pkt_len_var', DoubleType()),\\n\",\n \" StructField('fin_flag_cnt', IntegerType()),\\n\",\n \" StructField('syn_flag_cnt', IntegerType()),\\n\",\n \" StructField('rst_flag_cnt', IntegerType()),\\n\",\n \" StructField('psh_flag_cnt', IntegerType()),\\n\",\n \" StructField('ack_flag_cnt', IntegerType()),\\n\",\n \" StructField('urg_flag_cnt', IntegerType()),\\n\",\n \" StructField('cwe_flag_count', IntegerType()),\\n\",\n \" StructField('ece_flag_cnt', IntegerType()),\\n\",\n \" StructField('down_up_ratio', DoubleType()),\\n\",\n \" StructField('pkt_size_avg', DoubleType()),\\n\",\n \" StructField('fwd_seg_size_avg', DoubleType()),\\n\",\n \" StructField('bwd_seg_size_avg', DoubleType()),\\n\",\n \" StructField('fwd_byts_b_avg', IntegerType()),\\n\",\n \" StructField('fwd_pkts_b_avg', IntegerType()),\\n\",\n \" StructField('fwd_blk_rate_avg', IntegerType()),\\n\",\n \" StructField('bwd_byts_b_avg', IntegerType()),\\n\",\n \" StructField('bwd_pkts_b_avg', IntegerType()),\\n\",\n \" StructField('bwd_blk_rate_avg', IntegerType()),\\n\",\n \" StructField('subflow_fwd_pkts', IntegerType()),\\n\",\n \" StructField('subflow_fwd_byts', IntegerType()),\\n\",\n \" StructField('subflow_bwd_pkts', IntegerType()),\\n\",\n \" StructField('subflow_bwd_byts', IntegerType()),\\n\",\n \" StructField('init_fwd_win_byts', IntegerType()),\\n\",\n \" StructField('init_bwd_win_byts', IntegerType()),\\n\",\n \" StructField('fwd_act_data_pkts', IntegerType()),\\n\",\n \" StructField('fwd_seg_size_min', IntegerType()),\\n\",\n \" StructField('active_mean', DoubleType()),\\n\",\n \" StructField('active_std', DoubleType()),\\n\",\n \" StructField('active_max', DoubleType()),\\n\",\n \" StructField('active_min', DoubleType()),\\n\",\n \" StructField('idle_mean', DoubleType()),\\n\",\n \" StructField('idle_std', DoubleType()),\\n\",\n \" StructField('idle_max', DoubleType()),\\n\",\n \" StructField('idle_min', DoubleType()),\\n\",\n \" StructField('label', StringType())\\n\",\n \"])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.3 Common classes and functions\\n\",\n \"\\n\",\n \"The following cell contains common classes and functions used in the later sections of the notebook.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class BinaryLabelMaker(Transformer, HasOutputCols, HasInputCols, DefaultParamsReadable, DefaultParamsWritable):\\n\",\n \" '''\\n\",\n \" A transformer that adds binary labels (0|1) based on the value of the input colums. \\n\",\n \" The \\\"classLabel\\\" parameter specifies the value of the input columns used to determine class 0.\\n\",\n \" '''\\n\",\n \" \\n\",\n \" classLabel = Param(Params._dummy(), 'classLabel', 'label for class 0')\\n\",\n \" \\n\",\n \" def __init__(self, inputCols=None, outputCols=None, classLabel=''):\\n\",\n \" super(BinaryLabelMaker, self).__init__()\\n\",\n \" self._set(inputCols=inputCols)\\n\",\n \" self._set(outputCols=outputCols)\\n\",\n \" self._set(classLabel=classLabel)\\n\",\n \"\\n\",\n \" def setClassLabel(self, classLabel):\\n\",\n \" return self._set(classLabel=classLabel)\\n\",\n \"\\n\",\n \" def getClassLabel(self):\\n\",\n \" return self.getOrDefault(self.classLabel)\\n\",\n \" \\n\",\n \" def _make_label(self, df, inputCol, outputCol):\\n\",\n \" return df.withColumn(outputCol, when(df[inputCol] == self.getClassLabel(), 0).otherwise(1).cast(DoubleType()))\\n\",\n \" \\n\",\n \" def _transform(self, df):\\n\",\n \" cols = zip(self.getInputCols(), self.getOutputCols())\\n\",\n \" return reduce(lambda acc, col: self._make_label(acc, col[0], col[1]), cols, df)\\n\",\n \" \\n\",\n \" \\n\",\n \"class ValueCleaner(Transformer, HasOutputCols, HasInputCols, DefaultParamsReadable, DefaultParamsWritable):\\n\",\n \" '''\\n\",\n \" A transformer that removes invalid values from the input columns.\\n\",\n \" Invalid values are \\\"inf\\\" and values < 0.\\n\",\n \" '''\\n\",\n \" \\n\",\n \" def __init__(self, inputCols=None, outputCols=None):\\n\",\n \" super(ValueCleaner, self).__init__()\\n\",\n \" self._set(inputCols=inputCols)\\n\",\n \" self._set(outputCols=outputCols)\\n\",\n \" \\n\",\n \" @staticmethod \\n\",\n \" def _replace_invalid_values(df, inputCol, outputCol, replacement): \\n\",\n \" return (df.withColumn(outputCol, \\n\",\n \" when(df[inputCol] == 'inf', replacement)\\n\",\n \" .when(df[inputCol] < 0.0, replacement)\\n\",\n \" .otherwise(df[inputCol])\\n\",\n \" .cast(DoubleType())\\n\",\n \" ))\\n\",\n \" \\n\",\n \" def _transform(self, df):\\n\",\n \" cols = zip(self.getInputCols(), self.getOutputCols())\\n\",\n \" return reduce(lambda acc, col: ValueCleaner._replace_invalid_values(acc, col[0], col[1], None), cols, df)\\n\",\n \" \\n\",\n \"\\n\",\n \"def load_csv(path, columns=None):\\n\",\n \" df = (spark.read\\n\",\n \" .schema(schema)\\n\",\n \" .option('inferSchema', 'false')\\n\",\n \" .option('header', 'true')\\n\",\n \" .option('sep', ',')\\n\",\n \" .csv(path))\\n\",\n \" \\n\",\n \" if columns:\\n\",\n \" return df.select(*columns)\\n\",\n \" else:\\n\",\n \" return df\\n\",\n \" \\n\",\n \"\\n\",\n \"def find_columns_to_impute(df):\\n\",\n \" col_values = df.select([count(when(col(c).isNull(), c).when(col(c) < 0.0, c).when(col(c) == 'inf', c)).alias(c) for c in df.columns]).collect()[0].asDict()\\n\",\n \" return [k for k, v in col_values.items() if v > 0]\\n\",\n \"\\n\",\n \"\\n\",\n \"def cat_column_name(c):\\n\",\n \" return f\\\"{c}_cat\\\"\\n\",\n \"\\n\",\n \"\\n\",\n \"def index_column_name(c):\\n\",\n \" return f\\\"{c}_idx\\\"\\n\",\n \"\\n\",\n \"\\n\",\n \"def imputed_column_name(c):\\n\",\n \" return f\\\"{c}_imputed\\\"\\n\",\n \"\\n\",\n \"\\n\",\n \"def print_classification_report(predictions, pred_col, label_col, report_type):\\n\",\n \" predictionAndLabels = predictions.select(pred_col, label_col).rdd\\n\",\n \" \\n\",\n \" binary_metrics = BinaryClassificationMetrics(predictionAndLabels)\\n\",\n \" multi_metrics = MulticlassMetrics(predictionAndLabels)\\n\",\n \" \\n\",\n \" evaluator = BinaryClassificationEvaluator(rawPredictionCol=\\\"rawPrediction\\\", labelCol=label_col)\\n\",\n \" aupr = evaluator.evaluate(predictions, {evaluator.metricName: \\\"areaUnderPR\\\"})\\n\",\n \" \\n\",\n \" print(f'Classification Report ({report_type}):')\\n\",\n \" print(f'Recall: {multi_metrics.weightedRecall}')\\n\",\n \" print(f'Precision: {multi_metrics.weightedPrecision}')\\n\",\n \" print(f'F1: {multi_metrics.weightedFMeasure()}')\\n\",\n \" print(f'FPR: {multi_metrics.weightedFalsePositiveRate}')\\n\",\n \" print(f'TPR: {multi_metrics.weightedTruePositiveRate}')\\n\",\n \" print()\\n\",\n \" print(f'Area under PR (raw predictions): {aupr}')\\n\",\n \" print(f'Area under PR: {binary_metrics.areaUnderPR}')\\n\",\n \" print(f'Accuracy = {multi_metrics.accuracy}')\\n\",\n \" print()\\n\",\n \" print('Confusion Matrix:')\\n\",\n \" print(multi_metrics.confusionMatrix())\\n\",\n \"\\n\",\n \" \\n\",\n \"def find_columns_with_value(df, value):\\n\",\n \" col_values = df.select([count(when(col(c) == value, c)).alias(c) for c in df.columns]).collect()[0].asDict()\\n\",\n \" return [k for k, v in col_values.items() if v > 0]\\n\",\n \"\\n\",\n \"\\n\",\n \"def find_neg_columns(df):\\n\",\n \" col_values = df.select([count(when(col(c) < 0.0, c)).alias(c) for c in df.columns]).collect()[0].asDict()\\n\",\n \" return [k for k, v in col_values.items() if v > 0]\\n\",\n \"\\n\",\n \"\\n\",\n \"def find_null_columns(df):\\n\",\n \" col_values = df.select([count(when(col(c).isNull(), c)).alias(c) for c in df.columns]).collect()[0].asDict()\\n\",\n \" return [k for k, v in col_values.items() if v > 0]\\n\",\n \"\\n\",\n \"\\n\",\n \"def assert_column_validity(df):\\n\",\n \" impute_cols = find_columns_to_impute(df)\\n\",\n \" assert len(impute_cols) == 0, f'Invalid columns found {impute_cols}'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.4 Spark Session\\n\",\n \"\\n\",\n \"A SparkSession is created configured with 3 executors using all cores.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spark = (SparkSession.builder\\n\",\n \" .master('local[*]')\\n\",\n \" .appName('ml-ids')\\n\",\n \" .config('spark.executor.instances', '3')\\n\",\n \" .config('spark.executor.cores', '3')\\n\",\n \" .config('spark.executor.memory', '15g')\\n\",\n \" .config('spark.driver.memory', '15g')\\n\",\n \" .getOrCreate())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Dataset creation\\n\",\n \"\\n\",\n \"In this part both the [CIC-IDS-2018](https://drive.google.com/open?id=1HrTPh0YRSZ4T9DLa_c47lubheKUcPl0r) and [CIC-IDS-2017](https://drive.google.com/open?id=1Q2J_pPB0K0PHjq0YO5BPwYQwrvoZgYqo) datasets are loaded, combined and split into a training and holdout dataset with the ratio of `90%/10%`. \\n\",\n \"Both respective datasets are subsequently saved to disk.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Data samples: 17157022\\n\",\n \"Holdout samples: 1906664\\n\",\n \"Writing training set...\\n\",\n \"Writing holdout set...\\n\"\n ]\n }\n ],\n \"source\": [\n \"df = load_csv(dataset_path)\\n\",\n \"\\n\",\n \"(training_df, hold_df) = df.randomSplit([0.9, 0.1], seed=42)\\n\",\n \"\\n\",\n \"print(f\\\"Data samples: {training_df.count()}\\\")\\n\",\n \"print(f\\\"Holdout samples: {hold_df.count()}\\\")\\n\",\n \"\\n\",\n \"print('Writing training set...')\\n\",\n \"training_df.write.csv(training_path, header='true', mode='overwrite')\\n\",\n \"\\n\",\n \"print('Writing holdout set...')\\n\",\n \"hold_df.write.csv(holdout_path, header='true', mode='overwrite')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As both datasets contain invalid values as well as empty values all columns that have to be processed are determined.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Columns to impute:\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"['flow_duration',\\n\",\n \" 'flow_byts_s',\\n\",\n \" 'flow_pkts_s',\\n\",\n \" 'flow_iat_min',\\n\",\n \" 'fwd_iat_tot',\\n\",\n \" 'fwd_iat_min',\\n\",\n \" 'init_fwd_win_byts',\\n\",\n \" 'init_bwd_win_byts',\\n\",\n \" 'fwd_seg_size_min']\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"impute_cols = find_columns_to_impute(df.select(*selected_features))\\n\",\n \"print(f'Columns to impute:')\\n\",\n \"impute_cols\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Model Training\\n\",\n \"\\n\",\n \"In this section a pipeline is created in order to remove invalid values and impute missing values in the dataset. Afterwards a GBTClassifier is trained on the data using grid-search. \\n\",\n \"Both the pipeline and the classifier are serialized to disk as a last step.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.1 Dataset Loading\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"impute_cols = [\\n\",\n \" 'flow_duration',\\n\",\n \" 'flow_byts_s',\\n\",\n \" 'flow_pkts_s',\\n\",\n \" 'flow_iat_min',\\n\",\n \" 'fwd_iat_tot',\\n\",\n \" 'fwd_iat_min',\\n\",\n \" 'init_fwd_win_byts',\\n\",\n \" 'init_bwd_win_byts',\\n\",\n \" 'fwd_seg_size_min'\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_df = load_csv(os.path.join(training_path, '*.csv'), selected_columns).cache()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.2 Feature columns\\n\",\n \"\\n\",\n \"The feature columns of the classifier are defined in order to create the pipeline in the next step.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Number of feature columns: 31\\n\",\n \"Feature columns:\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"['tot_fwd_pkts',\\n\",\n \" 'tot_bwd_pkts',\\n\",\n \" 'totlen_fwd_pkts',\\n\",\n \" 'totlen_bwd_pkts',\\n\",\n \" 'fwd_pkt_len_mean',\\n\",\n \" 'fwd_pkt_len_std',\\n\",\n \" 'bwd_pkt_len_mean',\\n\",\n \" 'flow_iat_std',\\n\",\n \" 'bwd_iat_tot',\\n\",\n \" 'bwd_iat_min',\\n\",\n \" 'fwd_psh_flags',\\n\",\n \" 'fwd_urg_flags',\\n\",\n \" 'bwd_pkts_s',\\n\",\n \" 'fin_flag_cnt',\\n\",\n \" 'rst_flag_cnt',\\n\",\n \" 'psh_flag_cnt',\\n\",\n \" 'ack_flag_cnt',\\n\",\n \" 'urg_flag_cnt',\\n\",\n \" 'down_up_ratio',\\n\",\n \" 'active_mean',\\n\",\n \" 'idle_mean',\\n\",\n \" 'protocol_cat',\\n\",\n \" 'flow_duration_imputed',\\n\",\n \" 'flow_byts_s_imputed',\\n\",\n \" 'flow_pkts_s_imputed',\\n\",\n \" 'flow_iat_min_imputed',\\n\",\n \" 'fwd_iat_tot_imputed',\\n\",\n \" 'fwd_iat_min_imputed',\\n\",\n \" 'init_fwd_win_byts_imputed',\\n\",\n \" 'init_bwd_win_byts_imputed',\\n\",\n \" 'fwd_seg_size_min_imputed']\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"cat_cols = ['protocol']\\n\",\n \"\\n\",\n \"cleaned_impute_cols = [f'{c}_clean' for c in impute_cols]\\n\",\n \"\\n\",\n \"processed_cols = cat_cols + impute_cols\\n\",\n \"unprocessed_cols = [c for c in selected_features if c not in processed_cols] \\n\",\n \"feature_cols = (unprocessed_cols + \\n\",\n \" [cat_column_name(c) for c in cat_cols] +\\n\",\n \" [imputed_column_name(c) for c in impute_cols])\\n\",\n \"\\n\",\n \"print(f'Number of feature columns: {len(feature_cols)}')\\n\",\n \"print('Feature columns:')\\n\",\n \"feature_cols\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.3 Pipeline Creation\\n\",\n \"\\n\",\n \"In this section the pipeline to process the input data is created. The pipeline consists of the following stages:\\n\",\n \"1. `ValueCleaner`: sets all values to `None` which have a value of `inf` or `<0` in order to be imputed in the next stage.\\n\",\n \"2. `Imputer`: Imputes all missing values with the mean value of the column.\\n\",\n \"3. `OneHotEncoderEstimator`: One-hot encodes the `protocol` category column.\\n\",\n \"4. `VectorAssembler`: Merges all feature columns into a feature vector column. \\n\",\n \"5. `BinaryLabelMaker`: Adds a binary label with value `0 = Benign` and `1 = Attack`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"stages = []\\n\",\n \"\\n\",\n \"# ValueCleaner\\n\",\n \"cleaner = ValueCleaner(inputCols=impute_cols, outputCols=cleaned_impute_cols)\\n\",\n \"stages += [cleaner]\\n\",\n \"\\n\",\n \"# Imputer\\n\",\n \"imputer = Imputer(\\n\",\n \" inputCols=cleaned_impute_cols, \\n\",\n \" outputCols=[imputed_column_name(c) for c in impute_cols]\\n\",\n \")\\n\",\n \"stages += [imputer]\\n\",\n \"\\n\",\n \"# OneHotEncoderEstimator\\n\",\n \"for c in cat_cols:\\n\",\n \" encoder = OneHotEncoderEstimator(inputCols=[c], outputCols=[cat_column_name(c)])\\n\",\n \" stages += [encoder]\\n\",\n \" \\n\",\n \"# VetorAssembler\\n\",\n \"assembler = VectorAssembler(inputCols=feature_cols, outputCol=\\\"features\\\")\\n\",\n \"stages += [assembler] \\n\",\n \"\\n\",\n \"# LabelMaker\\n\",\n \"label_maker = BinaryLabelMaker(\\n\",\n \" inputCols=['label'], \\n\",\n \" outputCols=['label_is_attack'],\\n\",\n \" classLabel='Benign' \\n\",\n \")\\n\",\n \"stages += [label_maker]\\n\",\n \"\\n\",\n \"pipeline = Pipeline(stages=stages)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.4 Pipeline Fitting\\n\",\n \"\\n\",\n \"In this step the pipeline is fitted, the training dataset is transformed and the validity of the resulting dataset is asserted.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pipeline_model = pipeline.fit(train_df)\\n\",\n \"train_transf_df = pipeline_model.transform(train_df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"assert_column_validity(train_transf_df.select(feature_cols).drop('protocol_cat').drop('features'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.5 Dataset Preparation\\n\",\n \"\\n\",\n \"The dataset is splitted into training and test datasets.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training samples: 13728637\\n\",\n \"Test samples: 3428385\\n\"\n ]\n }\n ],\n \"source\": [\n \"(training_data, test_data) = train_transf_df.randomSplit([0.8, 0.2], seed=42)\\n\",\n \"print(f'Training samples: {training_data.count()}')\\n\",\n \"print(f'Test samples: {test_data.count()}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.6 Model Training\\n\",\n \"\\n\",\n \"In this section the GBTClassifier is trained using grid-search with cross-validation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Best parameters:\\n\",\n \"Max depth: 5\\n\",\n \"Max iterations: 20\\n\",\n \"Step size: 0.5\\n\"\n ]\n }\n ],\n \"source\": [\n \"gb = GBTClassifier(labelCol='label_is_attack', featuresCol='features', seed=42)\\n\",\n \"\\n\",\n \"param_grid = (ParamGridBuilder()\\n\",\n \" .addGrid(gb.maxDepth, [1, 3, 5])\\n\",\n \" .addGrid(gb.maxIter, [10, 20])\\n\",\n \" .addGrid(gb.stepSize, [0.5, 0.1, 0.05])\\n\",\n \" .build())\\n\",\n \"\\n\",\n \"evaluator = (MulticlassClassificationEvaluator(predictionCol=\\\"prediction\\\", \\n\",\n \" labelCol='label_is_attack',\\n\",\n \" metricName='weightedRecall'))\\n\",\n \"\\n\",\n \"cv = (CrossValidator(estimator=gb,\\n\",\n \" estimatorParamMaps=param_grid,\\n\",\n \" evaluator=evaluator,\\n\",\n \" numFolds=3))\\n\",\n \"\\n\",\n \"gb_model = cv.fit(training_data)\\n\",\n \"\\n\",\n \"print('Best parameters:')\\n\",\n \"print(f'Max depth: {gb_model.bestModel._java_obj.getMaxDepth()}')\\n\",\n \"print(f'Max iterations: {gb_model.bestModel._java_obj.getMaxIter()}')\\n\",\n \"print(f'Step size: {gb_model.bestModel._java_obj.getStepSize()}')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.7 Model Evaluation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Train):\\n\",\n \"Recall: 0.9875890082897523\\n\",\n \"Precision: 0.9875789062812305\\n\",\n \"F1: 0.9874730933711836\\n\",\n \"FPR: 0.04899706171213246\\n\",\n \"TPR: 0.9875890082897523\\n\",\n \"\\n\",\n \"Area under PR (raw predictions): 0.9776131473914916\\n\",\n \"Area under PR: 0.9626597197081999\\n\",\n \"Accuracy = 0.9875890082897523\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"DenseMatrix([[11317165., 30602.],\\n\",\n \" [ 139784., 2241086.]])\\n\"\n ]\n }\n ],\n \"source\": [\n \"train_pred = gb_model.transform(training_data)\\n\",\n \"print_classification_report(train_pred, 'prediction', 'label_is_attack', 'Train')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Test):\\n\",\n \"Recall: 0.9875626570528105\\n\",\n \"Precision: 0.9875525094295057\\n\",\n \"F1: 0.9874458377349316\\n\",\n \"FPR: 0.04922211493411625\\n\",\n \"TPR: 0.9875626570528105\\n\",\n \"\\n\",\n \"Area under PR (raw predictions): 0.97758372423304\\n\",\n \"Area under PR: 0.9625211546722784\\n\",\n \"Accuracy = 0.9875626570528105\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"DenseMatrix([[2827204., 7643.],\\n\",\n \" [ 34997., 558541.]])\\n\"\n ]\n }\n ],\n \"source\": [\n \"test_pred = gb_model.transform(test_data)\\n\",\n \"print_classification_report(test_pred, 'prediction', 'label_is_attack', 'Test')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The performance of the model is promising with a recall of `0.988` and a precision of `0.988` on the test set. \\n\",\n \"The following table summarizes the performance on the test set: \\n\",\n \"\\n\",\n \"|Precision|Recall|F1|Area under PR|\\n\",\n \"|---------|------|--|-------------|\\n\",\n \"|0.988|0.988|0.988|0.978|\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.8 Model Persistence\\n\",\n \"\\n\",\n \"In the last step of this section the models for the pipeline and the GBTClassifier are persisted to disk in order to be used independent of training.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pipeline_model.write().overwrite().save(pipeline_model_path)\\n\",\n \"gb_model.write().overwrite().save(gb_model_path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Model Usage\\n\",\n \"\\n\",\n \"This section demonstrates the usage of the classifier trained in the previous section. Models for the pipeline and the GBTClassifier are read from disk in order to perform predictions on the holdout dataset. \\n\",\n \"This section can be used as a basis for a separate driver program.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.1 Pipeline and GBTClassifier Loading\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pipeline_model = PipelineModel.load(pipeline_model_path)\\n\",\n \"gb_model = CrossValidatorModel.load(gb_model_path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.2 Holdout Dataset Loading\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Holdout samples: 1906664\\n\"\n ]\n }\n ],\n \"source\": [\n \"hold_df = load_csv(os.path.join(holdout_path, '*.csv'), selected_columns).cache()\\n\",\n \"print(f\\\"Holdout samples: {hold_df.count()}\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.3 Pipeline Application\\n\",\n \"\\n\",\n \"The pipeline is applied to the dataset, asserting the validity of all values in the next step.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"hold_transf_df = pipeline_model.transform(hold_df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"assert_column_validity(hold_transf_df.select(feature_cols).drop('protocol_cat').drop('features'))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.4 Predictions on the Holdout Dataset\\n\",\n \"\\n\",\n \"The classifier is applied to the holdout dataset, printing the classification report for the resulting predictions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report (Holdout):\\n\",\n \"Recall: 0.9876931646058246\\n\",\n \"Precision: 0.9876838348438507\\n\",\n \"F1: 0.9875794596235876\\n\",\n \"FPR: 0.04846458013698347\\n\",\n \"TPR: 0.9876931646058246\\n\",\n \"\\n\",\n \"Area under PR (raw predictions): 0.9777125841930321\\n\",\n \"Area under PR: 0.9630935075027023\\n\",\n \"Accuracy = 0.9876931646058246\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"DenseMatrix([[1570985., 4206.],\\n\",\n \" [ 19259., 312214.]])\\n\"\n ]\n }\n ],\n \"source\": [\n \"hold_pred = gb_model.transform(hold_transf_df)\\n\",\n \"print_classification_report(hold_pred, 'prediction', 'label_is_attack', 'Holdout')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"spark.stop()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/04_ml-prototype-spark/models/gb-model/bestModel/data/_SUCCESS", "content": "" }, { "path": "notebooks/04_ml-prototype-spark/models/gb-model/bestModel/metadata/_SUCCESS", "content": "" }, { "path": "notebooks/04_ml-prototype-spark/models/gb-model/bestModel/metadata/part-00000", "content": "{\"class\":\"org.apache.spark.ml.classification.GBTClassificationModel\",\"timestamp\":1568299022309,\"sparkVersion\":\"2.4.4\",\"uid\":\"GBTClassifier_0f5cdab6ac21\",\"paramMap\":{\"seed\":42,\"maxDepth\":5,\"labelCol\":\"label_is_attack\",\"stepSize\":0.5,\"featuresCol\":\"features\",\"maxIter\":20},\"defaultParamMap\":{\"rawPredictionCol\":\"rawPrediction\",\"minInstancesPerNode\":1,\"impurity\":\"gini\",\"minInfoGain\":0.0,\"seed\":-715221063584165447,\"maxDepth\":5,\"labelCol\":\"label\",\"featureSubsetStrategy\":\"all\",\"subsamplingRate\":1.0,\"probabilityCol\":\"probability\",\"maxMemoryInMB\":256,\"cacheNodeIds\":false,\"validationTol\":0.01,\"stepSize\":0.1,\"checkpointInterval\":10,\"maxBins\":32,\"lossType\":\"logistic\",\"predictionCol\":\"prediction\",\"featuresCol\":\"features\",\"maxIter\":20},\"numFeatures\":47,\"numTrees\":20}\n" }, { "path": "notebooks/04_ml-prototype-spark/models/gb-model/bestModel/treesMetadata/_SUCCESS", "content": "" }, { "path": 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\"|---------|------|----|----------------|-------------|\\n\",\n \"|0.88 |0.88 |0.88|0.86 |0.90 |\\n\",\n \"\\n\",\n \"\\n\",\n \"## 1. Introduction\\n\",\n \"\\n\",\n \"In this notebook, we will use an unsupervised learning approach to create a binary classifier based on the ideas of representation learning and anomaly detection.\\n\",\n \"We will train multiple deep learning models on benign data of the [CIC-IDS-2018 dataset](https://www.unb.ca/cic/datasets/ids-2018.html) with the goal of learning a meaningful representation of this benign data. With this approach we want to create a model that is able to classify network traffic as benign or malicious, based on the notion of traffic being similar or dissimilar to the data the model has been trained with. The rationale of using unsupervised learning is that benign data is usually easier to obtain and hence can be provided in higher volumes than malicious data.\\n\",\n \"\\n\",\n \"### Autoencoder\\n\",\n \"\\n\",\n \"The architecture used for the models in this notebook is based on different variations of the [Autoencoder](https://en.wikipedia.org/wiki/Autoencoder). An autoencoder is a neural network with the goal of learning a dense representation of the input data. To this end, an autoencoder is trained to reconstruct the given inputs by initially encoding the input features as dense representations (which are called latent representations or codings) and then decoding the dense representations to reconstruct the initial inputs. Using this approach, the model should learn the identity function of the input data. \\n\",\n \"To ensure that the model actually learns a meaningful representation of the data and does not just copy the input features to the output, an autoencoder restricts the model in different ways, for example by limiting the dimensionality of the latent representations as shown in figure 1, which illustrates an undercomplete autoencoder.\\n\",\n \"\\n\",\n \"\\\"Undercomplete\\n\",\n \"
Figure 1: Undercomplete Autoencoder (Source)
\\n\",\n \"\\n\",\n \"Autoencoders share many characteristics with PCA and are therefore often used for dimensionality reduction or as feature extractors, but can also be utilized for anomaly detection as demonstrated in this notebook. \\n\",\n \"The underlying assumption of using an autoencoder for the task of anomaly detection is that the autoencoder is trained on normal data only and hence learns a meaningful representation for only this kind of data. The objective of the model is to minimize the reconstruction error of a given sample, which is measured as the mean-squared error given the reconstructed output and the original input. \\n\",\n \"As the autoencoder is trained on normal data only, it will output results with a low reconstruction error given normal data, but will not be able to reconstruct anomalous data with the same precision and hence will recreate the outputs of anomalous data with a higher reconstruction error. \\n\",\n \"Measuring the reconstruction error of a sample and comparing it with a predefined decision boundary allows us to classify a sample as normal or anomalous.\\n\",\n \"\\n\",\n \"### Autoencoder for Anomaly Detection\\n\",\n \"\\n\",\n \"Given our use case of network traffic classification, the model is trained on benign data only. A validation set containing benign and malicious data is used subsequently to define the decision boundary, based on the reconstruction errors of the two types of traffic. \\n\",\n \"At inference, a sample is fed to the autoencoder, the reconstruction error is measured and the sample subsequently classified as malicious if the reconstruction error exceeds the predefined decision boundary.\\n\",\n \"\\n\",\n \"This notebook explores the following architecture variants of autoencoders:\\n\",\n \"* Undercomplete Autoencoder\\n\",\n \"* Stacked Autoencoder\\n\",\n \"* Denoising Autoencoder\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Dataset Preparation\\n\",\n \"\\n\",\n \"In this section we load the dataset and prepare the data for training. The preprocessed dataset from CIC-IDS-2018 can be found at [Google Drive](https://drive.google.com/open?id=1HrTPh0YRSZ4T9DLa_c47lubheKUcPl0r).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dataset_base_path = r'/home/glados/Development/Projects/ids-2018/processed'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import tensorflow as tf\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"import gc\\n\",\n \"from ml_ids.data.dataset import load_dataset\\n\",\n \"from ml_ids.transform.preprocessing import create_pipeline\\n\",\n \"from ml_ids.model_selection import split_x_y\\n\",\n \"from ml_ids.visualization import plot_confusion_matrix, plot_hist, plot_threshold\\n\",\n \"from ml_ids.keras.metrics import AveragePrecisionScoreMetric\\n\",\n \"from ml_ids.keras.callbacks import OneCycleScheduler\\n\",\n \"from ml_ids.libs.dfencoder.dataframe import EncoderDataFrame\\n\",\n \"from sklearn.preprocessing import StandardScaler, MinMaxScaler\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.metrics import average_precision_score, mean_squared_error\\n\",\n \"from sklearn.utils.multiclass import unique_labels\\n\",\n \"from functools import partial\\n\",\n \"import tensorflow as tf\\n\",\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers, models, optimizers, callbacks, regularizers, constraints\\n\",\n \"from scipy import stats\\n\",\n \"from notebook_utils import predict, evaluate_pr_roc, plot_evaluation_curves, plot_pr_threshold_curves, best_precision_for_target_recall, print_performance, filter_benign\\n\",\n \"\\n\",\n \"K = keras.backend\\n\",\n \"\\n\",\n \"rand_state = 42\\n\",\n \"tf.random.set_seed(rand_state)\\n\",\n \"np.random.seed(rand_state)\\n\",\n \"\\n\",\n \"physical_devices = tf.config.experimental.list_physical_devices('GPU')\\n\",\n \"tf.config.experimental.set_memory_growth(physical_devices[0], True)\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Data Loading \\n\",\n \"\\n\",\n \"In this part we load the dataset and specify two kinds of features:\\n\",\n \"1. *Features to impute*: these are features containing missing values which will be imputed in a later step.\\n\",\n \"2. *Features to omit*: these are the features that will not be used for training the model. These encompass all the features having no variance, features having the same distribution for benign and malicious traffic and features which should not be taken into account in order to obtain a better generalization of the model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cols_to_impute = [\\n\",\n \" 'flow_duration',\\n\",\n \" 'flow_byts_s',\\n\",\n \" 'flow_pkts_s',\\n\",\n \" 'flow_iat_mean',\\n\",\n \" 'flow_iat_max',\\n\",\n \" 'flow_iat_min',\\n\",\n \" 'fwd_iat_tot',\\n\",\n \" 'fwd_iat_mean',\\n\",\n \" 'fwd_iat_max',\\n\",\n \" 'fwd_iat_min'\\n\",\n \"]\\n\",\n \"\\n\",\n \"features_no_variance = [\\n\",\n \" \\\"bwd_blk_rate_avg\\\",\\n\",\n \" \\\"bwd_byts_b_avg\\\",\\n\",\n \" \\\"bwd_pkts_b_avg\\\",\\n\",\n \" \\\"bwd_psh_flags\\\",\\n\",\n \" \\\"bwd_urg_flags\\\",\\n\",\n \" \\\"fwd_blk_rate_avg\\\",\\n\",\n \" \\\"fwd_byts_b_avg\\\",\\n\",\n \" \\\"fwd_pkts_b_avg\\\"\\n\",\n \"]\\n\",\n \"\\n\",\n \"features_same_distribution = [\\n\",\n \" 'fwd_urg_flags',\\n\",\n \" 'pkt_len_var',\\n\",\n \" 'fin_flag_cnt',\\n\",\n \" 'urg_flag_cnt',\\n\",\n \" 'cwe_flag_count',\\n\",\n \" 'down_up_ratio',\\n\",\n \" 'fwd_act_data_pkts',\\n\",\n \" 'active_max'\\n\",\n \"]\\n\",\n \"\\n\",\n \"omit_cols = features_no_variance + features_same_distribution + ['timestamp', 'dst_port', 'protocol']\\n\",\n \"\\n\",\n \"ids_data = load_dataset(dataset_base_path,\\n\",\n \" omit_cols=omit_cols,\\n\",\n \" preserve_neg_value_cols=['init_fwd_win_byts', 'init_bwd_win_byts'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Data Splitting\\n\",\n \"\\n\",\n \"In this step we split the dataset into:\\n\",\n \"- training set,\\n\",\n \"- validation set and\\n\",\n \"- test set.\\n\",\n \"\\n\",\n \"The training set contains benign data only, as we will use benign data as the normal category for our anomaly detection models. The validation and test sets contain benign data as well as malicious data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Samples:\\n\",\n \"========\\n\",\n \"Training: (10787766, 60)\\n\",\n \"Val: (2722588, 60)\\n\",\n \"Test: (2722589, 60)\\n\",\n \"\\n\",\n \"Training labels:\\n\",\n \"================\\n\",\n \"Benign 10787766\\n\",\n \"Name: label, dtype: int64\\n\",\n \"\\n\",\n \"Validation labels:\\n\",\n \"==================\\n\",\n \"Benign 1348471\\n\",\n \"DDOS attack-HOIC 343006\\n\",\n \"DDoS attacks-LOIC-HTTP 288095\\n\",\n \"DoS attacks-Hulk 230956\\n\",\n \"Bot 143095\\n\",\n \"FTP-BruteForce 96680\\n\",\n \"SSH-Bruteforce 93794\\n\",\n \"Infilteration 80967\\n\",\n \"DoS attacks-SlowHTTPTest 69945\\n\",\n \"DoS attacks-GoldenEye 20754\\n\",\n \"DoS attacks-Slowloris 5495\\n\",\n \"DDOS attack-LOIC-UDP 865\\n\",\n \"Brute Force -Web 306\\n\",\n \"Brute Force -XSS 115\\n\",\n \"SQL Injection 44\\n\",\n \"Name: label, dtype: int64\\n\",\n \"\\n\",\n \"Test labels:\\n\",\n \"============\\n\",\n \"Benign 1348471\\n\",\n \"DDOS attack-HOIC 343006\\n\",\n \"DDoS attacks-LOIC-HTTP 288096\\n\",\n \"DoS attacks-Hulk 230956\\n\",\n \"Bot 143096\\n\",\n \"FTP-BruteForce 96680\\n\",\n \"SSH-Bruteforce 93795\\n\",\n \"Infilteration 80967\\n\",\n \"DoS attacks-SlowHTTPTest 69945\\n\",\n \"DoS attacks-GoldenEye 20754\\n\",\n \"DoS attacks-Slowloris 5495\\n\",\n \"DDOS attack-LOIC-UDP 865\\n\",\n \"Brute Force -Web 305\\n\",\n \"Brute Force -XSS 115\\n\",\n \"SQL Injection 43\\n\",\n \"Name: label, dtype: int64\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"11\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"benign_mask = ids_data.label_is_attack == 0\\n\",\n \"attack_mask = ids_data.label_is_attack == 1\\n\",\n \"\\n\",\n \"train_data, hold_data = train_test_split(ids_data[benign_mask], test_size=0.2, random_state=rand_state)\\n\",\n \"\\n\",\n \"val_data_benign, test_data_benign = train_test_split(hold_data, test_size=0.5, random_state=rand_state)\\n\",\n \"\\n\",\n \"val_data_attack, test_data_attack = (train_test_split(ids_data[attack_mask],\\n\",\n \" test_size=0.5,\\n\",\n \" stratify=ids_data[attack_mask].label_cat,\\n\",\n \" random_state=rand_state))\\n\",\n \"\\n\",\n \"\\n\",\n \"X_train_raw, y_train = split_x_y(train_data)\\n\",\n \"X_val_raw, y_val = split_x_y(val_data_benign.append(val_data_attack))\\n\",\n \"X_test_raw, y_test = split_x_y(test_data_benign.append(test_data_attack))\\n\",\n \"\\n\",\n \"print('Samples:')\\n\",\n \"print('========')\\n\",\n \"print('Training: {}'.format(X_train_raw.shape))\\n\",\n \"print('Val: {}'.format(X_val_raw.shape))\\n\",\n \"print('Test: {}'.format(X_test_raw.shape))\\n\",\n \"\\n\",\n \"print('\\\\nTraining labels:')\\n\",\n \"print('================')\\n\",\n \"print(y_train.label.value_counts())\\n\",\n \"print('\\\\nValidation labels:')\\n\",\n \"print('==================')\\n\",\n \"print(y_val.label.value_counts())\\n\",\n \"print('\\\\nTest labels:')\\n\",\n \"print('============')\\n\",\n \"print(y_test.label.value_counts())\\n\",\n \"\\n\",\n \"del ids_data, train_data, hold_data, val_data_benign, val_data_attack, test_data_benign, test_data_attack\\n\",\n \"gc.collect()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.3 Data Preparation\\n\",\n \"\\n\",\n \"Here we prepare the datasets by performing the following steps:\\n\",\n \"- imputation of missing data and\\n\",\n \"- normalization of data using a `MinMaxScaler`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Samples:\\n\",\n \"========\\n\",\n \"Training: (10787766, 60)\\n\",\n \"Val: (2722588, 60)\\n\",\n \"Test: (2722589, 60)\\n\",\n \"\\n\",\n \"Missing values:\\n\",\n \"===============\\n\",\n \"Training: 0\\n\",\n \"Val: 0\\n\",\n \"Test: 0\\n\",\n \"\\n\",\n \"Scaling:\\n\",\n \"========\\n\",\n \"Training: min=0.0, max=1.0\\n\",\n \"Val: min=0.0, max=2.2449962079994723\\n\",\n \"Test: min=0.0, max=2.5524120420747187\\n\"\n ]\n }\n ],\n \"source\": [\n \"pipeline, get_col_names = create_pipeline(X_train_raw, \\n\",\n \" imputer_strategy='median',\\n\",\n \" imputer_cols=cols_to_impute,\\n\",\n \" scaler=MinMaxScaler)\\n\",\n \"\\n\",\n \"X_train = pipeline.fit_transform(X_train_raw)\\n\",\n \"X_val = pipeline.transform(X_val_raw)\\n\",\n \"X_test = pipeline.transform(X_test_raw)\\n\",\n \"\\n\",\n \"X_val_benign = filter_benign(X_val, y_val)\\n\",\n \"\\n\",\n \"column_names = get_col_names()\\n\",\n \"\\n\",\n \"print('Samples:')\\n\",\n \"print('========')\\n\",\n \"print('Training: {}'.format(X_train.shape))\\n\",\n \"print('Val: {}'.format(X_val.shape))\\n\",\n \"print('Test: {}'.format(X_test.shape))\\n\",\n \"\\n\",\n \"print('\\\\nMissing values:')\\n\",\n \"print('===============')\\n\",\n \"print('Training: {}'.format(np.count_nonzero(np.isnan(X_train))))\\n\",\n \"print('Val: {}'.format(np.count_nonzero(np.isnan(X_val))))\\n\",\n \"print('Test: {}'.format(np.count_nonzero(np.isnan(X_test))))\\n\",\n \"\\n\",\n \"print('\\\\nScaling:')\\n\",\n \"print('========')\\n\",\n \"print('Training: min={}, max={}'.format(np.min(X_train), np.max(X_train)))\\n\",\n \"print('Val: min={}, max={}'.format(np.min(X_val), np.max(X_val)))\\n\",\n \"print('Test: min={}, max={}'.format(np.min(X_test), np.max(X_test)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Model Training\\n\",\n \"\\n\",\n \"In this section we train and evaluate various types of autoencoder architectures to create a binary classifier using anomaly detection.\\n\",\n \"\\n\",\n \"The following autoencoder architectures are evaluated:\\n\",\n \"- Undercomplete Autoencoder\\n\",\n \"- Stacked Autoencoder\\n\",\n \"- Denoising Autoencoder\\n\",\n \"\\n\",\n \"All models are trained using [Tensorflow 2](https://www.tensorflow.org/) in combination with [Keras](https://keras.io/). \\n\",\n \"We utilize the training set containing benign data only to train the model and the benign portion of the validation set to detect overfitting. \\n\",\n \"Furthermore, we use a custom Keras callback named *AveragePrecisionScoreMetric* to calculate the average precision score, using the corresponding [scikit-learn function](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.average_precision_score.html), after each epoch on the complete validation set containing benign and malicious data.\\n\",\n \"This score is used to perform early stopping of the training process in order to obtain the best performing model. \\n\",\n \"Inputs are normalized using a `MinMaxScaler` and reproduced using the `Sigmoid` activation function. We utilize the `Adam` optimizer with a `binary crossentropy` loss per ouput unit to train the model, resulting in a multilabel classifier model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As a first step we define the training parameters for all models.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"input_dims = X_train.shape[1]\\n\",\n \"epochs = 50\\n\",\n \"batch_size = 4096\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.1 Undercomplete Autoencoder\\n\",\n \"\\n\",\n \"The undercomplete autoencoder represents the simplest type of autoencoder, as it uses a single restricted hidden layer containing the latent representations of the data. \\n\",\n \"Figure 2 illustrates the architecture of an undercomplete autoencoder.\\n\",\n \"\\n\",\n \"\\\"Undercomplete\\n\",\n \"
Figure 2: Undercomplete Autoencoder (Source)
\\n\",\n \"\\n\",\n \"We choose to restrict the latent layer to 30 units cutting the number of features in half. The number of units was determined empirically by training and comparing multiple hyperparameter values.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense (Dense) (None, 30) 1830 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 60) 1860 \\n\",\n \"=================================================================\\n\",\n \"Total params: 3,690\\n\",\n \"Trainable params: 3,690\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"K.clear_session()\\n\",\n \"gc.collect()\\n\",\n \"\\n\",\n \"simple_ae = models.Sequential([\\n\",\n \" layers.Dense(30, activation='elu', input_shape=[input_dims]),\\n\",\n \" layers.Dense(input_dims, activation='sigmoid')\\n\",\n \"])\\n\",\n \"\\n\",\n \"simple_ae.compile(optimizer='adam', loss='binary_crossentropy')\\n\",\n \"simple_ae.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"early_stopping = callbacks.EarlyStopping(monitor='val_auprc', \\n\",\n \" mode='max',\\n\",\n \" patience=15, \\n\",\n \" restore_best_weights=True)\\n\",\n \"\\n\",\n \"reduce_lr = callbacks.ReduceLROnPlateau(monitor='val_auprc', \\n\",\n \" mode='max', \\n\",\n \" factor=0.2, \\n\",\n \" patience=3, \\n\",\n \" min_lr=0.0001)\\n\",\n \"\\n\",\n \"mc = callbacks.ModelCheckpoint(filepath='models/simple_autoencoder_model.h5',\\n\",\n \" monitor='val_auprc', \\n\",\n \" mode='max',\\n\",\n \" save_best_only=True, \\n\",\n \" verbose=0)\\n\",\n \"\\n\",\n \"hist = simple_ae.fit(x=X_train, \\n\",\n \" y=X_train, \\n\",\n \" validation_data=(X_val_benign, X_val_benign),\\n\",\n \" batch_size=batch_size,\\n\",\n \" epochs=epochs,\\n\",\n \" callbacks=[\\n\",\n \" AveragePrecisionScoreMetric(X_val=X_val, y_val=y_val.label_is_attack, batch_size=16384),\\n\",\n \" early_stopping,\\n\",\n \" reduce_lr,\\n\",\n \" mc\\n\",\n \" ])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[Output omitted for brevity]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Validation\\n\",\n \"\\n\",\n \"Inspecting the learning curves we observe that the model does not overfit the training data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_hist(hist)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The plot of the average precision score shows a significant increase of the score until epoch 23. Afterwards the performance stabilizes and does not increase significantly.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_hist(hist, metrics=['val_auprc'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For this model we obtain an average precision score of `0.91972`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"simple_ae = models.load_model('models/simple_autoencoder_model.h5')\\n\",\n \"pred_train_simple_ae = predict(simple_ae, X_train, y_train.label_is_attack)\\n\",\n \"pred_val_simple_ae = predict(simple_ae, X_val, y_val.label_is_attack)\\n\",\n \"\\n\",\n \"plot_evaluation_curves(pred_val_simple_ae)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_pr_threshold_curves(pred_val_simple_ae, pr_plot_lim=[0, 0.0001])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the next step we choose a threshold to define the decision boundary, used to separate benign and malicious data, by utilizing the validation data. \\n\",\n \"The threshold is chosen to obtain a minimum recall of `0.9`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.63808195226977e-06\\n\"\n ]\n }\n ],\n \"source\": [\n \"threshold_simple_ae = best_precision_for_target_recall(pred_val_simple_ae, target_recall=0.9)\\n\",\n \"print(threshold_simple_ae)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following plot illustrates the distributions of benign and malicious data, outlining the decision boundary. \\n\",\n \"We can observe that the overlap of both traffic classes is quite large.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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mmaT4OPA4wduzY5jNGEZHOavbseEcgEnUuj6vRrQDgeMVNWwKISFtWXV3N5MmTcbvdmKbJwoULg07aoiGYxG01MNgwjAFAMXA98K0TTzBNc0DgzydU3E5K2kREJEhK3NqEanc117x2DfeNv4/JZ0xu+QJplsvjIjs5u9H3AgmdWiVFpC1LS0tr2LctHlpc42aapge4C/+0yK+AF03T3GQYxu2GYdwe6wBFRDqdw4f9h8TVprJNlDhL2H5se7xD6RBaGk4CaC83EZFmBLWPm2maS4Alp7zW6CAS0zRnRx6WiEgnNn26/6PWuMXVhsP+aWFOtzPOkXQMNd4akqzNt0q63Kq4iYg0JZipkiIiIp3OxsMbASVu0RJMxc3lVeImItIUJW4iIiKNUMUtuprbDiBQidMaNxGRpilxExEROUVpdSkHnAcAJW7R4PV5qfXWtrzGTVMlRVrVpEmTePfdd096bcGCBQ17njUlNTUVgJKSEqYH2vsbuXdLgzwWLFhAdXV1w+eXXXYZx44dCyb0oOTl5Z20qTjAs88+S0nJ8QH5p8YQimeffZa77rorohhDocRNRETkFIE2yURrohK3KAgMHWmy4qapkiJxMWvWLBYvXnzSa4sXLz4t2WlK7969efnl07ZuDtqpSdOSJUvIyMgI+34n+uqrr/D5fCxfvhyn8/i/49FM3FqbEjcRkbZm7lz/IXGz4fAGrIaV3OxcJW5REEjImtrHLdmWDKjiJtLapk+fzptvvkltbS0AhYWFlJSUMGHCBKqqqpgyZQpjxoxh1KhRvPbaa6ddX1hYyMiRIwFwuVxcf/315ObmMnPmTFyu47+ImTt3LmPHjmXEiBH8/Oc/B+Avf/kLJSUlTJ48mcmT/Vuu9O/fn8P1U5Xnz5/PyJEjGTlyJAsWLGh43llnncVtt93GiBEjuPTSS096zon++c9/cuONN3LppZfy+uuvA/Dyyy+zZs0abrjhBvLz8/nzn/98WgyNxQqwevVqzj//fPLy8hg/fjyVlZUnPe+tt97ivPPOa4g/FoKaKikiIq1o5sx4R9DpbTy8kcGZg+ma1JXS6tJ4h9PuBRK3pipuNosNq2FVxU2klWVlZTF+/HjeeecdrrrqKhYvXszMmTMxDIOkpCReffVVunTpwuHDhzn33HO58sorMQyj0XstXLiQ5ORkCgoKKCgoYMyYMQ3v/eY3v6Fr1654vV6mTJlCQUEB8+bNY/78+Sxbtoxu3bqddK+1a9fyzDPP8Nlnn2GaJueccw4XXXQRmZmZbN++neeff54nnniCGTNm8Morr/Dtb3/7tHheeOEF3n//fbZu3crDDz/MrFmzmD59Og8//DAPPfQQY8eOBeBPf/rTSTE0FuuwYcOYOXMmL7zwAuPGjaOiogKH4/i/Z6+++irz589nyZIlZGZmRvx9aYoSNxGRtmbfPv/Hvn3jG0cn5TN9bCzbyNf7fx3TNFVxi4JAJa2pipthGCTZkpS4Saf2h8//wJYjW6J6z2Fdh/E/4/+n2XMC7ZKBxO3pp58GwDRN7r//fpYvX47FYqG4uJiDBw/Ss2fPRu+zfPly5s2bB0Bubi65ubkN77344os8/vjjeDwe9u/fz+bNm096/1QrVqzgmmuuISUlBYBrr72WTz75hCuvvJIBAwaQn58PwNlnn01hYeFp169evZrs7Gz69etHnz59uPnmmzl69GhQSVVjsRqGQa9evRg3bhwAXbp0aTh/2bJlrFmzhvfee++k12NBrZIiIm3NjTf6D4mLvRV7qayrJLdbLsn2ZCVuUdBSxS3wnjbgFml9V199NUuXLuWLL77A5XI1VMoWLVpEaWkpa9euZd26dfTo0YOamub/G22sGrd7924eeughli5dSkFBAd/4xjdavI9pmk2+l5iY2PBnq9WKx+M57Zznn3+eLVu20L9/fwYNGkRFRQWvvPJKs89sLlbTNJusNA4cOJDKykq2bdvW4v0jpYqbiIjICQLbAIzsNpL9zv1Ue6rxmT4shn7XGa5gErckqypu0rm1VBmLldTUVCZNmsTNN9980lCS8vJyunfvjt1uZ9myZezZs6fZ+1x44YUsWrSIyZMns3HjRgoKCgCoqKggJSWF9PR0Dh48yNtvv82kSZMASEtLo7Ky8rRWyQsvvJDZs2dz3333YZomr776Ks8991xQX4/P5+Oll16ioKCAnJwcwF8V+/Wvf82tt97a8MyAE2NoKtZhw4ZRUlLC6tWrGTduHJWVlQ2tkv369eOhhx7immuu4aWXXmLEiBFBxRkOJW4iIiIn2HB4Aw6bg4HpA0mx+9t0qt3VpCakxjmy9ivQKtlsxc3u0HASkTiZNWsW11577UkTJm+44QauuOIKxo4dS35+PsOGDWv2HnPnzuWmm24iNzeX/Px8xo8fD/hH8o8ePZoRI0YwcOBALrjggoZr5syZw7Rp0+jVqxfLli1reH3MmDHMnj274R633noro0ePbrQt8lTLly8nJyenIWkDfyK4efNm9u/fz+zZs7n99ttxOBysXLnytBgaizUhIYEXXniBu+++G5fLhcPh4IMPPmi4/9ChQ1m0aBHXXXcdb7zxBoMGDWoxznAYzZUiY2ns2LFmS3s7iIh0SvW/ieSjj+IZRaf1rbe+RaI1kWemPsNL217ilyt/yQfTP6BHSo94h9ZuvbP7Hf57+X/z76v+zaCMxn+gueGtG0hNSOWxrz3WytGJxM9XX33FWWedFe8wpJU09v02DGOtaZpjg7lefR8iIiL16rx1bDmyhVHdRgGQavdX2bTOLTItbQcA9WvcVHETEWmSWiVFRNqaH/4w3hF0WtuObsPtczOym39fokCrpBK3yAS1xs2WREV1RWuFJCLS7ihxExFpa664It4RdFqBwSSBiltgY+gqd1XcYuoIgp0qqeEkIiJNU6ukiEhbs3Wr/5BWt/HwRrKSsuiZ4t+nKDCQpNpdHc+w2r3AmP9Ea2KT52gfN+ms4jVvQlpXNL7PqriJiLQ13/2u/6OGk7S6DYc3MKrbqIb9elJs9a2SHrVKRsLlduGwOZrdUkH7uElnlJSURFlZGVlZWU3uEybtn2malJWVkZTU9DrfYChxExERASrrKtldvptvDPhGw2spCf7ErapOrZKRqPHWkGRt/geWJFsSLrcqbtK59OnTh6KiIkpLS+MdisRYUlISffr0iegeStxERESATWWbgOPr2+D4cJJqj1olI+HyuJpd3wb+iludrw6vz4vVYm2lyETiy263M2DAgHiHIe2E1riJiIjgX98GMKLbiIbXEiwJ2AybKm4RCipxs/rfV7ukiEjjlLiJiIgAG0o30L9Lf9IT0xteMwyDlIQUbQcQIZfH1ewebnB84qQGlIiINE6tkiIibc1PfxrvCDod0zTZcHgD5/Q657T3UmwpapWMUDAVt0Bip8RNRKRxStxERNqaSy6JdwSdzsHqg5S6Shs23j5RSkKKWiUjVOOpITMps9lzAoldjUetkiIijVGrpIhIW7Nunf+QVhNY33biYJKAFFuKtgOIUCgVNyVuIiKNU8VNRKSt+f73/R+1j1ur2XB4AzaLjaFdh572Xoo9hYq6ijhE1XEEO1UycK6IiJxOFTcREen0Nh7eyNDMoSRaE097L8WeQpVbrZKRqPHUBJ24aaqkiEjjlLiJiEin5jN9bCrb1Oj6NvAnbpoqGZmgWiXrN+jWIBgRkcYpcRMRkU7tWO0xnG4nA9Ib3wRXiVtkfKaPGm9Ny9sB2DWcRESkOUrcRESkU3PW+ZOytIS0Rt9PsadQ7a7GNM3WDKvDCCRiwVbctMZNRKRxGk4iItLW/Pa38Y6gUwmsX0uxpTT6foo9BRMTl8dFsj25NUPrEAKJWNBr3FRxExFplBI3EZG25vzz4x1BpxJog0xJaDpxA3+Cp8QtdIFhI4GKWlO0AbeISPPUKiki0tZ8+qn/kFYRSNxS7amNvh9I3LTOLTwud33Fzd58xc1iWEiyJqniJiLSBFXcRETamvvv93/UPm6toqFV0t58xU2JW3gCFTeHtfnEDfxVN02VFBFpnCpuIiLSqaniFlvBrnELnKOKm4hI45S4iYhIp9awxk0Vt5gIJXFLsiVpjZuISBOUuImISKdW5a7CwGgysQhU4pS4hSeQiLW0jxvUV9y8qriJiDRGiZuIiHRqTreTVHsqhmE0+n5gkqQSt/CEVHGzquImItIUDScREWlrFiyIdwSdSlVdVZNbAYBaJSMVWLMWVMXN7qCitiLWIYmItEtK3ERE2pr8/HhH0Kk43c4mN98GfxXIaliVuIUpUEFLtrW8B57D6uCg52CsQxIRaZfUKiki0tZ88IH/kFbhdDubrbgZhkGyPVmJW5hCqrjZHGqVFBFpgipuIiJtza9/7f94ySXxjaOTcLqdpCY0vhVAQIo9pWG/NwmNy+Mi0ZqIxWj5d8VJNm3ALSLSFFXcRESkU6tyVzW5FUBAqj2Varc2hg5Htac6qMEkoO0ARESao8RNREQ6tWASN7VKhq/GUxNUmyQc3w7ANM0YRyUi0v4ocRMRkU6t2l3dsFdbU1LtqUrcwuTyuIKuuDlsDnymjzpfXYyjEhFpf5S4iYhIp+Uzff7hJC1U3FLsKUrcwlTjrQkpcQO0zk1EpBEaTiIi0tY89li8I+g0XB4XJmaLFbdkWzJOjxK3cLg8LpKswbVKBs5zeVykJ6bHMiwRkXZHiZuISFszdGi8I+g0qur8kyKb2w4AIDUhFWedErdwuNwu0pOCS8ICFTcNKBEROZ1aJUVE2po33vAfEnOBKlpzG3DD8YqbhmaErsZbg8Ma/FRJUOImItIYVdxERNqaP/7R//GKK+IbRycQqKK1tI9bakIqPtOHy+Mi2Z7cGqF1GKEOJwGtcRMRaYwqbiIi0mkFNtVucThJfUWu2qO93EIVTuKmipuIyOmUuImISKcVmBTZ4nCS+iqbJkuGzuVxhbSPG6jiJiLSGCVuIiLSaQUqbi21PwYSu8D5EhzTNKnxBL8dQCDBU2VTROR0QSVuhmFMNQxjq2EYOwzDuK+R968yDKPAMIx1hmGsMQxjQvRDFRERia5gK26BVspqtxKKUNR6azExQ6+4eVVxExE5VYvDSQzDsAKPAF8DioDVhmG8bprm5hNOWwq8bpqmaRhGLvAiMCwWAYuIdHjPPRfvCDqNQOIWzAbccHz7AAlOYK1aqBU3l1tr3EREThXMVMnxwA7TNHcBGIaxGLgKaEjcTNM88f9kKYDmJYuIhKtv33hH0GlUuatIsCSQYE1o9rxA4qZNuEMTWKuWbAtuEqcqbiIiTQumVTIH2HfC50X1r53EMIxrDMPYArwF3Byd8EREOqEXXvAfEnPOOmeL1TZQq2S4AhW3YFsl7RY7NsOm4SQiIo0IJnEzGnnttIqaaZqvmqY5DLga+FWjNzKMOfVr4NaUlpaGFqmISGexcKH/kJhzekJL3DScJDShtkoGztV2ACIipwsmcSsCTuzb6QOUNHWyaZrLgUGGYXRr5L3HTdMca5rm2Ozs7JCDFRERiSZnnbPFzbfBn0xYDIu2AwhRqBW3wLlK3EREThdM4rYaGGwYxgDDMBKA64HXTzzBMIwzDcMw6v88BkgAyqIdrIiISDRVuauCqrgZhkGyLVmtkiEKp+KmxE1EpHEtDicxTdNjGMZdwLuAFXjaNM1NhmHcXv/+34BvAt8xDMMNuICZpmlqQImIiLRpTreT7sndgzo3xZ6iVskQBYaMhNoqqTVuIiKnC2aqJKZpLgGWnPLa30748x+AP0Q3NBERkdhyup0tbr4dkGJPUatkiBoqblZV3EREIhVU4iYiIq3o5ZfjHUGnUeWuanHz7YBUe6oStxAF9mNz2EOsuGk7ABGR0yhxExFpa7qdNttJYsTpdgaduCXbk5W4hSiQgCVZgx9O4rA6KK8tj1VIIiLtVjDDSUREpDU9+6z/kJhye93UemuDGk4CapUMR7XHP8xFa9xERCKnxE1EpK1R4tYqAkmYErfYqfHUkGBJwGqxBn1Nki2pIeETEZHjlLiJiEin5PQocYs1l8cV0h5uoIqbiEhTlLiJiEinVFXnH+0fzAbccDxx0243wXN5XCG1SYKmSoqINEWJm4iIdErhtEp6TahoeI4AACAASURBVC+13tpYhtWh1HhqQk7cHDYHbp8bj88To6hERNonJW4iItIpBTbTDiVxA9QuGYJwKm6B89UuKSJyMm0HICLS1ixZEu8IOoVqt38ARij7uIE/cctyZMUsro4k3Iob+LcSSCW4742ISGegxE1EpK1JTo53BJ1CqBW3ZLv/+6KKW/BcHlfQawgDAsNMXG4XhJbziYh0aGqVFBFpax591H9ITAUSsGArbmqVDF21pzrsipvLqwElIiInUuImItLWvPii/5CYClTcApW0lpzYKinBqfHUhLwdQJI1qeFaERE5TombiIh0Sk63k2RbMhYjuP8VqlUydJEMJ9GWACIiJ1PiJiIinZLT7Qy6TRKOV9wClTppWY03guEkqriJiJxEiZuIiHRKVXVVpCQEN5gEjq9xC0yjlOaZponL42pofQyWKm4iIo1T4iYiIp1SqBW3QELh9KhVMhh1vjp8pi/oNYQBDVMllbiJiJxE2wGISEy8tq6YwsPVfO+SwfEOpf356KN4R9ApVLmrQkoqLIaFFHsKVXVqlQxGoNUx1IqbEjcRkcap4iYiMfHsp4U8++nueIch0qRQK24AKbYUqj1qlQxGIPGKZANuERE5TombiERdncfHppIKjla7qa7zxDuc9uehh/yHxJTT7Qx68+2AZHuypkoGKdzELdGaeNL1IiLip8RNRKJu64FK6jw+AEqO6YevkL35pv+QmKpyV4VccUu1p2qqZJACiVeo+7hZDAsOm0NTJUVETqHETUSibl3RsYY/Fx1V4iZtj2maYVXcUuwpmioZpHArbuBfF6eKm4jIyZS4iUjUrd93jASr/5+XkmP6rbm0PTXeGnymL6zETRW34AQqZuEkbg6bQ4mbiMgplLiJSNSt33eM8wZlYbUYFB9TdULansA6tZCHk6jiFrSIKm42VdxERE6lxE1Eoqqq1sOO0irGnJFJzy5JqriFw+HwHxIzgZH+oWzADRpOEopIEjetcRMROZ32cRORqNpQVI5pQl7fdP6z8zDFWuMWurffjncEHV64FTcNJwleuMNJAteo4iYicjJV3EQkqtbXDybJ65NBToaDYk2VlDYokLiFs8bN4/NQ562LRVgdiipuIiLRpcRNRKJq/b5j9MtKJjMlgZwMBwcqavB4ffEOq3351a/8h8RMoGoWTuJ24vXStEDiFU7FTcNJREROp8RNRKJq/b5j5PXJAKB3hgOvz+RQZW2co2pnli71HxIzkQwnOfF6aZrL48JmsWG32EO+1mFzUONVxU1E5ERK3EQkag5V1FBSXkNeX3/ilpPpb5FSu6S0NZFW3DRZsmU13pqw2iRB+7iJiDRGiZuIRM36onIA8vumA5CT4W+RKlHiJm1MJGvcQK2SwXB5XDis4SVuapUUETmdEjcRiZr1+45htRiM6O1P3Hpn+H9oK9JkSWljnG4nNsNGojUxpOvUKhk8l9uFwx5mxc2WRI2nBtM0oxyViEj7pe0ARCRq1hcdY1jPNJLsVgCSE2xkJttVcQtVVla8I+jwquqqSElIwTCMkK5Tq2TwXF4XSdbQB5OAv+JmYlLrrQ1ruImISEekxE1EosI0TdbvO8Y3cnuf9HpOprYECNkrr8Q7gg7P6XaGPJgE1CoZCpfHFf4at/pkrcZTo8RNRKSeWiVFJCoKy6qpqPE0rG8L6J3uUMVN2pwqd1XI69tArZKhqPGEP5wkcJ3WuYmIHKfETUSiYv2++o236ydKBuRkOig+6tJalVD8+Mf+Q2Km2l0dVuKWbEsGlLgFw+VxhV0ta0jcvErcREQC1CopIlGxbt8xkhOsDO6aAIe3Q7fBAORkOHDWealweUhPDn0/p05p5cp4R9DhVbmryEzKDPk6q8WKw+ZQ4haESCpugbVxqriJiBynipuIRMX6omOMzEnHuvRBePRcOLIb8CduAEXHNMxB2o5w17iBv11SiVvLIlnjFphGWePRJtwiIgFK3EQkYnUeH5tKKji/pwlrnwWfB1Y+DBzfhLvkmH4Ak7Yj3DVuAKn2VCVuQYhoOIkqbiIip1HiJiIR23qgkjqPj29UvwaeGhhwEXz5D6g61LCXW/FRVdyk7XC6nWEnbsn2ZCVuQYjGcBJV3EREjlPiJiIRW1d0jFSqGbT7n3DW5XD5n8BTC6sWkpWSQKLNoi0BQtGnj/+QmPD6vLg8LrVKxpDb68ZjeiIfTqKKm4hIAyVuIu2Ez2fi9vriHUaj1u87xpzkj7DUVcCEH0DWIBh+Fax+EqO2gpwMh1olQ/GPf/gPiQmnx590hVtxU+LWssA0SG0HICISPUrcRNqJe19ezzcXfoqnDSZvm/ce4jvGEhg4CXLG+F+c8H2orYA1z5CT6aBIFTdpI5x1/qQrNUEVt1hxuf3/vYdbcQtcp8RNROQ4JW4i7cTaPUcpKCrn2U8L4x3KSapqPYw+soQM7xF/tS2g92gYOBlWPcoZaRZtwh2K73/ff0hMBJKuZHtyWNdrOEnLAglX2MNJ6hM3rXETETlOiZtIO+Cq87L3SDU2i8Gf3t/GgfK288PMmp2H+K71DSqz8mDAhSe/OeEeqDrIlLqllFbWUuP2xiyOQ5U1HWeT73Xr/IfERJW7CiDsNW4aTtKyGq//36hwEze7xY7NYmu4j4iIKHETaRd2llZhmnDv14fi8Zn86q3N8Q6pQdnqFzjDUkri5HvBME5+c8CFkHM240v+gRVvzBLO0spaJvx+Ga+vL4nJ/aVjCSRdYQ8nsaVQ56vD7XVHM6wOpaHiZg0vcQN/0qdWSRGR45S4ibQDOw75KwRThnXnzsln8lbBfpZvK41zVIBpkr/nWYrs/UgYfvnp7xsGTLiH1Op9XGb5LGaTJbcfqqTO6+Pz3Udicn/pWAIVt7D3catfG6eqW9MaEjd7BImb1aFWSRGREyhxE2kHth2sxGYx6N8thTkXDmRAtxR+9trGmLYeBqNs3RsM8hWyY/AtYGnin5Oh38CdMYi5tjditpfbnjL/fTeVVMTk/tKxRFpxS7b518YFplPK6QKJW2Aj7XA47A6qPdr/UUQkQImbSDuw/VAVA7qlYLdaSLJb+cWVIygsq+bx5bviGpf3kz9TZHaj1wU3Nn2SxYJxwfcYbtmDd9+amMRRWOb/AXrLgQq8vg6wzm3IEP8hMRHxcJL6iltVXVXUYupoIh1OAv6kTxU3EZHjlLiJtAPbD1YypEcaFK6ADS9zYXopV47qxsPLdrCnLE6/9d+/nu5H1vCy7RsM6Z3Z7Km2YV8HIPVgbBK3PYf9v5WvcfvYVdoBfph+/HH/ITERaatkis1/napBTQskXJEkblrjJiJyMlu8AxCR5tW4/RMlvzmqKyy6Dtz+Hxb/bLGx09qTg08N5Ixp/4UxanqrxuVbtRAXSRwePBPj1KEkp0rryQFLT7qXr49JLIVlTnIyHBQfc7GppILBPdJi8hzpGJx1Thw2BzZLeP8LDFTqtMataQ2tkmHu4xa4VhU3EZHjVHETaeN2llbhM+EC3xp/0nbVo/DNpzAu+B727EHkODdhvHILvPcA+Fppc+7Kg7DhFV70XMS4Yf2DumRvykgG1W6CKI/sN02TPWXVTDmrOwk2C5tKyqN6/7iYM8d/SExUuavCrrbB8bVxgcqdnC6QcAXWA4YjyZakipuIyAmCStwMw5hqGMZWwzB2GIZxXyPv32AYRkH98alhGHnRD1WkcwpMlBxc+h6k9YK862HUdJjyM3Lm/pvfnrmY/+f5Gnz6F3jpv6CuFdq31jyN4XPzd++lTDizW1CXHM3Kp5t5FN/RvVENpbSyFpfby5ndUxnWM61jDCjZts1/SExUu6sjStwC11a71SrZFJfHhdWwhl3VBH+rpPZxExE5rsXEzTAMK/AIMA0YDswyDGP4KaftBi4yTTMX+BWgxRkiUbLtYCUZFhepe5fB8KvBYm14z2a1sOBbY/ls2I/5pftGzK/egL9f7q+IxYq7BtY8xReJ40ntPZSs1MTgLus1FoDKHf+JajiF9RMl+2WlMKJ3FzaVVHScjbglJiKuuNUPJ6msq4xWSB2Oy+PCYXO03EbdDIfNgcutipuISEAwFbfxwA7TNHeZplkHLAauOvEE0zQ/NU3zaP2nq4A+0Q1TpPPafrCKWV02YHhrYeQ3T3vfbrXw51mjKR15C3Pq7sG9fxM8eQkc2NDQlni4qpbHPt7J9Y+vZO2eCPc62/gKOEtZUHUJEwdnB31Zcp9cnGYidbtXRfb8UwQmSvbPSmZ473TKXe6Y7RfXEc1fO5/VB1bHO4xW5XQ7w94KAPytkjaLjSM12jewKYHELRIOmwOXV/8ti4gEBNPDkAPsO+HzIuCcZs6/BXg7kqBE5Ljth6q427IS0s+APmMbPcdmtfCnGXnca8A167vygvVPpPxtAh5bCgcsPdlUk4Xp685A6xnc+vQEnpszgZE56aEHY5qwaiGV6UP45OBw5g4Ork0SoHdWGut8ZzJyf3SThD1lTmwWg5wMByN6dwH8+7n1yQx/bU1nsat8F89sfIaiyiLG9RwX73BaTZW7ipzUnLCvNwyDroldOVpztOWTOymXxxXRYBLwbwegNW4iIscFU3FrrM+h0T4kwzAm40/c/qeJ9+cYhrHGMIw1paWlwUcp0knVuL2Ulx1guGsNjLwGmmk7slkt/HFGPkNGT2Ry5S/5o+UmnquZwO66LoxJPsicxPf5rfEo/7L8iAVPPcuOQ2G0eRWugIMbWJp+LckJNs7u1/w2ACfqneFgrTmYtPKtUBe9aXyFZdX0yXRgs1o4q2cXLEYH2Ig7P99/xNiHez8EYP2h9Z2qvdTpdkbUKgmQkZShxK0ZNZ6aqFTcPD4Pbp87SlGJiLRvwVTcioC+J3zeByg59STDMHKBJ4FppmmWNXYj0zQfp37929ixYzvPTwkiYdpV6uRrljVYTW+jbZKnsloM/m96Hr9KsvPFoTOZMbYv40b0JMlu9U+c3P4efd68lycrf8Ybf/sIxy1/ISenb4v3bbBqISRnsbBsDOcO7EqizdryNfW6JNn5ynYWFvPfUPwFDJgY/HObsafMSb8s/w/hjgQrA7NT2dzeJ0suWNAqj/lgzwcAHHId4oDzAL1Se7XKc+MtGolbZlImR2uVuDUlKhW3+utrPDXYE+zRCEtEpF0LpuK2GhhsGMYAwzASgOuB1088wTCMM4B/ATeapqlRaCJRsv1QJVdYPqUufQD0zA3qGqvF4MErR7Do1nO5Kj/Hn7QBWCwwdCr2uz+nbPSdTPV9TMoT51H+6TPBjeg/sgu2LqF8xI1sLfMwMYQ2yYDSLvVfw77PQr62MaZpsudwNf2zjrdFBgaUSPMOOA+wqWwTX+v3NQDWl8Zmj722xjRNqtxVEa1xA9Qq2YJorXEDtJebiEi9FhM30zQ9wF3Au8BXwIumaW4yDON2wzBurz/tZ0AW8KhhGOsMw1gTs4hFOpHifXs4z7IZy6hvNtsmGZKEZLKu+i07r32HnWYO6e99n7rnrgPn4aavMU1YsQAsNj5MvQIgpMEkAelds9lr6Qv7Pg83+pMccdZRWetpqLhxaAu3Vj3GoXInR5x1UXlGXHz72/4jhpbuXQrAHXl3kGRN6jSJW52vDo/P0zAZMlyZSZlK3JpR441OqySgdW4iIvWC2sfNNM0lpmkOMU1zkGmav6l/7W+maf6t/s+3mqaZaZpmfv3R+AQFEQlJ5p4lWA0TW+70qN97WO546m58k196Z8Ouj/A+ch7s/PD0E4u/gKcuhS/+DqO/zXv7DHqnJzEoO/RWs94ZDtb4BkPR51HZLDywFcCAbvWxvP0jRhU9z2hje/veiLuoyH/E0NK9SxmUPogzM89kRLcRrDu0LqbPayuq6vz7IkajVbLSXYnbq/VXjXF5XDisStxERKIpqMRNROJjxJGlFCcMgO5nxeT+552ZzcXfeYBZ/JbC6gR47hp47wHw1EFVKbx2FzxxMRzdDVc9gmfaQ6zYcZiJg7PD2p8pJ9PBSveZ4DoKZTsijn9P/VYA/bKSYfcnsPtjACZaN6hdshlHa46y9uBapvSbAkBedh5bjmzpFC1pgU2zI03cuiZ1BdA6tya4PC4c9sgSt4Y1btqEW0QEUOIm0mbVHtlLrm8zhT0ujelzJgzuxvy7b+AH6X/iH94p8OlfMB+/CP46BtY/D+fdCXevhdHfZn1xJZU1HiYOCX19G/grbl/4Bvs/KYq8XbKwrBqLAX0yHLDsN5DWC3qOYop9kxK3Zny07yN8po8pZ/gTt/zsfDymh81lm+McWexVuaNXcQPULtkEl8dFkjWy4SSquImInEyJm0gbdWz1iwDUDLs65s/ql5XC83dezOoRD/DdunuoOlyMJ2c83LEK39d+TZHLzrIth3jyk10YBkw4M7zELSfDwS6zF+6EjKgMKNlT5iQn00HCno9g70qY+EMYMo3h5g4Ki4sjvn9HtXTvUnqn9Oasrv5Kbm62f2jMutKO3y4ZSNwiHU6SmehP3LQJd+OiOZwkUCUVEensgtkOQETiIGHLa2zw9Sdn0MhWeV5ygo0FM/N5pk8Go5eMpdeBZDIXH2THoZ1U13kbzvva8B5kJCeE9YxB2SlgWChKGcGAKAwoKSyrpn/XZPjwx5DeF8Z8B4rXYln+v/Q+ugZn7VRSEtvhP3PnnRezWzvdTlaWrGTG0BkN7a5Zjiz6pvVl/aGOP6DE6fa310Y8VTLQKqmK22ncPjcenyfixC0tIQ2Ayrow9pwUEemA2uFPNCKdwKEtZB4t4DHft7inW2QtXaEwDIObJwxgeO8u/G7JV6Ql2Zkxti9DeqQxuEcqZ2ankpkSXtIGkJGcQG6fDD6tHsSAo//xr3VzBL+J96n2lDn5Yb+dsPsLuOIvYEuEPuPw2FKY6Clgy4EKzu7XNez7x83vfhezW39S/Al1vrqGNsmAvOw8VpasxDTNsNYvtheBxC3ZntzCmc1raJXUGrfTBNZKRrqPW5eELgBU1KntWUQElLiJtE2f/Y06I4FV6dNC2uQ6Ws4dmMVrd02Iyb0nDcnmzWV9uSEBKFoDg78W1n2OVddRXl3LtNJnIHMA5H/L/4bVjqfv+UzYWcDyknaauMXQh3s+pGtSV0Z3H33S6/nZ+by5602Kq4rpk9YnTtHFXrQqbumJ6VgMi1olGxFI3KJRcTMwlLiJiNTTGjeRtqb6CKxfzHu2SfTo2Tve0UTdpKHZrPcNwmdYI9rPbU9ZNV+3rKFb1VaYdB9Y7Q3vJQ69hP6Wg5Ts3hKNkFvfN7/pP6KszlvH8uLlTO47Gavl5F8I5HXPAzr+OrdoDSexGBYyEjPUKtmIwDCRSBM3i2Eh1Z6qVkkRkXpK3ESaYpqw9R2oPNC6z137LHhcPFx9CUN6pLXus1tBbp8MEpPTKE4cFNGAksLDFfzA9hJ1GWfCqOtOes8YdDEAKcWfRBRr3JSV+Y8oW7V/FU63k4vPuPi0987MOBOHzdHh17lV1VVhMSwRJxXgH1CixO101R7/MJFkW2TtqABdErtQUauKm4gIqFVSpHFeD77X52FZvwifYWNT+iQ+734d6UMuYFSfDAZlp2CzxuD3Hl43fP4Ezj4XsmVHH+Z2j6ydqy2yWgwuHJLNp1sHMaP4EwyvB6yh/1OUuOU1hliKqZv8JJxSPaLbYCoSujO48nPcXh/2WHyv2qEP935Iij2Fc3ude9p7NouN3G65rC/t2IlbtaeaFFtKVNbxZSZlqlWyEeW15YA/6YpUl4QuapUUEamnxE3kBEeddTzy/kambL6f8+pW8jfPFdjwMPPoR4w69gEbtvTnCe/Xec8ykcG9szi7XyZ3X3wmaUn2lm8ejM2vQWUJG0f+DHbQIStu4G+XXFYwkJnm23BoM/TKDfkeA4peZZ/Ri76jGmkpNAyO9ryAc/e8x/b95QzvE/4AlI7C6/OybN8yLsy5kARr4wNmcrNzeXrj01S7qyMe3tFWVdVVkZIQnYE/mUmZ7DgW+UbyHc2x2mMAZCRmRHyvtIQ0JW4iIvX0a2iReq46L3c88zEXf3EX59Wt5L0z7qHX9D8w+XtPkvLj7fgu+yNDs+w8ZH+MJWm/pavvCE+t2M33F6/D5zOjE8SqhZB1Jv8xRmMxYEArTpRsTRcOzmatOdT/ye6PQ79BTTmDnOv4MmUCWBr/Zyxp2NfIMJyUfLUygkg7ji8PfcmRmiNM6TelyXPyu+fjNb1sKtvUipG1LqfbGfFgkoCuSV3VKtmIQMUtPTE94nt1SeiiNW4iIvWUuIkAHq+P+/7xEfcd+hHnWrfANY9x6c0PclV+DoOyU7EkpWEZfysJ81bD9Kfp4y7kibr7mH+RnaVbDrFg6fbIg9i3GorXwDm3s/2Qk/5ZKSTZW3+iZGvISk2kW84gdtkG+quModrxATa8lPSY1OQp2aMuBcDYtSzMKONoyhT/EUVL9y4lwZLAxJyJTZ6T281f+ezI7ZJV7qqIB5MEZCZlUl5bjtfnbfnkTiSqiZvWuImINFDiJp2eaZr8/pUV3Fl4NyNtRVhm/gPyrm/8ZMOAkd+Em94Gn4cr187mp0P28Zel23lvU4RDTFY9ConpkDeL7YeqOLMDrm870UVDsvlXzVgoWg3lRSFd6978FmVmGkbf8U2eY0nLZpdtEL0Ot8OK2wMP+I8oWrZvGef1Pq/ZFsiMpAz6d+nfoQeURLPilpmYiYnZ0BoofuW15ThsDhKtiRHfS2vcRESOU+Imnd5flu4gb8NvGGg9hPXGf8Gwy1q+qHc+3PYhRteB3LLvx/w462N+8OJ6dhyqCi+I8iJ/5ens71BnTabwsLPDrm8LuGhod970nuP/ZPPrwV/odWPZ8T4fekfTr1vzf0clXc9lcN1mvDWdu9XqSM0RiquKGddzXIvn5mXnsb50PaYZpfbfNsbpdkZt/V7DJtxqlzxJeV15w+bZkUpLSKPGW0Odty4q9xMRac+UuEmntvjzvaz/cDFXWFdhnfQjGNB0G9lpuvSGm97GGDKN7zof4/eWR7jv2feoqHGHHsjnTwAmtWNu5YlPduHxmQzu0bErbvl9MzjmOIPixDNh87+Dv3DvSqx1FXzgO5t+Wc23vPkGXozd8HKgYGmE0bayadP8R5RsO7oNgKFdh7Z4bl73PI7WHmVv5d6oPb8tqXJXRa/iFkjcapW4nehY7bGoDCYBGhJAVd1ERJS4SSf24ZaD/Pbfq/k/x98xs8/CuOD7od8kMRVmPgcT7+Uy41P+7rydD//2Q3w1IVTeKvZjrn2W4l6XMOWpXfzfu1uZOLgbl5zVI/R42hGrxWDi4Gxec4/z7+dWXhzchVvfxmMk8IlvFP2ymq+c5OReRI1px/nV+1GIuBW5XP4jSrYe2QrAkMwhLZ6bn50PdNx1bk63M3pr3BJjU3EzTZN1h9ZR46mJ6n1bS0VtRVTWt4ESNxGREylxk07JWevhv18q4Ndpr9LVexjjyr+CrfER6S2yWGHKA1ju/IzSHhO5+tjfcf4xD98Xz0FzQwvKdsLr8/AtyMVbU8kduyeQlmTn/908nuduOYeUxI6/W8ekIdm85Brr/+SrINolTRO2vMX2lDGkpqWTnND839GAnt1Yy3AySlZEIdr2a9vRbWQ7suma1LXFcwdlDCLVnsq6Q+taIbLW5TN9/jVuCdGbKgnRTdxcHhf3r7ifG9++ke++/12q6sJsv46jY7XHopa4pSX426E1WVJERImbdFJPr9hNv+qNXFH7Jsb4OdC35bU/LcoaRL+5L/PYmY+yozYDy+t34fvTKHh+Fiz9JRS8BAc2QPFaeGk25sNjcX/5TxbVXcishIf5r+u+yVt3T+DCIdmRx9JOXDgkm91mLw6nDIZNQbRLlm6BY3v4xDKO/i20SQJYLAa708fRvbYw+IpeB7Tt6DaGdG252gZgMSyM6jaqQ1bcAj/8R6tVMiPJ3w54pDY6m3Dvq9zHjUtu5K1db3H5wMtZX7qeOe/PaZjS2F6U15ZHr+JWv4m3JkuKiChxk07oiLOOp5dv46+pz2J0yYEp0ZveZxgGc274FusvfYl5nnksr+5H7aHt8J8/w79uhb9NgCcupm7r+zzlu5LJnr9Sdcn/8tx/z+TaMX2wWIyoxdIeZKclMionnfc5D/atgoqS5i/YugSA16pbbpMMqD3jQgDcO8PYL64DcPvc7Dy2M6g2yYC87nnsOLYjKtWePRV7cHmi1/YZiYPVBwHokRKdNmS7xU5aQlpUKm7Li5Yz882ZlDhLeGTKI/xu4u+YP2k+W45s4ZZ3b6HMVRaFiGPPNE3Ka8u1xk1EJAaUuEmns/CjHdzo/Rc57kK4fD4kRnd6o2EYzJ4wkBtvvYf/tvyQ/MO/5o0r1sLclRy89FEeS/8eZ1ctYFnfufzz+1cxd9KgDrtfWzAmDc3myaP+/cNanC659W28vfLZVJVK/yA3J+89ZCzlZjIVWz6KLNDWdPnl/iMKCssLcfvcDM1seTBJQH52Pj7Tx4bDGyJ6tsvj4ro3ruOpDU9FdJ9oOeD0b9nRM7ln1O4Z6SbcPtPHwnULuWvpXfRO6c0Ll7/AxD7+IUkXn3Exf734rxRWFHLTuzdxqPpQtMKOmWpPNR7TQ3qC1riJiESbEjfpVPaXu/hk5afMs7/m349tyNdj9qxx/bvy1t0TGJnThbtf2MSt7ziZuKQrj5RfwAPTz+Uft5zDGUFWjTqySUOz2enrTUWXIc1Pl6w8CEVrOJLj35g6mFZJgNwzuvK5bxj2ona0n9u99/qPKAhMlAyl4jYqexQGBgWlBRE9e8uRLbg8LtYeXBvRfaKlIXFLiV7ilpmYGXbi5jN93LPsHh5d/yiXD7yc5y57jr5pfU8654KcC1h4yUIOOg/yX2//F8VVbbvlN7CnXbSHk2iNm4iIEjfpZP78wXb+x/IPLAnJMPX3MX9e9y5J/PO2c7npgv588NUhpgzrzgc/vIgZY/tiGJ2rLbIpm4+3IQAAIABJREFU+X0zSXfY+TRxIuxdBRX7Gz9x+7uAybYMfzUi2FbJ3ulJbLSPokv13qbv3YFtPboVu8VO//T+QV/TJaELvVN7s/PYzoieHUj8NpVtwuPzRHSvaDjgPIDVsJLtiN460sykzLDXuG0/up0P933Id3O/y28m/AaHzdHoeeN6juOJS5+gvK6c2e/MZl/lvkhCjqnAerxoJW52qx2HzaE1biIiKHGTTmTHoSp2rX2fyZYvsUz4PqR2b5Xn2q0Wfn7FCNb+9BIWfvtsuqcltcpz2wurxeCiIdk8dngUYDY9XXLr25Delw0ef0Ui2MTNMAwqe4z3f7LnP1GIuBVMmuQ/omDbkW0MyhiE3WIP6boB6QPYVb4romdvPLwR8LdM7ji2I6J7RcMB5wG6J3fHaolea3IkrZKBaujU/lNb/EVObnYuT3/9aard1cz7cF6bnTYZ7cQN/JMl1SopIqLETTqR+e9t4X/sL+JL6QHn3N7qz89KTWz1Z7YX3z63H1+6unM09czGp0u6XbBzGQydxoaSCrqlJpCWFHwikjnobCpNB3W7Poli1O3DtqPbQmqTDBiQPoDCikJ8pi/sZ284vIHhWcMBIm67jIb9zv1RbZMEf8XtWM0xTNMM+drtR7djt9jpl94vqPOHdR3GHyf9kd3lu/nxJz/G29x2I3ESSNyiNZwE/BVgtUqKiChxk06ioOgY1Zvf4WxjC5ZJP4IErS1rS8b1z2T0GRn8q3Yc5t6VUHng5BN2fQweF8U9JvP2hv1cnZ8T0v1H9c1ijW8Inl2daz+3IzVHKHWVhpW4DUwfSK23lv3O8NpLj9QcobiqmKn9p9I1qWub2F7ggPNA9BO3xEw8piesitC2o6FXQ8/tdS4/GvcjPir6iIfXPRzyM2MtFhW3LgldVHELgWmaFJYXxjsMEYkBJW7SKTz0zlf8OOElfBn9YfR34h2OnMIwDG6/aBD/rBqDgQn/nAnPfwte/C945Tb48NeQkMb/be1Gkt3K3EmDQrp/Xp8MPvOdRXL5DqgqjdFX0fYEWvGGdg1+omTAgPQBAOwu3x3WswNtkqO6jSI3OzfuFTef6eNg9cGYVNwgvE24tx/dHlZSPWvYLKYPmc6TG57krV1vhXx9LDUMJ4nSVElQ4haq5UXLueLfV0S8RlVE2h4lbtLhrS48QsauNxlKIZaLfwq2hHiHJI342lk9MLOGsCRxGqbFCsf2wqGvoGg1uI5QNuI7/LuglJsu6B9y22lmSgKFqaP9n7SXdW5RsPXIViC0iZIBgcRt17Hw1rltPLwRi2FheNZw8rLzKKwojOtG0kdqjuD2uaO6FQD417gBHK0NLXE7WnOUQ65DYX1vDMPg/vH3c3aPs/n5pz9vSJLbgvK6cpJtyditoa2pbE6XxC4aThKCQHU78IsbEek4lLjJ/2fvvuPbrK4Gjv8eDQ/ZluTtxPFeSezshAAZEMimYZcdVlghFCirjLaU0ZZRRst4IayyWlYCZWUBIZPsBDvLduK9tzxkyxrP+4eQyXBs2dbwuN9+9IFEj+5zTVJbR+fccwa997cc4T6fT7FFpNtHAAj9kkIhccvMRG43LGbrrE9g6Wa4YwfctQ/uOcjDjRcT5Kvi5hmJvVrfN24SRvwGRuB22WX2Rx/l1OcQ7h/eEVz0RLBvMDpfHfmNvcu4ZdZkkqRPQqPWMDbMPqevr3Ph+sIxCmBYwDCXruvIuNW19ayzZG59LgApwSm9uq9aqeb5s58n1C+Uu364q9/MeHPl8G2HIJ8gccatB7Lr7R/YFDQWeHcjgiC4nAjchEGtusmE7vBHxFKJ4tw/g0L8le/PLpwQTXiQL69tOL7EJ6vEwJoDlSyZkYBe07uM6ZiYUHZZU7AMhAYlt99uf/RRbxuTgD2rk6hL7FWppCzL7K/Zz5iwMQCkh6WjkBReLZd0nNVzdalkR8ath6WSvZmv19m9/3XOv2gyN3H3+rtps7T1ei1XMZgMLj3fBr80JzE39ctmLP2RI9Ne2Fjo5Z0IguBq4l2sMKit3J7DHYoVtA6b4tZh24Jr+KmV3DgtgU25Newv/bWs7vl12eg1am6cntDrtcfF2M+5qWoOgbF3c7c8xmi0P/rAbDNztOEoqSG9DwwSdAm9CtxKmkowmAxkhGUAEKAOIFmf7NXAzd0Zt94EbiF+IYT5h/Xp/mkhafx9+t/JqsliRe6KPq3lCg2mBrcEbgDN5v45AqE/aWhroNJYCUChQQRugjDYiMBNGLSsNhm2vUqk1ID//MdBDLweEK6aGkugr4rlG+1nq3YX1rM+u5pbZiai7cEIgBOlD9eyUx5p/0XhVlds1X0WLrQ/+qDAUIDZZu5TRidBm0BdW12Pz6Y5SiIdJZJgn0OWWZPZp/ECfVHRUoGf0s/lQYWv0heNStPjUsm+ZENPdG7cuSTrk/mu8DuXrNcX7si4BfkEAYgGJU5wlEnGaeMobCrs1ZgKQRD6LxG4CYNW1oaVLDF/RHn0PIg709vbEZyk81dz1dRYvskqp7jOyPPrsgkN8OG6M+L7tK7GR0Vr+HjaJZ/+H7i5QEdHyeCed5R0SNTbzxP2NOuWVZOFn9KPJP2v3T/Hho2lqb3Ja+duHDPcuht03RvBfsE9ak5itVk50nDEZYEbwOy42eyp2kNta63L1uyNxvZGl3aUhF8zbiJw697husMAzI2bS1N7U4+b5giC0L+JwE0YnKpzSN34O/KlEYRd/Ya3dyP00I3TElBIcNdHe9lypJalZycR4Kvq87qjY8LZJ6cgF/Z8ntvnuZ+zr2pfn/fgKdn12agVauJ18b1eI0Hbu5EAjsHbKsWvf2bjwscB3hvEXdni+lEADsG+9iHczipqKsJkNbk2cIudjU22sb54vcvW7ClZlt1zxs33l8BNdJbsVk59DmH+YUyIsHfRFefcBGFwEYGbMPgY67B88FuMNiXrJ76EWuPaNxGC+0Xp/LhwfDR7ihqI1PpyzelxLll3bIyOrZY0qMiCNufL/1rMLTy+7XHe3v+2S/bhLFmW+deef7G1rOcZwt4Mdz7R8MDhqBVq8gzOjwQw28wcqj3Ucb7NIV4XT5A6yGuBmzuGbzsE+wX3qFTSFY1JTpQanMqIwBF8V+S9cslmczNW2eq2M24i49a9w3WHSQtJI14bDyAGcQvCICMCN2FwsbTDJ9dCYym3me/hNzOnentHQi/delYiPkoFd52bip9a6ZI1HYO4JdkGRducft228m1YbJZeD6PurR+KfuCNrDd47efXevzanLq+n6FSKpTEaeN69HXn1ufSbmvv6CjpoJAUjAkf45XAzWw1U91a7dbArSclaTn1OSglZUcpqitIksScuDlsL9/utQDHMXzbHeMAADESoBtmq5k8Qx5pwWkMCxyGSqESGTdBGGRE4CYMHrIM394HBZt4XLoNfdoMhuv9vb0roZeSI4LY+chsrpoa67I106KCOKBIwyKpocD5csnNpfZri5uKMVvNLtvPKV1/PebFV/PsrmeRkNhbtZfKlkqnX17XVkd1a7VLMjo9HQngGAZ9YsYN7A1KchtyMZr71jGzp6paq5CRXd5R0iHEL4T6tnqnG0Hk1OcQr43HV9mzQfLdOTfuXCw2CxuKN7h0XWc5ShlFxs07jhqOYrFZGBkyEpVCxYjAESJwE4RBRgRuwoBU22zii72l1Le023/DUALfPwZ73iU39RbeM57B1ae77g2/4B06Te/L/DqjVipIHh5GrirV6UHcsiyzuXQzfko/rLLVM2+Err+etyfbKG0u5aGpDwHwfdH3Tr/claV4CboESppLaLe2O3V9Vk0WIX4hRAdGn/Tc2LCx2GQbB2oP9HlfPeEYBRClcV/GzWQ10Wppder63Ppcl5ZJOowJG0OEJqJHf1dcyV0ZN3+VPyqFSpxx64ZjfpujIVG8Np7CJhG4CcJgIgI3YUCx2WQ+2lHE7H/8wLuffMqnz9xK9bNT4IV02PwCpF/Mn5suJCbEn7NSwr29XaEfGjtCzwZTKnLZPjB1X3qVZ8ijoqWCRUmLOn7tbpWFB/jsp9eYHTubK0deSZIuiXWF65x+fU7dLx0lQ3rfUdIhQZeATbY5HbBmVWeREZbRafdGR/nkz9U/93lfPeGu4dsOwb72WW7OnHNram+itLm0T/P1TkUhKTg39ly2lG7xeFYT6Bgb4Wgm4iqSJNmHcItSyS5l12fjp/QjTms/ExynjaOoschrIzgEQXA9EbgJA8bhikZ++/pPPL5yJ18pH+Bz30dZIn1BXpOCF6XFrDjjcw6c+QI/5Tdw1WlxKBRibptwsvExejZbRiLJVije3u31jjLJa0ZfA/S8w2JvNF+4gL//8wj3Tr4XgDnxc9hduZua1hqnXp9dn02YfxghfiF93kuizvmRAM3tzeQZ8jotkwTQ++mJ18Z7/JxbR8bNTYGb47+zM0O4jzQcAVzbmORYs2Nn02ZtY0uZcxllV3Jk3Fw9DgDs5ZKiVLJr2XXZJOuTUSrsZ4LjdHGYrKYelVkLgtC/icBN6PdaTBb+9u0hzvvXZvKqm/lk7B5GWIvgNy+gfOAoPjetZsfwa7h3fSsXvLIVtVLit5NHeHvbQj81LkbPblsK7cpA2PQC2Lr+NHpT6SaS9ckk6hIZFjDM7Rm3PZV7qGutJSogihFB9r/Hc+LmICPzQ9EPTq2RW5/bp/ltx3J8eu9M4Haw9iAy8kmNSY41NnwsmdWZHh0MXNFSgdZHi0atccv6wX72jJszDUoc2VB3BW4TIycS7Bvcowytqxja7Rk3V59xAxG4dUeWZbLrs4/Lsnd0lvTS7ERBEFxPBG5Cv/e7/+5l+cY8Lp04gvVLx5BR+B6M/A1MvhE0IUyIDeY/N5/OB0umMjE2mBumJRAW6NpD/8LgER+qYWzCcJ6WF0PhZtj11imvNZqN7Kncw/To6UDPG3X0lNVm5akdT+Gj9DmukUaKPoV4bbxTb8bNNrNLhztr1BqGBQwjv7H7rzurJguAjNDOM25gP+dW21ZLWUuZS/bnjIqWCrc1JoFfAzdnSiVz6nMI8gkiUhPplr2oFCpmxc5iY8lGp88lukqjqZFAdeBx8/tcJcgnSJxx60KlsRKDyXBc4BYbZD/nLRqUCMLgIQI3oV+z2WS259Vy9dRYnr50LPo9L0N7M5zzp5OunZ4Sxie3ncHDC0d5YafCQCFJEg/MH8lbxukUBp8J6/4MdZ0HJdvLt2O2mTsCtwRdAvmGfLedGVl5ZCWH6g4xIigGpfTrt2dHq/edFTu7LccrNBRitplJCU5x2b4SdAnkNXSfadxfs5/YoFj0fqduTjE2fCzg2UHc7pzhBj0rlcypt49p6OwMoKvMjp1Ni7mFbeXOj7xwhQZTg1uybWDPuDWZxRm3U3E0JhkZMrLj9yI0Efir/EXgJgiDiAjchH6ttKGVlnYr6cN1YCiFHW/AuCshYmT3LxaEU5gUF8yc0VEsqVuMLCnhf3d0WjK5qXQTvgp/0nT2YCNRn0ibta2j2YUrGUwGXtrzEhMjJnZ6Nm1O3BysspX1xeu7XCe7/pfOci5oTOKQqEukoLGg24A1qybrlOfbHFKCU/BT+nk0cCtvKXdr4KZRafBR+HQbuNlkG7kN7ukoeaypw6YSqA7ku0LPDuM2mAzuC9x8tSLj1oXDdYeB40twJUkiThsnAjdBGERE4Cb0a9kV9k9Y06KCYMNTgAxnP+jdTQmDwv3z0jjaruOb6DvtJZM73zzueVmWWZO3gWZDAg+tPIgsyx2NOpzJPvXUaz+/RoOpgQdPexBp6VJYuvS450eGjGRE4AjWFq7tcp2c+hxUChUJ2gSX7S1Bl0CrpZUqY9Upr6kyVlFprOzyfBvYS/nSw9I9FrgZzUYa2xvdGrhJkkSwX3C3pZJlzWW0mFvcHrj5KH2YOWIm64vXY7FZ3HqvYxlMBpePAnBwnHHz5NnIgSS7PpuYoBgC1AHH/b4I3ARhcBGBm9CvZVf+EripymHvhzB5CejFfDah71Ijg7h4wgjuyc2gLf4c+O5RqPs1IPsscw+NlioCrems2l/BV5nlJOjswZCrz7nJsszK3JWcl3geo0JHweWX2x/HkCSJOfFz2F62vaPtemey67NJ0iWhVrpuBp7j6+4qYO0439ZNxg3s5ZKH6g555AxWhdG9HSUdQvxCum1O4sr5et2ZEzeHBlMDuyt3u/1eDoZ2g1s6SoL9jJtVtjo9K2+oya7L7rQhUZw2jtLmUsxWsxd2JQiCq4nATejXciqbiNb7E7jlaVD7w4x7vb0lYRC5e3YKyBLP+y0DhbqjZDK/poXHv18BwIdXX8/4GD1//t9+LGYNel+9yztLVhorMVqMjA8fb/+N4mL74wRzYudgkS38WPxjp+s0tDVwoOaAywODjoC1iwYl+2v2o5JUx52xOZVxYeMw28wcqjvksj2eSkWze4dvOwT7BXdbKplTn4OERLI+2a17AThz+Jn4Kf08Wi7p7jNugOgs2YkWcwvFTcWdlkfHaeOwylZKmku8sDNBEFxNBG5Cv5Zd0cQcfRkc/ALOWAaBYqi24DoxIRquPj2WN382UXnmo1C4BcvbCyh67VKSNN+RoAggbe9bvDK1AWO7lUc+39/RoMSVHO2643Xx9t9YvNj+OEFGWAZRAVGddpdsMbdw+/e3YzQbuSztMpfuL9QvlCCfoC6/7qyaLFJDUvFT+XW7nicblDgybsMC3ddVEnCqVDKnPoeYoBi3jSU4lkatYVr0NH4o+sEjA5htso1GU6Nbz7gBXWabh6rc+lxk5FNm3EB0lhSEwUIEbkK/ZbbaOFrdzLWt74F/CJxxh7e3JAxCd8xKxl+t5LHicdjOuJOiqnpCLIUU+RmZ2VQPP71C9DfX8Mr4EtYdrERhiXR5xs0REDkyW6fi6C65tWwrTe2/dthrt7Zz9/q7OVh7kH+c9Q/GR4x36f4kSeoyYDWajeyv2d/lGIBjhWvCCfUL7SgddKeKlgokJCI0EW69T7Bv9xm33Hr3NyY51uy42VS1VnkkQG5qb0JGdlvgFuQT1HEf4XiOjpKdZdwcs9xE4CYIg4MI3IR+q6CmhVRbPomG7TDjHvDTentLwiAUGujLzTMT+XZ/JbdWXsA5jY/y+YzHMEsS0y96Fx4shOhJzD70MDdEFbA714cGU4NTM7ucVWAoQKPSEO7ffUZ5btxczDYzG0o2APbZbw9uepBt5dt4fNrjzIqd5bJ9HStRl3jKgPW9g+/RYm5hUdIip9dL1idztOGoq7Z3SuUt5YT5h6FWuO7MX2dC/EIwWoyYrKZOn2+1tFLUVOTRwG1G9AwAdlXucvu9HJkwdzYnAVEq2Zns+myCfII6nVWo89Wh99WLwE0QBgkRuAn9VnZlE+co9iAj2UcACIKb3DQjkZAAH9YdrOT6M+NRBGTjr/JnYsRE8AmAqz5BCk3mTy1PMsJkP+TvyqAj35BPvC7eqdleY8PHEuEfwbqCdciyzBPbnmBd4ToemPIA5yed77I9nShBl0BNa81Jb5zr2up4Z/87nBt7bo8yfUn6JI42HHV7l0B3D992cAzhPlXWLa8hD5ts82jgpvPVERMUw4GaA26/lyNwE2fcPM/RmORU3z9EZ0lBGDxE4Cb0WzkVTZyt/Bl5+AQICPP2doRBLNBXxd8vHsN1Z8TxyMKRbC7dzNRhU/FR+tgv0ITA4s9RBITzivQxACsy97rs/gWNBd2WSTooJAWz42azuXQzT+98mhW5K7h5zM0sHn3ymThXcowXKDAUHPf7yzOXY7KauHPinT1aL0mfhNFidMtMvGNVtFQQGRDp1nuAvVQSTh24ebKj5LEyQjM4UOv+wK3B1AC4MXD75YybmOV2PKvNSm5DbpdNgeK0cR3naAVBGNicCtwkSZovSVK2JElHJEk6aYiWJEkjJUn6SZIkkyRJ97l+m8JQVFRaynjFURQpc729FWEImJcexWMXZFDcUkhpc2lHmVmHoCi49gviVL742WQ2HP6J1nZrn+/bammlvKW84ywKAPfea3+cwpy4ObTb2vnw0IdclnoZv5vwuz7vozsdIwGOKZcsbirm4+yPuSjloo4Zd85ydFY80nDEdZs8gSzL/SbjllOfg7/Kn+igaLfv5VjpYemUt5RT21rr1vsY2n/JuLlpHECgOhAJiSazOON2rKKmIlotrV1+IBCnjaPKWIXRbPTgzgRBcIduAzdJkpTAK8ACYDRwpSRJo0+4rA64E/iHy3coDFkhFZtRYoOUOd7eijCEbC7ZDMD06OknPxmSiPKalcRbrKSod/DVnoI+389RwnRcxm3RIvvjFCZETCA1OJXzk87n4akPO1Vi2VcjgkagUqiOa1Dy0t6XUEkqlo5b2sUrO5ekTwJcW3J6IoPJQJu1ze0z3ODXwK3O1PnZx5z6HFKCU1BIni10GR1q/3Ht7qybu8+4KSQFgT6BIuN2gux6e2OS7jJuYA/yBEEY2Jz5CXIacESW5TxZltuBj4ALjr1AluUqWZZ3AmLCo+ASre1WMozbaVXpYPgEb29HGEK2lG0hUZfI8MDhnV8QlUHisMlU+Nio3Phmn89oOUoPj8u4ZWfbH6egVCj5dNGn/HX6X1EqlH26v7NUChVxQXEdgduB2gOsyl/F4tGLe9WxUeerI9w/3K0ZN08N3wZ7cxLoPOMmyzI59TkeL5MEe+AmIXkkcJOQOro/uoPWRyvOuJ0guy4blaTq+CCkM6KzpCAMHs4EbtHAsZNgS375PUFwmyOVjcxUZFI/bAZ46I2pIMiyTFZ1FlOipnR5XWLMdMpVKs4zfsSeoxV9ume+IR8JqeNTcQBuvdX+6IKnMzfAcSMBXtz9InpfPTdk3NDr9RwNStylvNl+fs4TpZJBPkEoJWWngduB2gM0mBoYGdz9cHJXC1AHkKBL4GDNQbfep8HUYP9v4Mbv11ofrRgHcILsumwS9Am/nsftRExQDCACN0EYDJz5yd9ZDU6vPmKWJOkWSZJ2SZK0q7q6ujdLCENEVc52wqRGlGnifJvgObVttTSZm7o9r5Wotz/f6tNE/tpX+nTP/MZ8hgcOd2pwtbcl6BIobipmY8lGtpVv45axt/Qpw5KkTyLPkOe2AdGezLgpJAV6X/1JYyJkWeaF3S8Q7BvMeYnnuX0fnUkPTWd/7X63dvA0mAxua0ziIDJuJ3N0lOyKRq0hQhMhAjdBGAScCdxKgJhjfj0CKOvNzWRZXi7L8mRZlieHh3c/r0gYulR532GTJcLGLfT2VoQhxJFNitfFd3mdo8PijqAUzqp8j6q63s90KzAUHF8m2Y8l6BKwylb+svUvRAdGc3na5X1aL0mf1NGcxR3KW8pRKVQdZYzuFux38hDuzaWb2VGxg1vH3UqgT6BH9nGi9LB0alprqDJWue0ehnaD2xqTOGh9teKM2zHq2uqoaq3q8nybQ7w2XnSWFIRBwJnAbSeQIklSgiRJPsAVwJfu3ZYw1EVXbyZXlYwySAT4guc4AjdHYHYqcdo4lJKS8pQzCZcMZH/1Yq/uJ8tyj0YBeJsjE1ndWs0dE+7osjzLGY7Oku4ql6xoqSBKE+WxstIQvxDqTb8Gblabled3P09MUAyXpV7mkT10Jj00HYD9tfvddg9DmwGdn8i4eVJ2nf0crDNnJ8UsN0EYHLr9aSbLsgW4A1gDHAI+kWX5gCRJt0mSdBuAJElRkiSVAPcAf5QkqUSSJK07Ny4MYsY6EtsPUxA8zds7EYaYgsYC/FX+3c79UivVxATFUO1rI8tvIhn572A2Gnp8v0pjJa2W1gGTcXNkIkeGjGRhQt+z4Y6GCu5qUFLZUumRMkmHEzNuX+V9xZGGI9w58U7USrXH9nGitJA0lJLSrYO4PZFxC/IJEmfcjuFoOOPoHNqVOG0cBpOBhrYGd29LEAQ3UjlzkSzL3wLfnvB7rx3z7xXYSygFoc+MB9eiQaYldpa3tyIMMQWGAuK0cU5laBJ0CeQZ8mid9hDB3/+W7G+eJ+23j/Xofh0ZvhMzbn/8Y4/W8ZQAdQCPTH2EiZETXZLF0vpoifCPcFvGrbylnEmRk9yydmeCfYM7zri1Wdp4ee/LZIRmMC9unsf20Bl/lT/J+mQO1rqvQUmDqcFtowActD5a2qxttFvb+5ztHQwyqzOJ08Y5dbawo7NkUyF6P/f+OQmC4D6eb0smCN0wHlxNnRxIcMpUb29FGGLyDflOZ78SdYkUNRYx9oyz2aKYRPTBN6GtZ1k3x5mTk87UzZ5tf/RDV4y8wqVt7ZP0SW7JuFltVqqMVR7pKOkQ4hdCY3sjZpuZDw99SKWxknsm3+OROXvdSQ9zX4MSq81KU3uTR5qTAKJckl864NZkMSZsjFPXx2pjAdFZUhAGOhG4Cf2LzUZgyY9stI0lbZj4VFDwnHZrO2UtZU6fN0vUJ2KRLZS1lFA+8R4C5WZqvuvZWbd8Qz4B6gDC/U84y7lvn/3RheomEz8XD/yypyR9EvmGfJd3lqxprcEqWz1eKglQaCjkray3mDliZrejJTwlPTQdg8lAaXOpy9d2BFLuDtwcHUxF4GYvs65prXE6cBsROAKlpOyYGykIwsAkAjehfynfi197PdsVExmm6//t0YXBo6ixCJtsczrj5mhgkt+Qzzmz5rLWNoXAPa9Do/MdEh0dJU/KyNx9t/1xCptza5j/4kYuenULmSUDO3hL1ifTaml1eUDh6FTpjcDtmZ3P0GJp4e6Jp/4z9LT0MHuDEncM4jaY7Jlmt2fcfH/JuInOkmRWZwIwNnysU9erlWqiA6NFxk0QBjgRuAn9S+532JCoiJjWL8qLhKEjv9G5UQAOjsxcfmM+IQE+7E29C5vVguWja8BicmqNgsYCp+8HYLXJvPhdDje+vYWQAB/CAn154LNMzFb3zEHzBEeDElefc/PkDDcHx9iBn8p/4oKkC0gJTvHYvbuTqk9FrVC7pUFJg8n+4YEcqw9DAAAgAElEQVQnzriByLgBZNVkoVaoe1S2LDpLCsLAJwI3oV+Rj6zjIIkMGx7T/cWC4EKOEiJnM26BPoFEaCLIa8gDYMHZM/i9eSmqsl3wzT3QzVkio9lIeUt5t6MHHGqaTVz31naCfvwz+/1u5ptxW/nromQOVzSxfGOeU2v0R+7qLFnZUgl4OOPma8+4+Sp9uX387R67rzPUSjVpwWluybh1lEq6e46bCNw6ZFZnMipkVI+atMRp4yhqKnLrIHZBENxLBG5C/2Gsg5JdfG8ZR1pkkLd3Iwwx+YZ8IjWRaNQap1+TqEskz2APmsaO0KMZdxEvWS6EvR/Azje7fG1RUxHgXIZvR34dC/+5idFFH7JEtQp1eBI+m55izg+LeCghl39+n8PR6man992fBPkEEaFxfWfJ8pZyAtQBBKk9970kXBOOSqHi2tHXejRgdFZ6WDoHaw+6/Dyhp0olHWfchvpIAIvNwqG6Q4wJd+58m0OcNo5WSyuVxko37UwQBHcTgZvQfxz+GgmZH63jSBWBm+BhPS1bBHvglm/I7/gE+4kLM/hCdx2bpEnIqx+Egs2nfO0pRwGcoKyhlWve2s485U4eUr4PoxYh3bYFrvsKfAK5tfxR3lX9jZc//gabbWB+kp6sT3Z9qeQvw7c9WXKt89Xx5QVfcseEOzx2z55ID02n2dzs8nI5R6mkOOPmGUcbjtJqaXW6MYmD4/ubo5utIAgDjwjchP7BaoHNL1ATmMY+OYnUyEBv70gYQmRZpsBQ4HTZokOiLhGjxdjxCXagr4p/XT2Ju8zLqFAMQ/7kWmgo7vS1BYYCJCRig2JPfvJvf7M/gLc355Mh5/KY5UWk6Elw0XJQKCBhJty6CRY8yyR1IU9XL+W71St79oX3E+7oLFnRUuGVrFeMNsYlM+7cwV0NSgwmAwpJ0ZERcxe1Qo2/yn/Il0pm1tgbk/Q0cEvUJQJ0lHcLgjDw9M+fLsLQk/UJ1OXxdfC1hAb6Exro6+0dCUNIbVstTeamHmfcHNmyY98IpQ/Xcfd5k7i65S7MJhN8dJW9DPgYNc0m1uZmEakZhp+qk+6pZ54JZ56JwWhm046dvOv3HIrASLjyI/A5ppRTqYKpt6C+aw916kjGbL+fisqKHn0NvdXUZubP/9vP/Bc3sr+0Z/PrTpSsT6bN2kZpk+s6S5a3lPfLckVvStQl4qf0c3mDkgZTA1ofrUcC1iCfoCFfKrm/Zj96Xz0xQT07Cx7uH06gOrCjvFsQhIFHBG6C91ktsOEZiBrDSuM4RkaJMknBszrKFnuacdPbP8F2dKR0WHx6HKmjJ3J721Lkyv3wQgas/SMttaW8sC6Hs55ZT3bdUZqagjtvFLB1K2zdyqebM3mVv6NRyXD1ZxAYfvK1gBQYDhe9STj1FL53G7LNvV0m1x+uYt4LG3l/WyGVjW1c+tpWvvy5rNfrubpBSbWxmrq2Oo8O3x4IVAoVI0NGujzj1mhqdHuZpIPWRysybtWZZIRl9LgMWJIkEnQJHd/vBEEYeETgJnhf1idQn4/trAfJqWoW59sEj3Oc+XB2+LZDqF8oWh/tSQGHJEk8felYDgWdybU+z9OWPA/b1ldQvTSO0A0Pc0GCGV//WuoNer7Y10mW6eGHsd2zjNlbryFWUYPyyv9CeNdtv6PSp7E3aSlTW9az86vXe/R1dMlqgfoCyPuRlp/e4tuXf8/O9x/hOr5i48zDbJyVx+0hu/nDf3/i6dWHsfbinF2S7peRAAbXnHN7ae9LqBQqFiQscMl6g0lGWAaH6w5jsVlctmaDqUEEbh7SYm7haMNRxoY5N7/tRCJwE4SBTeXtDQhD3DHZtuLws2kzbyAtSpxvEzwr35CPn9KPyIDIHr1OkiRGhY7qtPRM56/mpasmcNlrbYzNvIxhtuk8GryWa9q+p6psA1+NiGKSr4q/fXOI2aMiCfJT219oaoLaoyiaylHJcWTPeZuM+GlO7WfClY9x6JkfGbXnMbKSpjEmo+dv7kwWK9uO1jJs/2uMyPsEP2MZCtkKQACwEFioBkzAdvtr7gSuCRrGPRsXc1P5HP555QS0jq/HCYE+gUQFRLkk43ao9hBfHPmCa0dfS6y2k/ODQ9zo0NF8cOgD8gx5PZoB1hVDu4FQv1CXrNUdra+W8mbnh9wPNgdqDiAjkxGW0avXJ+oS+fLolzS1N7n9TKIgCK4nMm6Cd2V+DPX5VE+6h4c+3w/YzwgJgicVGAqI08b16ozOmLAx5Nbn0mZpO+m5ibHBPHZBOhPj9Dx54/nMuv8jFHftIz/jAgDubvyE99t/z+b/Pm0P2I58B6+egdxUTrUUyr1hr5E+bZHTe1Gp1Qy74T0kScL62c0U1/QsM2Gzydz13laaPriG1Kzn2NOo5VXzb3jAfDNXtj/CzSHvkH1zHjxSCQ+VwAP5cF8uXPslITot//Z5hkvy/swNL31NYW1Lj+6dpE/qc2dJWZZ5Zucz6H313DLulj6tNVh1NChx4Tk3g8ng9uHbDlof7ZA+45ZVkwX0vDGJQ0eDEnHOTRAGJJFxE7zHakHe+Cy1QSOZ+aU/SoWBv100hoxoEbgJnlXQWEB6aHqvXjsmbAwW2T5XaULEhJOev3pqHFdPjfv1N3QjyE89B3ZsJ2HWXzCtf4cFhc9ie/YlFJY2CEulUT+Ko3Vw3dk9P8eiH55M5eynGf/d7/j3Gw9y8e//6XT26z/rtnJHwR2kKwupOO0hQscvZZ5SgVIhoVYqiNb7o1A49nNMU5XACLhtE2z5Jws3PMvMlmW89cZ13HL3YwT4OTcgOFmXzK6KXVhtVpQKZY++Zocfin5gV+UuHpn6SMewZuF48dp4AtQBHKg9wEUpF7lkTYPJIEolPSSrJovYoFj0fr0LlB3ncvMa8hgXPs6VWxMEwQNExk3wmorN7yLV5/Ng7XmcnhjK2t/P5KqporRJ8Kx2azulzaU97ijp4PjkO6s6y+nXFDQWEKAOIOy0pfgv28RV/I31vucgz3oE+dZNFLWo8FMrmZfeu66IkdOvpTp+Ede0/Zf/LH8Gi8Xa7WsO7/yOeVuvJElVBVd+RNTCBxk1XEdKZBCJ4YHEhGiOCdo6ofKFsx5AcftPyJEZ/L7tVQ69cgWypd2pPSfpkzBZTZQ0lzj7ZR6n3drOc7ufI0mXxKWpl/ZqjaFAISkYHTraZRk3s81Ms7m5Y8aau2l9tDSbm7Hauv87PRhlVWf1ePD2saIDo1Er1OKcmyAMUCJwE7zi3U25mH54moMk8JtLb+Tt66cwXO/v7W0JQ1BRYxE22Ua8Nr5Xrw/XhBMVENVRwuQMx8w4SZIIDfJj/rzzWFJ7DatCFvNTUTN/mH4jZX/5G8quAqXu9nX5yxj06dxW9wylL5yFXLq38wubqzD+8ByJ31yOSeGP9cZ1SGnze31fwpLR3baGrfF3MLnpe0qWXwbmk8tIT9TXzpL/PfxfipuKuX/K/agUopikK+mh6WTXZ2O2mvu8lmMYtqdKJR3nsprNzR65X39S0VJBVWtVr8skwd5ZNE4bJ0olBWGAEoGb4HHb8mo5svoV4qRKRlz0OBdOHNHjcjBBcJXedpQ81piwMT0K3PIb84/L8F11Wiyjhml58uuDvPT9EaqSRnLOFfN6vR8A/PWE3rWRrxMeQdNcCG/MQv7fHdBcDW2NsO+/8P7FyM+NRLPxcbbZ0mm8eg2BI3rX9OA4ksTp1z7JeyG/I6ZqPU3vXALtXZ95cwRuvRkOXNdWx+s/v8706OlMi3aukctQlh6WjtlmJqchp89rGdrtM/x0Ph4qlfwls+cIGIeS/TX2c+B9CdxAdJYUhIFMBG6CRzW1mfn8ozd5VP0u1viZaMc633hBENzB8Qamtxk3gLFhYyltLqW2tbbba41mIxUtFcfdT6VU8PgF6ZQZ2vgpr5Y/+ZXht2F9r/fTQaFgweL7eTLhfd6wLMS69z+0vzAO+R8p8MVtUJvL3tjrmGN6hoIF7zE6Ob7bJZ2/tcSiJX/mCdXv0JRtxfLuhdDacMrrA9QBDAsY1quM26v7XsVoMXL/5Pv7suUhw/HGf2/lKbKwPWAw2QM3TzYnAYbkObfMmkzUCjUjQ0b2aZ1EXSIlzSWYrCYX7UwQBE8RgZvgUR9+9CGPm57FFJaB8ooPQWTaBC8raCwgQhOBRq3p9RqO1tyOT8S7UthYCJyc4ZsSH8LFE6PR+qk478u34ckne72fYykVEv9YPJPQi55hmf4VvmqfwEeWs3h31Bt8M2sNv82dQ0rGZBafHtf9Yj0UHODDb669l7std0LpHuR3F0FLzSmv701nySP1R/g051MuS7uso/GC0LXowGjitHFsKdvS57UcgZunmpM4SiUdmb6hJKs6i5EhI/FROtfw51QSdYnYZFvH9yJBEAYOEbgJHrNt01quyf8DTZoRBNz4P/ATXd8E78s35PepTBLss7GUkpLMmsxur3WUZnbWDOWpi8fy/b1no+rD2bbOqJUKLpk0gtfuuoK4Je+zOfVBHv85kGX/3Uu03p+nLhnrtnLlCbHBTFpwPTe134O1Khv55SmwfTl0cr4qWZ9MviHf6cYTsizz7K5nCVAHcPu421299UFtevR0dlbs7HSMRU80mOxZVE92lQQG5UiAow1HufOHOzlSf3LW2WqzcqD2QK/ntx2ro7OkOOcmCAOOCNwEj6jP38fI72+gSalDe8s3oAnx9pYEAVmWKTAU9KlMEkCj1pCsT3Yq41ZgKEBCIk57cobLR6UgPMi3T3vpiiRJTI4P4ZWrJrLpgVncPy+Nt6+f3KNh2b1x3ZnxBGQs4Detj7G1ZRisup+Kpybw1Sdv892BCtrM9kAtSZ9Eu62d4qZip9Z9YfcLbC3byrLxy3rdHn2omjZ8GiariV2Vu/q0jqczboO1VLLV0sq9P97L+uL13LjmRrLrso97/qjhKK2W1j6fbwN7WbiERH6DOOcmCAONCNwEt5Nr85A+uBiTrKb1ipX4BEd7e0uCAEBtWy1N5qY+Z9zAXi6ZVZOFTbZ1eV2+IZ/hgcPxVbovQHPGcL0/y2YlkxwR5PZ7SZLEi5eP597FF7Nrxr95JepJTBYbiw7+Hv+PLuYPL39AbbOJZH0ygFPlkm9lvcU7B97hirQruGrkVe7+EgadKVFT8FX6srl0c5/WMZgMKCUlgepAF+2sa4O1OcnTO57mqOEofzr9T6iVapasXcLB2oMdzzvGjYwNH9vne/mp/BgeONwjGbeSphIe2fwI81fMp6b11GXSgiA4RwRugns1ltHy1iJki4mNU5eTmOqCjnWC4CKOxiQJ2r4HbmPDx9LU3tTtuZGCxoJez4wbyNRKBXNGR3LXnFSW3fY74v74M6a5TzPZv5R/Gu4k88WLCKxvRaVQ8Xrm6112vfs051Ne3PMiCxMW8tDUh0RX2l7wU/kxOWqySwI3na/OY38Gfko/VArVoCqVXJ2/mhW5K1iSsYTL0i7j3/P+jUal4aa1N3Vk8bNqstD56ogNcs2sU3d3lqwyVvHktidZ9MUiVuevprS5lLUFa912P0EYKkTgJrhPSy1tby9CaqnhmbC/cvH8Od7ekSAcp6vzZj3lKGHqqlyytrWW3PpcRoWM6nqx11+3PwYzpRrfM2/D955MSsf9jqmWXcR8OJfHFImUNZVw2VeX8eGhD0/KYK4uWM0TPz3BjOgZPDn9SRSS+DHWWzOiZ1DYWEhxo3OlqZ0xtBs6yhc9QZIktD7aQVMqWdxUzGM/PcbY8LEsm7AMgBhtDO/Mfwetj5ab197Mvqp9ZNVkkRGW4bIAOVGXSEFjgcsHmde31fPcrudYuHIhK3JWcEnKJXx78bck65NZWygCN0HoK/ETT3CLRkMdpS8vgPpC7lc/zO1XX9anYcKC4A4FhgL8lH5EBUT1ea1EXSIalYbM6lM3KPnq6FdYZAuLErsZg5GWZn8MBX46oi96kpJrt/GRYiHzcjfwWf5RJisCeGrHU9yy9hYqWioA2FK6hYc2PcSEiAk8d/ZzqBXuPZs32E2Png7A5rLeZ90aTA0eGwXgMFgCN7PVzB82/gEJiWdmPnPc3+fowGj+Pf/fhPiFcOu6WznScMQl59scEnWJmKwmylrKXLbmwdqDLFi5gHcPvMu8+Hl8edGX/PH0PxIZEMncuLnsqdwjyiUFoY9E4Ca4lCzLfLs3jyMvnkeE8Qgrkv/G0/fdTkxI71utC4K75BvyidPGuSRro1QoSQ9LP+UgblmWWXlkJePCx3Xftv6rr+yPISQ1MZHpy5Zztd+rbGyfxstH9vNoTS2Z5du58LOFPLzqUX73w93EByXy0rkv4a/y9/aWB7w4bRwxQTF9KpdsNDV6rDGJg9ZHOyhKJV/a+xJZNVn85cy/EB148tnvqIAo3p73NhGaCGyyzbWB2y/fg1xZLvlx9scArDx/JX+d/ldigmI6npsTNwcZme8Kv3PZ/QRhKBKBm+AyxXVGlry9Fb+VNzBePkT5OS9w9eJbCHJzxzpB6C1XnzcbEzaG7PrsTgfb/lz9M/mGfC5Oubj7hZ57zv4YYuJCA3j19vN5O/huJhhfYXfdFfy5yJe0tia+qlqJvs2E/vAZVNaL7L2rTBs+jR3lO3o9jLnB1ODxwC3IN2jANyfZXLqZdw68w2WplzE3fu4pr4sMiOSd+e/w4GkPcubwM112/0TdLyMBGlzToMRsNbOucB3nxJxDcnDySc8nByeTqEsU5ZKC0EcicBNcorXdypWvb+WSor9yjnIf8sLniD3rOm9vSxBOqd3aTmlzqUs6SjqMCRuDxWbhcN3hk55bmbsSf5U/8+Lnuex+g1GE1o/Plp7JM9fMZNZVDxB0xSrumvoxDwdM5d2GNt5pe4Lc/7uM3T93PzNP6N6METNos7axu2J3r17vaE7iSQO9VLK2tZZHNj9Csj6Z+6fc3+31Yf5hXD3qalQKlcv2oPPVEeIX4rLOklvLttLU3sT8hPmnvGZu/Fx2V+4W5ZKC0AcicBNc4o1NeVzZ8i7nSVvg3D+jPG2Jt7ckCF0qaizCJtv6PMPtWI5SJkfrbocWcwurC1YzP34+AeoAl91vsAr0VTE/Yxhz06M4KzWcieMncuWlbxJz114Mp93DOewifeU5ZP/nATA1e3u7A9qUqCn4KHzYVLqpx681W80YLUZxxq2HPjz0IfVt9Twz8xn8VH5e20eiLtFlgduqglXofHWcMeyMU14zN24uNtnG94Xfu+SegjAUicBN6LPKxjYqN7zJMtWXMOl6mH6Pt7ckCN1yZUdJh8iASCI0ESedc1tTsIZWS6tzZZLCqfkEoFv4KO237WSn/3TScl6n5bmxyD+9Cu0t3t7dgOSv8mdy1GS2lG3p8WsN7b8M3/bxzhk3WZY9el9XaLW08knOJ8yKmUVKcIpX9+IYCdDX/46tllbWF61nduxs1MpTH41I1icTr40X5ZKC0AcicBP67IsV/+Ev0hu0xp4FC/8BYqaSMAA4AjdXzHA71piwMScFbitzV5KgS2Bc+DiX3muo0kbFc9p9K/hH7KvsbwtHWvMQ5ucyMK9/Glrrvb29AWd69HTyDfmUNpf26HWOxhYh/iHu2NYpaX20WGUrRovRo/d1ha/zvsZgMrB49GJvb4VEXSKN7Y3UttX2aZ1NJZswWowsSFjQ5XWSJDE3fi67KndR29q3ewrCUCUCN6FPcvfv5MqCR6jXxON/1fvQxadtgtCf5BvyidBEoFG7tuPpmLAxFDcV09DWANgP//9c/TMXJ1/s/Aym99+3P4RT8lUpuef6q9hw5rtc2v4oG41xqDf8DeMzo9i+/A5+2pdFm9m1M6oGq2nR0wDYXNKz7pLvHngXva+eacOnuWNbp6T1tc+NG2gNSmRZ5oODHzAqZBSTIid5ezsdDUr62llydcFqQv1CmRw5udtrO8oli0S5pCD0hgjchF6TmyrRfX41JskHv+tXgJ9ny2UEoS8KDAUuz7YBjA0fC9CRdVuZuxKVpGJRUjez244VE2N/CF1SKCQemD+SN/94B/KVH/Nm+nvs9pnClNIPmPL5TH76+0LWr/oEi0UEcF1J0CYQHRjdo7EA2XXZbCjZwNWjrnb5hx/dCfIJAhhw59y2lG0hz5DH4tGLXTZIuy8cIwH60lmyub2ZjSUbmRc/D6VC2e31qcGpolxSEPpABG5C77Q1YnjntwRZ6tlx+qtoI13/BlgQ3KWmtYaDtQfJCMtw+dqjQ0cjIZFVk4XZauarvK84K+YsQv1DnV/k44/tD8Epeo0Ps0dHctNvL2DGQ1/RtnQnpSNvYJJ8kFnbb6b8rxkcWvl35BZRntUZSZKYHj2d7RXbabe2O/Wat/a/hUal4cqRV7p5dyfT+vyScRtggdv7B98n3D+c+fGn7rzoSZGaSDQqTZ8alKwvXo/Jauq2TNJBkiTmxM1hZ8VO6trqen1fQRiqROAm9FxLLbZ3zyewbj9/19zHvDnOfcMWhP7iy6NfYpEtXJB8gcvXDlAHkKRPIqsmiw0lG6hrq+t5U5L/+z/7Q+gVTVQKcVc+T9DDOfw85RkMCi2jMp/C9mwyda8txLLzbWiu9vY2+5Xp0dNptbSyp2pPt9cWNxazpmANl6dd7vFRAAAjgkYAcLD2oMfv3VtH6o+wtWwrV4y8ossGHp4kSRIJuoQ+BW6rC1YzLGBYR6WBM+bGi3JJQegtEbgJPdNYBv9eiK3yILe0/55ZF96ASin+GgkDhyzLfJ77ORMjJrp0htuxxoaPZX/NflbkriDCP8Klg3MF50lqf8addysjH/6JNTNX8IHyQgxlR1B983ts/0il7MXZlK95HltZJths3t6uV50WdRpqhdqpc27vHHgHlaTyWoON6MBoRoeOZk3BGq/cvzc+OPQBvkpffpv6W29v5TiOzpK9YTAZ2Fq6lfnx81FIzr8PSAtOIzYolrUFolxSEHpKvOMWnFeXD2/Px9ZQzK22hzAnzeXs1HBv70oQemRP1R4KGgu4KOUit90jIyyDBlMDm0s3c0HyBS4dnCv0nEqpYN45s7n8wTc5fOmPvJT2bz7wuRRjXSnDfnoMxfIZtDwRQ/bzC9n1n8fY99P3lNU2YrMNvHbzvaVRa5gUOanbc27Vxmq+OPIFFyRfQLjGe9//58fPJ6smi+KmYq/twVn1bfV8nfc1i5IWEewX7O3tHCdRl0ilsZIWc8/HaXxX+B0W2dLl0O3OOLpL7qzYSX2b6AIrCD0hAjfBOZUHsbw5j5amei4xPsQW60j+eN7ofnHAWhB6YmXuSgLUAcyNm+u2e4wN+7Vs6KJk9wWIQs/4qZUsGDuc3115Edc+/AYB9+7h23PX8eHwP7LJZxpqQz6Tc55n/JqL0f0rmW2Pz2TN/93H4e2rkc2t3t6+202Pns5Rw1HKmstOec17B9/DKlu5IeMGD+7sZPPi5wEMiKzbJ9mfYLKauGbUNd7eykn60llyVcEq4rRxjAoZ1ePXzo2bi1W2inJJQegh8TGw0DWLiaqNbxGw+e80WxXcaP0TU6ZO45WZiQzX+3t7d4LQI03tTawtWMtvkn7j1k54Sfok/FX+ZIRlEKMV3SH7q2E6f4bNOA1mnAbYy2iryoupP/QjFG4hvnIHwyvfgFVvYF6lolETi//w0WiGj4KwNGyhybSHpGLCB5VCIsB3YP9IPWvEWTy36znu23Afz5/9PFEBUcc9bzAZ+CT7E+bHzycmyLt/r4cHDmdc+DhW56/mpjE3eXUvXTFbzXyU/RHThk8jSZ/k7e2cJEFvLxfPM+T1qFlTTWsNOyt2cvOYm3v1Ae7IkJHEBMWwrnAdl6Ze2uPXC8JQNbB/ygjuYzFRvn45ftv/SYSlmr1yKj+N+zvvzJlORJCft3cnCL2yKn8VbdY2Lkm5xK33USlUvDjrRaIDo3u3wGefuXZDglMkSSJieCwRw68FrgWguaGafZtXUXt4ExrDEZJydhGX+w1KSUYBqGQFhfJwDslxNAePYljaFMafMYfQkB50Ee0n4nXxvHD2Czy8+WEu//pynjvrOSZH/Tqb67+H/4vRYmTJmCVe3OWvFiQs4KkdT5FnyOvIHPU3qwtWU9Naw5PTnvT2VjoVExSDSlL1eCTAusJ12GSb090kT+ToLvnugXepaa0hzD+sV+sIwlAjybJ3avgnT54s79q1yyv3FrpgaqLkx7fQ7HiJEGsNe+U0ckcvY/Z5VxAS6Ovt3QlCn1zx9RW029pZsWiFKPMVeqyk3siXP5dhaGwmwlJGVHsBka1HiGjJRd94GG17FQAmWcVh/wm0J88nZcZl6CNjvbzznjnacJS7199NSVMJ9025j6tGXkWrpZV5K+YxLnwcL5/7sre3CECVsYrZn85m6bilLB2/1NvbOYksy1z+9eW0W9v5/ILP++33nAu+uIA4bRz/OudfTr/mulXX0WRuYuX5K3t93wJDAed/cT43ZNzA7yf9vtfrCMJAJ0nSblmWu59gj8i4DUkVhjYKa1vwVSvxUSrwM9ejL1qHdPhrAko3MwIzexjJhozHOGfBZUwI8PH2lgckq83K3qq9rCtch4/Sh2tGXUNkQKS3tzVkZddlc6D2AA+e9mC/fQPV4d//tv/z+uu9uQvhBCOCNdx+dvIvvxp/0vNySy1FB7dRtedrhld8T/T+J2D/ExzxGUlrwhwSpvyGwITJoOzfP3qT9En857z/8PDmh3lqx1McqDlAoj6RBlNDvypLjNBEMClyEqsKVnHbuNv63f+v3z/4PofqDvHoGY/2u70dK0GXwNGGo05fX9FSwZ6qPdw54c4+3TdeF8/8+Pl8dPgjbsy40SujJQRhoOnfPz0El9uRX8cN7+zA1t7ChcotXKjcQpqUjVKSKZHD+FYxF834S5gz73wm+vWPWTMDiU228XP1z6wpWMPagrVUt1bjp/TDYrPwn0P/4ZLUS1iSsUQEcF6wMnclaoWa8xLO8/ZWuicCtwFJClukl94AABhsSURBVAglbsp5xE05D9lmI/fALsq3f0ZE6XeMyX4Jsl/CKAVQFzEVKWkWwRlz0AwbCf3wTX2QTxD/nPVPlmcu59V9ryIjMzlyMuMjTg5YvWlBwgKe2PYEOfU5pIWkeXs7gP1Du2d3PcuHhz7knJhzuCDJ9fMiXSlRl8iPxT9itpq7nTFnk238dftfUUrKHneT7MxNY29iVcEqPjz0IbePv73P6wnCYCcCtyFk69Eanvj3lzzu9wMX+P6IytxEkzaF3GG3URRxLq0ho7g4fRj+Pkpvb3XAaWxv5P2D7/N57udUGivxUfgwY8QM5sfPZ+aImdS11fFm1pt8mv0pn+V8xqWpl4oAzoNMVhNf533N7NjZ6P303t6OMARICgUpY04jZcxpyLLM/tw8crZ9g6pgAxMr9hFd+QNs/RMVhJGtmUR1+OkEjZ7NjAmj0fj0jx/NCknBbeNuY3ToaJ7b9Rx3TuxbhsUdZsfN5m/b/8aagjX9InAzmo38YdMf+LH4RxaPXsy9k+5FqejfP1NTglOwyla+PPoll6R2ff739czX+bH4Rx487UGXNKhJDU7lnJhz+ODQB1w7+loCfQL7vKYgDGbijNtQYLOx/8dPaPjxZaYrspAVaqTRF8BpN0PM1H75ae9A0WJu4YODH/DugXdpMjcxc8RMFiQsYFbMLALUASddX9pcyhuZb/C/I/9DkiQuT7uc28bdJkpE3OzbvG/5w6Y/sHzOcs4Yfoa3t9O9s8+2//PHH725C8ENbDaZo1VNlBUcQj76I/qKLSQ17yZIbgYgW46lLGQq+ow5jD5jPr4a5783WKw2VMqhN+Xn1nW3UtRYxLcXf+vVksRqYzXLvl9Gdn02D572IFeOvNJre+mJdms7y75fxo6KHTw982nmx3eeSdtQvIE7friD85PO58lpT7rsv/WB2gNc8fUV3DXxrn5ViisIntKTM24icBvM2o3w838wbnwJTVMB1VIYAdNuQnP6EgiM8PbuBrRWSysfH/6Yt/a/RYOpgbNjzuaO8Xc4/YlvaXMpyzOX88WRL9D56Lhr4l1cmHxhv/9kdqC6ae1NlDSV8O3F36KQBsAbWxG4DS02K9ayTEp2f0t77g/ENv+ML2bMKCnyT6cuYio+uih0Oj3BwcFog3S04kt+ZR3FFTWUVddQU1uLpa2JgOBIguMnkDZ2ChMSovBRDYC/7330ee7n/Hnrn/novI9ID0v3yh5y6nNY9v0yDCYDz858lrNizvLKPnrLaDay9LulZFZn8sKsFzg75uzjni8wFHDlN1cSExTDewvew0/l2u7SS79byoGaA6y+ZLVbR7UIQn8kArehrrEcdr0FO9+C1joybYms1l7KLbf9Hn2g+IbYWyariczqTLaXb2dF7gpqWmuYNnway8YvY0z4mF6teaj2EE/teIo9VXsYHTqah057qN+dIRnoSppKWLByAcvGL+O2cbd5ezvOEYHbkGZua+HA9nXUZq4lqnYbo+Q8FFLPflZbZAWFDKM2IBmGTyBq3GxiRp+O1M0ZpoHIYDJw9idnc/XIq7lvyn0eu69NtrG3ai9rC9byv6P/Q6PS8PK5LzM6dLTH9uBKze3N3Lz2ZrLrs3n53Jc5c/iZgL2y5OpvrqaurY6PfvMRwwOHu/ze+6r2sXjVYu6ffD/Xpl/r8vUFoT8TgdsgZrPJFNYZ8VEp0KiVaJQWfKoPIJXuxlK0A2vxTnybipCR2ON/Os82zqVt2Gm8u2QqOv/B9wPbnYxmI/tr9rOzcie7KnaRWZ1Ju60dhaRgStQUbh93OxMjJ/b5PrIssyp/Fc/teo6q1ioWJS7ilrG3EKuN9Vh2SJZlWi2tGC1GWswtHQ+j2YjRYsRH6UNUQBRRmihC/EL6dYe0E7209yXeyHyDtZeuPWmgcL9lNNr/qREftAhgbGmivKqayppaqmtrqW9oQKcykxAVQuLwCLTaYPANBLUGmisxlvxMefYu2kv3o2/KZphsH1PQgj+FgeOwxU5j+NhZBMdmIGmCvfzVucYd399Bdn02ay5Z49bvm8c2oFpXsI6q1ip8lb7MHDGTB6Y8MHC+x5yCwWTgxjU3UtRYxGtzXmNixETu+fEefij+geVzljN12FS33fumNTeRZ8hj1SWr8FWK8UPC0CECt0GozWzlq525bNy0nuDGQ6RLhaQrCkiVivGRrABUyMHssyWzz5bEesVUfCNTGTdCzwPz0wgSHSJPyWwzU9xYTE5DDrn1ueTW53Kk4QglTSXIyCgkBaNCRjE5cjJToqYwIXICWh+ty/dhNBtZnrmc9w6+h9lmxl/lT0pwCqnBqaQGp5KiTyFCE4GMjCzLdPxPlmm3ttsDLYsRo/mY4MvS0vFrR1B27PNGs7HjGhnnvhf4Kn2J1EQSFRBFTFAMyfpkkvRJpASnEOoX2m+Cuuy6bN7a/xZrC9YyLXoar5z7ire3JAheUVZSQP6utdjyNxFt2E0ipR3P1aOlTBVDnX8szYEJKMNTCU/IICk1HW3AwPng4Ou8r3lo00O8t+A9JkRMcNm6je2NHKw9yIGaAxyoPcC+qn1Ut1Z3NKCaFz+PmSNmdnqmeaCqba3lhjU3UGWsYl78PFbmruS+yfdxXfp1br3vjvIdLFm7hEemPsIVI69w670EoT8RgdtgYTXTdGQrhzZ/iX/xBkbLR1D+Ui7T5hNCTWAalQGpFPuP4qjPKNTB0aRFBTEyKogRwRqUiv7xBtqbZFnGaDFS11pHbVstlcZKipuKKWkqsT+aSyhvKccm2wBQSkpitbGk6FNIDk4mPTSdCRETCPIJ8tieS5tL2Va2jZz6HHIbcsmpz8FgMvR6PV+lLwHqADQqDQHqAPu/q4/5d5Xm11+rjn/O8XyrtZWKloqTHgWNBTSYGjrupfPVkaxP7ngk6ZNI0ad4rJOjLMvsqtzFW/vfYkvpFjQqDb9N/S1Lxiwh2G8AZRZefdX+z9tFe2zBtaw2mewjuZQf2IJUdwRNUz7BxkIizcXo/7+9uw2Oo74POP797Z7uTqcnS7JkW8g1oiiADS4tBNJmEiAlBDxtqUtDYBoI7QuGSemb5EXD0BeZ5EXLdJpOOpOGUibTNJNJ8qKFmqfyNM3DdMLUdmzsmPBgGQfJsvVg6SSdTve4v77YvfMh7k4noZPO0u/j2dmH/3/3Vv7df3f/t//9r144zmTV5ayznXhsF17nAE7XAM29l9HRN0jXJZcTijRWpW4+O89NP7qJuwbv4pEbH1nRNlSVd2fe5eC5gxweP8yJyRO8N/deMb2/tZ+rt17NzTtv5qb+mzZ0D4hj82M88N8PMJIY4Y5L7+CxTz5W9x/lVJX7X7ifseQYz+1/bslXExizUVjF7WKkSm56mPOnjjD366O4Zw+zY+ogUS9JXoVT0auIfuRT9O/5ONJ3LbTt2DS9Qea9PNPpaWbSM8TTceLpeHF6LjNHKpcqNvMrTM9l5phKTTGVmiKdT39gm13RLvrb+ulv7Wdn2052te9isHOQgY6BhmuioaqMJ8d5e/rtYiVJRCj+EyHshD9Y4WqKEQvFCDn161pcVTmfOs9QfIiT8ZP+MH2SofgQc9m5Yr7uaDeXd17Ozrad9MZ66W3upSfWQ2+sl57mHtrD7YScUNULA1Ulpzmy+SzJXJLZzCxzmTkSmQRzmTmm09M8O/QsxyaP0RXt4vNXfZ67r7j74uyx055xM+thYZrp997g3KnjJEffxJk6SUfyNDu8MZol876sCWIknRZSTgspt4VMqJVsqI1cUxv5cBsaaUcj7RDrxmvvo2XrpVy6a4DOtua67f6Xfvwljowf4ZU/faWmjp489TgVP8WhsUMcPHeQQ2OHmEpNAdDb3Mvenr3s2bqH3d272d21e9O9SmQ0McozQ89w3+771qzDkJ+N/IwvvvpFvvZ7X2P/4P41+Uxj1tuqV9xE5Hbgm4ALPKmqf7coXYL0fUASeEBVf1Ftm5u54rYwP8foO0eYPX0Uzh2nZeYtdqSGit1BA5z2tnHI/S0S/Z/k47fuZ3BX/zrucf0s5BaIp+JMp6eJp+KML4xzNnGWM4kznJ33x2PzY+Q0V3b9kIRoDjUTDUVpDjUXp1vDrXRHu+mKdr1v6I310t/Wv6GatTSiQmWzWJmL+5W5M4kzxQujckJOiCanibAbpslpwlOPbD5LxsuQyWeWbM7Z39rPA3se4M7L71z1Xs/WlFXcTANJpLKMnX2PmdGTpMaG8KZP4yXO42TnCGfniHoJovl5Yt48MeZp1WSxCX+prLpMSBez4V5ysV7ctl60xR+c1q2EI1GiTU2IG8LDIacOeRxyKngSIhYN0xoN07JlK9EtO2DRHZmXTr/El3/yZfpa+i5UuLp3s6d7Dx2RDqZSUxyfOM6xyWMcmzjGickTxR+YtsW2ccP2G7h++/V8dNtH6W/rb5hm35uJqnLPc/cwm57lGzd/g6u6r1rvXTKm7la14iYiLvA28GlgBDgI3Kuqb5Tk2Qf8FX7F7Ubgm6pa9QnWTVFxU4WZYeaHX+fMmwdJnzlGx8zbXOKNFps8zmuE0+6lTLQMku6+kqa+vXRddi0DfX10xBqzmUA6n2YmPcNMeobZzCwz6RkS2QQL2QVS+RTJXJKF3ELx7ldhXJguPGsVT8dZyC18YPuC0NPcw47WHfS19tHX0kdvrJfOaCcdkQ62RLbQGfGnm0PNdnK9yGTzWSYXJhlfGGciOcFYcoz57DxZL0s2nyXrZcnkM2S9bPFuYtgNhmA6ForRFm6jNdxKe7jdn25qpSfWc3F0978Uq7iZi5h6Hpl0ktTcNKmZcXLTwyTG3yU58R4aHyacPEcsM0UXcbbI/Io+w1MhLm3E3S6S4R5ysV5o6+WnsQRvubMMZSc5k54o5u+MdDKdngb8JvGDnYPs3bqXa3qu4bpt19HfahW1RvHz0Z/z8KsPk/EyXNF5BfsH97NvYN/F1dzdmGVY7Yrb7wJfVdXPBPOPAKjq35bk+Rfgx6r6g2D+LeBmVT1babsXbcVNFXJpyCbJpxJkMwlyC3OkpobJTA2Tmx5B587QlBglNjdEOO+flBQY1l7GW36TbPcgsu1K2nddTU//AK4reOqR0xye55HXPHnN+8u8HJ6WLPOCfOqR90rylVtWsm7Oy5HxMv7di3ymeAejcLFcmC/Nk/UuLC/kWcgtlG16uFjYCdPc1EzUvXAnrPSOWEtTC52RTrZEt9AV7fIrY9FOuqPdbG/ZTtgN1zmQxjQwq7iZDU5VmUxkSCTnycTPkZmdIJNJs5DOgpfDFY8QHq4orng4Xp50JsNCOkM+OYU7P0Z4YYLwwgSx9AQd+Sm2EickXvEzZhzhV+EwRyKtnAy30OvF2Ol1sosOwqFmJBRBQhHUjaBu2B+HIhDMO24IcVwcx0WcEK7rII6L67iIG8y7Lo644LiIOOC4IH4+cRwQfyyF5SKIOAggjj8NQfN3ERw/wV8HAccJ1gvWLX6Og+CAI8W8hTRxLuRHBBGCZvX4nysSjGnYyupMeobn332ep08+zRvn3yDkhLhl5y3ctus2Yk0xCteuhVYYOS9XfIRiJuP/sBxPxUlkE8VrocL1kKceihJ2w0TdKBE3QjTkjyNuBFdcHHFwnWAsFcZBuoNT/H90xCk+wlAYL5UuSPEHx9L85bZDEMtKn1MtvdbPWZxf/I0W89fyOSv5+xd/TiF9M1hOxa2Wh18uAYZL5kfw76otlecSoGLFrRE9cewJnjz+JACaz6BeDg+KjbO0dKj2ZQoBW4AtXUDXosQRf5j8H5gEDq/mX7A0V9xiM7TSOxghJ/S++eam5gt3OpwwTW4TTU4TsVCM9kg77eF2OiIddEQ6/DseTW00N/kVs4gbqetzVcYYYy5uIkJPW4SetghsW3yeXD5VZXIuxdmzI0yPDZOZHsWbPYfMj3Hlwji/PTdJe3aSdu80TZohpFnC+ENEyjfF3wg8FTwERYrXL7Vb3kWzLjt/ZRFgfzC8Ew7xTFszL2Rf5OVfv7zkdptU6ch7dHgeLZ4SUn+ZAziAG3xwRiDlCDMipIMhI+CJkAc8wBPII3jiz+c3SUWikaxmxa472s0Ld71Qpz1dG7VcXZf7X1lc3mrJg4g8CDwYzCaCO3MXq634VS+zviwOjcHiUA/Lv0iwODQOi0VjsDg0BotDY9j0cZBl/sBQR6Wx2FXrSrVU3EaAnSXz/cDoCvKgqk8AT9S6c41MRA7VelvT1I/FoTFYHBqDxaFxWCwag8WhMVgcGoPFoXGsNBa1PMV/EBgUkQERCQP3AAcW5TkA3C++jwEz1Z5vM8YYY4wxxhhTuyXvuKlqTkQeBl7Efx3Ad1T1hIg8FKQ/DjyP36PkSfzXAfx5/XbZGGOMMcYYYzaXmnqQUNXn8StnpcseL5lW4C9Xd9ca3oZo8rkBWBwag8WhMVgcGofFojFYHBqDxaExWBwax4piUdMLuI0xxhhjjDHGrJ8N8KZaY4wxxhhjjNnYrOJWgYj8vYi8KSLHROQpEdlSkvaIiJwUkbdE5DMV1u8SkZdF5J1g3Ll2e7+xiMhnReSEiHgicn3J8j8TkaMlgyci15ZZ/6sicqYk3761/Qs2hipxuFREFkr+fx+vsL6ViVVQJQ6fFpHDInI8GH+qwvpWHlZBpTgEaXaOWCci8qOS7/ZpETlaId/poKwcFZFDa72fG12txxkRuT0oJydF5CtrvZ8bXbVr2UX5rDzUwVLf76BTx38K0o+JyO8stU2ruFX2MnC1qu4F3gYeARCR3fg9a+4Bbgf+WUTcMut/BXhVVQeBV4N5szK/BP4E+GnpQlX9vqpeq6rXAvcBp1W17Eka+MdC3uCZTbN8ZeMQGCr5/32owvpWJlZHpThMAn+oqtcAXwC+V2UbVh4+vLJxsHPE+lLVz5WcF/4D+M8q2W8J8lr36PVR9TgTlItvAXcAu4F7g/JjVk/Za9kKrDysohq/33cAg8HwIPDtpbZrFbcKVPUlVc0Fs6/hv5sO4E7gh6qaVtV38XvSvKHMJu4EvhtMfxf443ru70amqr9S1aVe1n4v8IO12J/NqsY4VGNlYhVUioOqHlHVwvszTwBREYms7d5tHlXKg50jGoCICHA3dl5oZDcAJ1X1lKpmgB/ilwuzSqpcy5r6q+X7fSfw7+p7DdgiIjuqbdQqbrX5C+CFYPoSYLgkbSRYtti2wrvsgnFvXffQfI7qJ+iHg9vQ37EmSXUxICJHROQnIvKJCnmsTKydu4AjqpqukG7loX7sHNEYPgGMqeo7FdIVeCloVvzgGu7XZrLUcabWsmJWR+m17GJWHlZfLd/vZZeBml4HsFGJyCvA9jJJj6rqfwV5HgVywPcLq5XJb11zfki1xKLKujcCSVX9ZYUs3wa+jh+nrwP/gH8AM4usMA5ngd9Q1fMich3wtIjsUdXZuu3oBvchy8Me4DHgtgpZrDzUaIVxsHNEndUYl6VaYXxcVUdFpBd4WUTeVNVyzcBNBdXiQG3HGSsrq2CF17KLWXlYfbV8v5ddBjZ1xU1Vb62WLiJfAP4A+H298N6EEWBnSbZ+YHTxusCYiOxQ1bPBbc/x1djnjWqpWCzhHqqcoFV1rDAtIv8KPPshPmtDW0kcgrs66WD6sIgMAR8BFj/gbGWiRistDyLSDzwF3K+qQxW2beWhRiuMg50j6qyGc3cI//nD66psYzQYj4vIU/jNmuxCdRlqLR9VjjO1lhVTxQqvZRdvw8rD6qvl+73sMmBNJSsQkduBvwb+SFWTJUkHgHtEJCIiA/gPFP5fmU0cwO8ggGBc9VdyszIi4gCfxW87XClPaXvh/fidCphVIiI9hc4XROQy/DJxqkxWKxN1FPQW9hzwiKr+b5V8Vh7qy84R6+9W4E1VHSmXKCItItJWmMa/O23lYBXVeJw5CAyKyICIhPF/hD2wFvu3WVS5li3NY+WhPmr5fh8A7g96l/wYMFNoQl+RqtpQZsB/oHwYOBoMj5ekPQoMAW8Bd5QsfxK4Ppjuxu8p7J1g3LXef9PFOuAf9Efw7+qMAS+WpN0MvFZmndJYfA84DhwLCsmO9f6bLsahUhzwn6c6AbwO/AK/Z8NycbAyUd84/A0wX3LMOgr0lomDlYc6xiFIs3PE+sbm34CHFi3rA54Ppi8LjlevB8euR9d7nzfaUOk4UxqHYH4ffm+HQxaHusSh7LWslYc1+///wPcbeKhwfMJvKvmtIP144fxQbZBgRWOMMcYYY4wxDcqaShpjjDHGGGNMg7OKmzHGGGOMMcY0OKu4GWOMMcYYY0yDs4qbMcYYY4wxxjQ4q7gZY4wxxhhjTIOzipsxxhhjjDHGNDiruBljjDHGGGNMg7OKmzHGGGOMMcY0uP8H1zQMAB+Nx9EAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_threshold(pred_train_simple_ae, pred_val_simple_ae, threshold_simple_ae, transform=np.log)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Performance\\n\",\n \"\\n\",\n \"The performance of this model seems compelling, given that the model is rather simple.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.89 0.82 0.85 1348471\\n\",\n \" 1 0.84 0.90 0.87 1374117\\n\",\n \"\\n\",\n \" accuracy 0.86 2722588\\n\",\n \" macro avg 0.86 0.86 0.86 2722588\\n\",\n \"weighted avg 0.86 0.86 0.86 2722588\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
misclassifiedtotalpercent_misclassified
Infilteration54751809670.676214
Bot805601430950.562983
Benign24056513484710.178398
Brute Force -Web283060.091503
Brute Force -XSS101150.086957
SQL Injection2440.045455
DDoS attacks-LOIC-HTTP22692880950.007876
DoS attacks-Slowloris2654950.004732
DoS attacks-Hulk122309560.000052
DoS attacks-GoldenEye1207540.000048
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 54751 80967 0.676214\\n\",\n \"Bot 80560 143095 0.562983\\n\",\n \"Benign 240565 1348471 0.178398\\n\",\n \"Brute Force -Web 28 306 0.091503\\n\",\n \"Brute Force -XSS 10 115 0.086957\\n\",\n \"SQL Injection 2 44 0.045455\\n\",\n \"DDoS attacks-LOIC-HTTP 2269 288095 0.007876\\n\",\n \"DoS attacks-Slowloris 26 5495 0.004732\\n\",\n \"DoS attacks-Hulk 12 230956 0.000052\\n\",\n \"DoS attacks-GoldenEye 1 20754 0.000048\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print_performance(y_val, pred_val_simple_ae, threshold=threshold_simple_ae)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.2 Stacked Autoencoder\\n\",\n \"\\n\",\n \"The main idea of the stacked autoencoder is to modify an undercomplete autoencoder by adding additional hidden layers, transforming the undercomplete autoencoder into a deep learning model. Adding more layers allows the autoencoder to create more complex representations, which can enhance performance. However, one has to pay attention not to make the autoencoder too powerfull as it might simply recreate the outputs by copying the given inputs if possible. \\n\",\n \"Figure 3 illustrated the architecture of a stacked autoencoder.\\n\",\n \"\\n\",\n \"\\\"Stacked\\n\",\n \"
Figure 3: Stacked Autoencoder (Source)
\\n\",\n \"\\n\",\n \"The autoencoder trained in this notebook is comprised of four hidden layers with a latent representation layer containing 10 units. We also use a `UnitNorm` kernel constraint to restrict the model further. The parameters were determined empirically. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense (Dense) (None, 50) 3050 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 30) 1530 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_2 (Dense) (None, 20) 620 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_3 (Dense) (None, 10) 210 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_4 (Dense) (None, 20) 220 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout (Dropout) (None, 20) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_5 (Dense) (None, 30) 630 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_1 (Dropout) (None, 30) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_6 (Dense) (None, 50) 1550 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_2 (Dropout) (None, 50) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_7 (Dense) (None, 60) 3060 \\n\",\n \"=================================================================\\n\",\n \"Total params: 10,870\\n\",\n \"Trainable params: 10,870\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"K.clear_session()\\n\",\n \"gc.collect()\\n\",\n \"\\n\",\n \"activation = 'elu'\\n\",\n \"dropout= 0.1\\n\",\n \"\\n\",\n \"stacked_ae = models.Sequential([\\n\",\n \" layers.Dense(50, activation=activation, kernel_constraint=constraints.UnitNorm(axis=0), input_shape=(input_dims,)),\\n\",\n \" layers.Dense(30, activation=activation, kernel_constraint=constraints.UnitNorm(axis=0)),\\n\",\n \" layers.Dense(20, activation=activation, kernel_constraint=constraints.UnitNorm(axis=0)),\\n\",\n \" layers.Dense(10, activation=activation, kernel_constraint=constraints.UnitNorm(axis=0)),\\n\",\n \" layers.Dense(20, activation=activation),\\n\",\n \" layers.Dropout(dropout),\\n\",\n \" layers.Dense(30, activation=activation),\\n\",\n \" layers.Dropout(dropout),\\n\",\n \" layers.Dense(50, activation=activation),\\n\",\n \" layers.Dropout(dropout),\\n\",\n \" layers.Dense(input_dims, activation='sigmoid')\\n\",\n \"])\\n\",\n \"\\n\",\n \"stacked_ae.compile(optimizer='adam', loss='binary_crossentropy')\\n\",\n \"stacked_ae.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"early_stopping = callbacks.EarlyStopping(monitor='val_auprc', \\n\",\n \" mode='max',\\n\",\n \" patience=15, \\n\",\n \" restore_best_weights=True)\\n\",\n \"\\n\",\n \"reduce_lr = callbacks.ReduceLROnPlateau(monitor='val_auprc', \\n\",\n \" mode='max', \\n\",\n \" factor=0.2, \\n\",\n \" patience=3, \\n\",\n \" min_lr=0.0001)\\n\",\n \"\\n\",\n \"mc = callbacks.ModelCheckpoint(filepath='models/stacked_autoencoder_model.h5',\\n\",\n \" monitor='val_auprc', \\n\",\n \" mode='max',\\n\",\n \" save_best_only=True, \\n\",\n \" verbose=0)\\n\",\n \"\\n\",\n \"hist = stacked_ae.fit(x=X_train, \\n\",\n \" y=X_train, \\n\",\n \" validation_data=(X_val_benign, X_val_benign),\\n\",\n \" batch_size=batch_size,\\n\",\n \" epochs=epochs,\\n\",\n \" callbacks=[\\n\",\n \" AveragePrecisionScoreMetric(X_val=X_val, y_val=y_val.label_is_attack, batch_size=16384),\\n\",\n \" early_stopping,\\n\",\n \" reduce_lr,\\n\",\n \" mc\\n\",\n \" ])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[Output omitted for brevity]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Validation\\n\",\n \"\\n\",\n \"The learning curves suggest that the model does not overfit the training data. As we use `Dropout` layers, the validation loss is lower than the training loss.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_hist(hist)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The plot of the average precision score shows a rather unstable performance, with the best score obtained at epoch 15.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_hist(hist, metrics=['val_auprc'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"With this model we obtain an average precision score of `0.88549`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"stacked_ae = models.load_model('models/stacked_autoencoder_model.h5')\\n\",\n \"pred_train_stacked_ae = predict(stacked_ae, X_train, y_train.label_is_attack)\\n\",\n \"pred_val_stacked_ae = predict(stacked_ae, X_val, y_val.label_is_attack)\\n\",\n \"\\n\",\n \"plot_evaluation_curves(pred_val_stacked_ae)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_pr_threshold_curves(pred_val_stacked_ae, pr_plot_lim=[0, 0.0001])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Once again we choose a threshold to define the decision boundary using the validation data. \\n\",\n \"The threshold is chosen to obtain a minimum recall of `0.9`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"6.451055123624049e-06\\n\"\n ]\n }\n ],\n \"source\": [\n \"threshold_stacked_ae = best_precision_for_target_recall(pred_val_stacked_ae, target_recall=0.9)\\n\",\n \"print(threshold_stacked_ae)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The distribution plot shows considerable overlap of benign and malicious traffic. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_threshold(pred_train_stacked_ae, pred_val_stacked_ae, threshold_stacked_ae, transform=np.log)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Performance\\n\",\n \"\\n\",\n \"The performance of the stacked autoencoder falls short of the performance of the undercomplete autoencoder, which is somewhat surprising.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.88 0.75 0.81 1348471\\n\",\n \" 1 0.79 0.90 0.84 1374117\\n\",\n \"\\n\",\n \" accuracy 0.83 2722588\\n\",\n \" macro avg 0.83 0.83 0.82 2722588\\n\",\n \"weighted avg 0.83 0.83 0.82 2722588\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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misclassifiedtotalpercent_misclassified
Infilteration45002809670.555807
Bot764851430950.534505
Benign33671513484710.249701
SQL Injection5440.113636
Brute Force -Web183060.058824
DDoS attacks-LOIC-HTTP154792880950.053729
DoS attacks-GoldenEye100207540.004818
DoS attacks-Hulk3222309560.001394
DDOS attack-HOIC13430060.000003
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 45002 80967 0.555807\\n\",\n \"Bot 76485 143095 0.534505\\n\",\n \"Benign 336715 1348471 0.249701\\n\",\n \"SQL Injection 5 44 0.113636\\n\",\n \"Brute Force -Web 18 306 0.058824\\n\",\n \"DDoS attacks-LOIC-HTTP 15479 288095 0.053729\\n\",\n \"DoS attacks-GoldenEye 100 20754 0.004818\\n\",\n \"DoS attacks-Hulk 322 230956 0.001394\\n\",\n \"DDOS attack-HOIC 1 343006 0.000003\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print_performance(y_val, pred_val_stacked_ae, threshold=threshold_stacked_ae)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.3 Denoising Autoencoder\\n\",\n \"\\n\",\n \"Another variant of the autoencoder is the denoising autoencoder. In this version, the model is restricted not by limiting the size of the hidden layers but by adding additional noise to the inputs. The objective of the model is to recreate the inputs without containing the noise. \\n\",\n \"Figure 4 illustrates the architecture of a denoising autoencoder.\\n\",\n \"\\n\",\n \"\\\"Denoising\\n\",\n \"
Figure 4: Denosing Autoencoder (Source)
\\n\",\n \"\\n\",\n \"Denoising autoencoders are typically used in image processing to denoise images but can also be applied to tabular data using a simple method orginally introduced by [Porto Seguro](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/discussion/44629) as the winning strategy in a Kaggle competition. \\n\",\n \"The idea is to randomly shuffle a portion of the input data row-wise on the complete training set. The percentage of values shuffled are determined by a likelihood, which acts as a hyperparameter of the model. This approach introduces noise and ensures that the noise originates from the same distribution as the original data. \\n\",\n \"The following approach is further inspired by, and uses utilities introduced in, a blog post from [Michael Klear](https://towardsdatascience.com/how-to-apply-self-supervision-to-tabular-data-introducing-dfencoder-eec21c4afaef).\\n\",\n \"\\n\",\n \"The denoising autoencoder presented in this notebook is overcomplete, hence it contains more units in the hidden layers than in the input layer. The model consists of five layers containing 512 units per layer. The likelihood for introducing noise is set to 15%. The parameters were determined empirically. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X_train_swapped = EncoderDataFrame(pd.DataFrame(data=X_train).copy()).swap(likelihood=.15).values\\n\",\n \"X_val_benign_swapped = EncoderDataFrame(pd.DataFrame(data=X_val_benign).copy()).swap(likelihood=.15).values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model: \\\"sequential\\\"\\n\",\n \"_________________________________________________________________\\n\",\n \"Layer (type) Output Shape Param # \\n\",\n \"=================================================================\\n\",\n \"dense (Dense) (None, 512) 31232 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout (Dropout) (None, 512) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_1 (Dense) (None, 512) 262656 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_1 (Dropout) (None, 512) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_2 (Dense) (None, 512) 262656 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_2 (Dropout) (None, 512) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_3 (Dense) (None, 512) 262656 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_3 (Dropout) (None, 512) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_4 (Dense) (None, 512) 262656 \\n\",\n \"_________________________________________________________________\\n\",\n \"dropout_4 (Dropout) (None, 512) 0 \\n\",\n \"_________________________________________________________________\\n\",\n \"dense_5 (Dense) (None, 60) 30780 \\n\",\n \"=================================================================\\n\",\n \"Total params: 1,112,636\\n\",\n \"Trainable params: 1,112,636\\n\",\n \"Non-trainable params: 0\\n\",\n \"_________________________________________________________________\\n\"\n ]\n }\n ],\n \"source\": [\n \"K.clear_session()\\n\",\n \"gc.collect()\\n\",\n \"\\n\",\n \"activation = 'elu' \\n\",\n \"dropout = 0.1\\n\",\n \"\\n\",\n \"denoising_ae = models.Sequential([\\n\",\n \" layers.Dense(512, activation=activation, input_shape=[input_dims]),\\n\",\n \" layers.Dropout(dropout),\\n\",\n \" layers.Dense(512, activation=activation),\\n\",\n \" layers.Dropout(dropout),\\n\",\n \" layers.Dense(512, activation=activation),\\n\",\n \" layers.Dropout(dropout),\\n\",\n \" layers.Dense(512, activation=activation),\\n\",\n \" layers.Dropout(dropout),\\n\",\n \" layers.Dense(512, activation=activation),\\n\",\n \" layers.Dropout(dropout),\\n\",\n \" layers.Dense(input_dims, activation='sigmoid')\\n\",\n \"])\\n\",\n \"\\n\",\n \"denoising_ae.compile(optimizer='adam', loss='binary_crossentropy')\\n\",\n \"denoising_ae.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"early_stopping = callbacks.EarlyStopping(monitor='val_auprc', \\n\",\n \" mode='max',\\n\",\n \" patience=15, \\n\",\n \" restore_best_weights=True)\\n\",\n \"\\n\",\n \"reduce_lr = callbacks.ReduceLROnPlateau(monitor='val_auprc', \\n\",\n \" mode='max', \\n\",\n \" factor=0.2, \\n\",\n \" patience=3, \\n\",\n \" min_lr=0.0001)\\n\",\n \"\\n\",\n \"mc = callbacks.ModelCheckpoint(filepath='models/denoising_autoencoder_model.h5', \\n\",\n \" monitor='val_auprc', \\n\",\n \" mode='max',\\n\",\n \" save_best_only=True, \\n\",\n \" verbose=0)\\n\",\n \"\\n\",\n \"hist = denoising_ae.fit(x=X_train_swapped, \\n\",\n \" y=X_train, \\n\",\n \" validation_data=(X_val_benign_swapped, X_val_benign),\\n\",\n \" batch_size=batch_size,\\n\",\n \" epochs=epochs,\\n\",\n \" callbacks=[\\n\",\n \" AveragePrecisionScoreMetric(X_val=X_val, y_val=y_val.label_is_attack, batch_size=16384),\\n\",\n \" early_stopping,\\n\",\n \" reduce_lr,\\n\",\n \" mc\\n\",\n \" ])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[Output omitted for brevity]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Validation\\n\",\n \"\\n\",\n \"The learning curves suggest that the model does not overfit the training data. As we use `Dropout` layers the validation loss is lower than the training loss.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_hist(hist)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The plot of the average precision score unveils a very unstable performance progression, with the best score obtained at epoch 17.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_hist(hist, metrics=['val_auprc'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using this model we obtain an average precision score of `0.93419`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"denoising_ae = models.load_model('models/denoising_autoencoder_model.h5')\\n\",\n \"pred_train_denoising_ae = predict(denoising_ae, X_train, y_train.label_is_attack)\\n\",\n \"pred_val_denoising_ae = predict(denoising_ae, X_val, y_val.label_is_attack)\\n\",\n \"\\n\",\n \"plot_evaluation_curves(pred_val_denoising_ae)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_pr_threshold_curves(pred_val_denoising_ae, pr_plot_lim=[0, 0.0001])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As before, we choose a threshold to define the decision boundary using the validation data. \\n\",\n \"The threshold is chosen to obtain a minimum recall of `0.9`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"8.805178334767583e-06\\n\"\n ]\n }\n ],\n \"source\": [\n \"threshold_denoising_ae = best_precision_for_target_recall(pred_val_denoising_ae, target_recall=0.9)\\n\",\n \"print(threshold_denoising_ae)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The distribution plot shows some overlap of benign and malicious traffic.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_threshold(pred_train_denoising_ae, pred_val_denoising_ae, threshold_denoising_ae, transform=np.log)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Performance\\n\",\n \"\\n\",\n \"The performance of the denoising autoencoder surpasses the performance of the other models.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.89 0.85 0.87 1348471\\n\",\n \" 1 0.86 0.90 0.88 1374117\\n\",\n \"\\n\",\n \" accuracy 0.88 2722588\\n\",\n \" macro avg 0.88 0.88 0.88 2722588\\n\",\n \"weighted avg 0.88 0.88 0.88 2722588\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
misclassifiedtotalpercent_misclassified
Infilteration63161809670.780083
SQL Injection22440.500000
Bot674111430950.471093
Brute Force -Web1293060.421569
Brute Force -XSS461150.400000
DoS attacks-Slowloris113754950.206915
Benign19637113484710.145625
DDoS attacks-LOIC-HTTP55632880950.019310
DDOS attack-LOIC-UDP38650.003468
DoS attacks-GoldenEye2207540.000096
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 63161 80967 0.780083\\n\",\n \"SQL Injection 22 44 0.500000\\n\",\n \"Bot 67411 143095 0.471093\\n\",\n \"Brute Force -Web 129 306 0.421569\\n\",\n \"Brute Force -XSS 46 115 0.400000\\n\",\n \"DoS attacks-Slowloris 1137 5495 0.206915\\n\",\n \"Benign 196371 1348471 0.145625\\n\",\n \"DDoS attacks-LOIC-HTTP 5563 288095 0.019310\\n\",\n \"DDOS attack-LOIC-UDP 3 865 0.003468\\n\",\n \"DoS attacks-GoldenEye 2 20754 0.000096\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print_performance(y_val, pred_val_denoising_ae, threshold=threshold_denoising_ae)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.4 Performance Comparison and Model Selection\\n\",\n \"\\n\",\n \"Comparing the models we obtain the following performance characteristics:\\n\",\n \"\\n\",\n \"|Model |PR Score |Precision|Recall |F1 |Precision Attack|Recall Attack|\\n\",\n \"|-------------------------|-----------|---------|--------|--------|----------------|-------------|\\n\",\n \"|Undercomplete Autoencoder|0.91972 |0.86 |0.86 |0.86 |0.84 |0.90 |\\n\",\n \"|Stacked Autoencoder |0.88549 |0.83 |0.83 |0.84 |0.79 |0.90 |\\n\",\n \"|Denoising Autoencoder |**0.93419**|**0.88** |**0.88**|**0.88**|**0.86** |**0.90** |\\n\",\n \"\\n\",\n \"We see that the denoising autoencoder obtains the best performance, with the undercomplete autoencoder falling short only slightly, which is rather impressive given that the undercomplete autoencoder is based on a much simpler architecture. \\n\",\n \"Neverthless we use the denoising autoencoder going forward, as it has best performance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.5 Model Performance Omitting the Top Misclassified Attack Class\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this section we test the performance of the classifier on the validation set, omitting the attack class with the most misclassified samples. The rationale being, that choosing the decision boundary without including this class might lead to a better overall performance of the classifier on the remaining attack classes, at the cost of not being able to detect attacks of this class.\\n\",\n \"\\n\",\n \"As roughly `78%` of all `Infiltration` traffic was misclassified, this class is chosen to be omitted from the validation set.\\n\",\n \"\\n\",\n \"As a result we gain a slightly better average precision score of `0.94698`, with precision of `0.90` and recall of `0.90`. The slight increase in performance does not warrant the removal of the class though. The removal even lowers the detection rate of other classes such as `Bot`, which now has a misclassification rate of `81%` instead of `47%` because of the modified decision boundary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"top_misclassified_mask = (y_val.label != 'Infilteration')\\n\",\n \"X_val_wo_top = X_val[top_misclassified_mask]\\n\",\n \"y_val_wo_top = y_val[top_misclassified_mask]\\n\",\n \"\\n\",\n \"pred_denoising_ae_wo_top = predict(denoising_ae, X_val_wo_top, y_val_wo_top.label_is_attack)\\n\",\n \"plot_evaluation_curves(pred_denoising_ae_wo_top)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.90 0.90 0.90 1348471\\n\",\n \" 1 0.89 0.90 0.90 1293150\\n\",\n \"\\n\",\n \" accuracy 0.90 2641621\\n\",\n \" macro avg 0.90 0.90 0.90 2641621\\n\",\n \"weighted avg 0.90 0.90 0.90 2641621\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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misclassifiedtotalpercent_misclassified
Bot1161411430950.811636
SQL Injection26440.590909
Brute Force -Web1343060.437908
Brute Force -XSS501150.434783
DoS attacks-Slowloris114154950.207643
Benign13928513484710.103291
DDoS attacks-LOIC-HTTP114982880950.039910
DDOS attack-LOIC-UDP48650.004624
DoS attacks-Hulk3172309560.001373
DoS attacks-GoldenEye4207540.000193
SSH-Bruteforce1937940.000011
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Bot 116141 143095 0.811636\\n\",\n \"SQL Injection 26 44 0.590909\\n\",\n \"Brute Force -Web 134 306 0.437908\\n\",\n \"Brute Force -XSS 50 115 0.434783\\n\",\n \"DoS attacks-Slowloris 1141 5495 0.207643\\n\",\n \"Benign 139285 1348471 0.103291\\n\",\n \"DDoS attacks-LOIC-HTTP 11498 288095 0.039910\\n\",\n \"DDOS attack-LOIC-UDP 4 865 0.004624\\n\",\n \"DoS attacks-Hulk 317 230956 0.001373\\n\",\n \"DoS attacks-GoldenEye 4 20754 0.000193\\n\",\n \"SSH-Bruteforce 1 93794 0.000011\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"threshold_denoising_ae_wo_top = best_precision_for_target_recall(pred_denoising_ae_wo_top, target_recall=0.9)\\n\",\n \"print_performance(y_val_wo_top, pred_denoising_ae_wo_top, threshold=threshold_denoising_ae_wo_top)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Performance on Test Set\\n\",\n \"\\n\",\n \"In this section we evaluate the performance on the test set using the best performing model, in our case the denoising autoencoder.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Average Precision Score: 0.9341264614111068\\n\",\n \"\\n\",\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.89 0.85 0.87 1348471\\n\",\n \" 1 0.86 0.90 0.88 1374118\\n\",\n \"\\n\",\n \" accuracy 0.88 2722589\\n\",\n \" macro avg 0.88 0.88 0.88 2722589\\n\",\n \"weighted avg 0.88 0.88 0.88 2722589\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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WSnkmewsoqkAdl59zWYpGZtS5inl8k1Q+t5PrAwpA+D9gpab9GwPyQ3iif9OQ88yRVAGoSdZfMA47Ik2cUsAjYTlKF0FpOLqtA3qfsnItNlgf68jMMSMyG6AEMTUrvGmZU7Eo0oDc+dHWskHRQ6BM+K0+eRFknAx+GfufhQAdJtcIAXwdgeNj2Udg37/EL5C1l51xssnnxiKRXiFqsdSXNI5oRcRcwWFIvYC5wCoCZTZc0GPgKyAEuNrP1oagLiWZyVAHeCw+AZ4AXJM0haiF3DWUtkdQX+Dzsd7uZJQYcrwMGSvoX8EUoo1AelJ1z8cniQJ+ZdStgU/sC9r8DuCOf9AlAi3zS1xKCej7bngWezSf9W6JpcmnzoOyci41fZp2aB2XnXCw2s2+4zPCBPuecK0G8peyci423lFPzoOyci40H5dQ8KDvn4uMxOSUPys652HhLOTUPys65ePidR9Lisy+cc64E8Zaycy4WAryhnJoHZedcTPzikXR4UHbOxcZjcmoelJ1zsfGWcmoelJ1z8ZC3lNPhsy+cc64E8Zaycy4WAsqV86ZyKh6UnXOx8e6L1DwoO+di4wN9qXlQds7Fwwf60uJB2TkXi+iKPo/KqfjsC+ecK0G8peyci4lfZp0OD8rOudh4TE7Ng7JzLjbeUk7Ng7JzLh4++yItHpSdc7Hw2Rfp8dkXxaTf1cfww6uXMOGpc3LTTmq3BxOf7sWqD65l/2Y75qbvXK8GS965ks/69eSzfj15+PIOAFSpXIHX7ziZyc+ey8Sne9H33MM3Oc6JbfdgzX+v26i8lcOvyS3r1dtPyk3fZceafPzImUztfx4v3HQCFSv4jwfAZReeS/PGDTjsgJa5adOmTqHTkYfRtk1LTj+lCyt++y132/RpX9LpyMM4tPW+tG3TkrVr1wIw+YuJtG3TkgP2aU7vq6/AzAB4/JEHOKTVPrQ7cD9O/FsHfpz7Q25Zp3b5G7s1rEu3kzvHdLbZJWX+KCuy9lsnab2kyZKmSJok6ZDNKOt2SUdtyfoVtxeGT6Vz71c3Spv+/SK63voGY6b+uMn+385fxkEX9OegC/pz2UMf5KY/OHg8Lc95moMueI6D925IhwN2y91WrUolLjqxFeO/nr9RWWv+yMkt65RbXs9Nv+O8I3jktQn8pedTLF2xlp7H7LOlTner1rV7Dwa9+fZGaVdc/A9uvu3ffDJ+Mn87vjOPPvgfAHJycriwVw/ue+gxPp0whaHvjaRixYoAXHPFJdz/yBOMn/I1334zh5EjhgPwl33247+ffMbH477g+C4ncetNvXOPc8nlV/H4U/3jOVFXImSzKbTGzFqa2b5Ab+DOTAsys1vM7L9brmrF79Op81iyYs1GaTPnLmb2vCVpl7Hm9xw+njIXgHU5G5g8+xcabl89d3ufnm25f9A41v6Rk1Z5h7fcmdc/ngHASx9M4/hDm6Vdl9LskMPaUqtW7Y3S5syexSGHtQXgiCOP4q2hbwDw0cgR7NXiL7T4y74A1K5Th/Lly/PzzwtY8dsKDjjwYCRxarczePetoQC0PfwIqlatCkDrNgeyYP683OO0++uRVKtWndJCUsaPsiKu76c1gKWJF5KukfS5pC8l3RbSGkv6WtJTkqZL+kBSlbCtv6STw/NjJc2QNEbSw5LeDum3SnpW0ihJ30q6LKZzi0XjHWsytl9PPvhPNw5t0WiT7TW3rcyxBzfhoy++B2DfJjvQaIfqvDfum0323aZSBcY8dhajHzmT4w9pCkCdGlVYvvJ31m+IvlL/tGgFDepUy94JbeX23Gtv3nvnLQCGvjGEn36Kvt18M2cWkjil87H89dADePiB+wBYMP8nGjRsmJu/QcNGLFgwf5NyXxrwHO2P7hTDGRQP775ILZsDfVUkTQa2AeoDRwJI6gA0BdoQ9f0Pk9QOmBvSu5nZeZIGA38HXkwUKGkb4P+Admb2naRX8hyzOfBXoDowU9ITZrYui+cYi5+XrKJZ9ydY8tta9mtaj8G3ncT+5z7DitV/AFC+nBhw4wk8/sZEvl+wHAnuubA9593zTr7lNTv9CRYsXknj+jV5/95uTPvuV1as/n2T/SyrZ7V1e/jxp+h9zT+5765/0enY46lUqRIAOTnrGTf2f4wYPZYqVaty0nEdaNlyf6rV2LS1m7f1N3jgS0z+YiLD3v8wlnOInXygLx1xdF80BzoBzyv6RDqExxfAJKJA2jTk+c7MJofnE4HGecpsDnxrZt+F13mD8jtm9ruZLQIWAvXyVkrS+ZImSJpg61Zt3hnG5I9161nyWzRY9MXsX/h2wTKaNvrz6/RjV3bim5+W8OjrEwCoXrUSezWuywf/OZ0ZL15Amz0bMOT2k3IH+xYsXgnA9wuW8/GUubRsUo9Fy9dQs1plyof1bhvWrZ67n9tU0z2aM2TYe3w4ZjwnnXIajXeN+vIbNGjIIYe1pU7dulStWpWjOhzDlClf0KBhI+b/9FNu/vk/zWPHHevnvh790UgeuOcuXhz0BpUrV479fOKQuJu1t5QLF0v3hZmNBeoC2xN9NneGgN3SzJqY2TNh1+Tm2no2bcmn+mhS5cfMnjSz1mbWWhW3LdJ5FJe6NavkLg7euH5NmjSsxXcLlgHQ5+y21Ny2Mlc/PjJ3/99W/cFOf3+E5mf0o/kZ/Rj/9XxOvuV1Js36me2qVaZSxfJA1GVx8N4N+fqHRQB8PHkuJ7VrDkD3Di14+3+z4zzNrcqvCxcCsGHDBu6/59/07HU+AEce1YHp06ayevVqcnJy+N+Yj9mj+Z7suGN9qlWvxoTxn2FmDH7lRY457gQAvpzyBVdddhEvDn6d7XfYodjOKfsy708uSy3sWOYpS2oOlAcWA8OBvpJeMrOVkhoC6XYxzAB2k9TYzL4HTstKhWMw4IbjabvvztStWYU5r1xE3wFjWLpiDfdfcjR1a1bh9TtO5stvFnLC9YM5bJ+duLlHW3LWb2D9hg1c+uBwlq5YS8O61bm++yHM+GExY5/oCUC/oZPo/96XBR63+c51eeSfHdmwwShXTtw3cBwz5i4G4ManR/HCjSfQ5+y2TJnzS6HllCXn9TyDTz8ZzZLFi/hLs8Zcd+MtrFq5kmee6gfAcSd04fQzewKwXa1aXHjpFRzdLhrQO6pjJzp0OhaAex98lEv/cS5r166h/dEdOapD1Hd8643Xs2rlSnqd2RWAhjvtzEuDo4HD444+gtmzZrJq1Ur+0qwxDz3+JEce1SHmd8DFSYm5klu8YGk9MDXxErjBzN4J2y4Hzg3bVgJnELVs3zazFmGfq4FqZnarpP5h2xBJxwP3AouA8UA9M+su6VZgpZndF/JPA44LwTtf5Wo0ssptLt2CZ+22pHlDryruKrgCtG97IJMnTSxS87Vao+a2z2VPZnzMsdcdPtHMWmdcwFYiay1lMytfyLaHgIfy2dQiaZ/7kp73TNrnIzNrHvqnHwMmhH1uzXOMFjjnSpSy1A2Rqa3xkq3zwqyO6UBNotkYzrmSbjMG+cpSLN/q1r4wsweAB4q7Hs65ovG1L9Kz1QVl59zWy4Nyaltj94VzzpVa3lJ2zsXGG8qpeVB2zsXGuy9S86DsnItHGZtFkSkPys65WMjvZp0WD8rOudh4TE7NZ18451wJ4i1l51xsynlTOSVvKTvnYpPNy6wl/TPctWiapFckbSOptqQRkmaH/2sl7d9b0hxJMyV1TEpvJWlq2PZwWGcHSZUlDQrp4yQ1TsrTIxxjtqQem/MeeVB2zsVCyt49+sISwJcBrcNiZOWBrsD1wEgzawqMDK+RtFfYvjfRTTgel5RYRO0J4Hyim280DdsBegFLzawJ0VIPd4eyagN9gAOJ7qjUJzn4F5UHZedcbMop80caKhDdhq4CUBWYD3QGBoTtA4Au4XlnYGC4U9F3wBygjaT6QA0zG2vRusbP58mTKGsI0D60ojsCI8xsiZktBUbwZyAv+nuUaUbnnCuqbLWUzewn4D6ie30uAJab2QdE660vCPssABK3dmkI/JhUxLyQ1jA8z5u+UR4zywGWA3UKKSsjHpSdc1uLuon7a4bH+YkNobugM7Ar0ADYVtIZhZSVX5S3QtIzzVNkPvvCORebzZx8saiQO48cRXTj5V+j4+h14BDgF0n1zWxB6JpYGPafB+yUlL8RUXfHvPA8b3pynnmhi6QmsCSkH5Enz6hMThC8peyci4kIV/Vl+C+FucBBkqqGft72wNfAMCAxG6IHMDQ8HwZ0DTMqdiUa0BsfujhWSDoolHNWnjyJsk4GPgz9zsOBDpJqhRZ7h5CWEW8pO+dik+aAXZGZ2ThJQ4BJQA7wBfAkUA0YLKkXUeA+Jew/XdJg4Kuw/8Vmtj4UdyHQH6gCvBceAM8AL0iaQ9RC7hrKWiKpL/B52O92M1uS6bl4UHbOxSONAbvNYWZ9iKamJfudqNWc3/53AHfkkz6BpPuFJqWvJQT1fLY9CzxbxCrny4Oycy42fkFfat6n7JxzJYi3lJ1zsRC+9kU6CgzKkmoUltHMftvy1XHOlWYek1MrrKU8nU0nRideG7BzFuvlnCuFfJH71AoMyma2U0HbnHOuqNJd7a2sS2ugT1JXSTeE540ktcputZxzpVE5KeNHWZEyKEt6FPgrcGZIWg30y2alnHOurEpn9sUhZra/pC8g9+qVSlmul3OuFCo77d3MpROU10kqR1j1SFIdYENWa+WcK5V8oC+1dILyY8BrwPaSbgNOBW7Laq2cc6VONE+5uGtR8qUMymb2vKSJREvjAZxiZtOyWy3nXKmT5bUvSot0r+grD6wj6sLwS7OdcxnxmJxaOrMvbgReIVrNvxHwsqTe2a6Yc86VRem0lM8AWpnZagBJdwATgTuzWTHnXOnj3ReppROUf8izXwXg2+xUxzlXWvlAX3oKW5DoAaI+5NXAdEnDw+sOwJh4quecK028pZxaYS3lxAyL6cA7SemfZa86zrnSzENyaoUtSPRMnBVxzpVukq+nnI6UfcqSdie6j9VewDaJdDNrlsV6OedcmZTOnOP+wHNE3zyOAQYDA7NYJ+dcKZVYvjOTR1mRTlCuambDAczsGzO7iWjVOOecKxKFq/oyeZQV6UyJ+13RO/KNpAuAn4Adslst51xpVIZia8bSCcr/BKoBlxH1LdcEzslmpZxzpY8oW4vVZyqdBYnGhacr+HOhe+ecK5oy1jecqcIuHnmDsIZyfszspKzUyDnnyrDCWsqPxlaLYrJf0x359P3rirsargC1DrikuKvgCvD7zB8zyleWBuwyVdjFIyPjrIhzrvTzdX9TS3c9Zeec2yzCW8rp8KDsnIuNrxKXWtpBWVJlM/s9m5VxzpVuHpRTS+fOI20kTQVmh9f7Snok6zVzzrkyKJ1+94eB44DFAGY2Bb/M2jlXRNEaFn6ZdSrpdF+UM7Mf8rwp67NUH+dcKebdF6mlE5R/lNQGMEnlgUuBWdmtlnOuNCpDDd6MpROULyTqwtgZ+AX4b0hzzrm0Rffo86icSjprXywEusZQF+dcKecXj6SWzp1HniKfNTDM7Pys1Mg558qwdLov/pv0fBvgRCCzC9+dc2Wa916klk73xaDk15JeAEZkrUbOuVJJ8vWU05HJZda7Arts6Yo450o/j8mppdOnvJQ/+5TLAUuA67NZKedc6eTzlFMrNCiHe/PtS3RfPoANZlbgwvfOOVcQnxKXnkJnqIQA/IaZrQ8PD8jOOZdF6UwbHC9p/6zXxDlX6kmZP8qKwu7RV8HMcoDDgPMkfQOsIvoWYmbmgdo5lz55n3I6CutTHg/sD3SJqS7OuVJOeFROpbDuCwGY2Tf5PWKqn3OulIgG+jJ/pCxf2k7SEEkzJH0t6WBJtSWNkDQ7/F8raf/ekuZImimpY1J6K0lTw7aHw4QHJFWWNCikj5PUOClPj3CM2ZJ6bM77VFhLeXtJVxa00czu35wDO+fKnix3XzwEvG9mJ0uqBFQFbgBGmtldkq4nms57naS9iNb02RtoAPxXUjMzWw88AZwPfAa8C3QC3gN6AUvNrImkrsDdwGmSagN9gNZE04cnShpmZkszOYnCWsrlgWpA9QIezjlXIkiqAbQDngEwsz/MbBnQGRgQdhvAn92xnYGBZva7mbXUaXkAABprSURBVH0HzAHaSKoP1DCzsWG22fN58iTKGgK0D63ojsAIM1sSAvEIokCekcJaygvM7PZMC3bOubyyeAeR3YBfgeck7QtMBC4H6pnZAgAzWyBph7B/Q6KWcMK8kLYuPM+bnsjzYygrR9JyoE5yej55iixln7Jzzm0JW6BPua6kCUmP5JUqKxBNTHjCzPYjmilW2JXH+cU3KyQ90zxFVlhLuX2mhTrn3CY2f77xIjNrXcC2ecA8MxsXXg8hCsq/SKofWsn1gYVJ+++UlL8RMD+kN8onPTnPPEkVgJpEy07MA47Ik2dUkc8uKLClbGZLMi3UOefyUy6sFJfJozBm9jPRrev2CEntga+AYUBiNkQPYGh4PgzoGmZU7Ao0BcaHro4Vkg4K/cVn5cmTKOtk4MPQ7zwc6CCpVpjd0SGkZSSTVeKcc67IEt0XWXQp8FKYefEtcDZRw3OwpF7AXOAUADObLmkwUeDOAS4OMy8gut1df6AK0ayL90L6M8ALkuYQtZC7hrKWSOoLfB72u31zGrUelJ1zpYKZTSaalpZXvl2xZnYHcEc+6ROAFvmkryUE9Xy2PQs8W5T6FsSDsnMuNmVpDYtMeVB2zsVElPNJXSl5UHbOxUJ4SzkdHpSdc/HwVeLS4kHZORcbv/NIaukscu+ccy4m3lJ2zsXC+5TT40HZORcb775IzYOycy42HpNT86DsnIuF8EGsdHhQds7FQ1ldT7nU8D9czjlXgnhL2TkXG28np+ZB2TkXi2jpTg/LqXhQds7FxkNyah6UnXOx8YZyah6UnXMxkc++SIPPvnDOuRLEg3IJ8I9zz2HnBjvQquWfd6C5rc/NHLDfPhzYqiXHHdOB+fOjG+q+8vJLHNiqZe6jaqVyTJk8GYA+N99Ik113ou521TY5xpBXB7PfPnux/7570+PM0wEYPeqjjcrarto2DBv6ZgxnXLL169OdH0beyYRXb8hN+/cVXZj8+k2MH9SbQf85j5rVqgDQeu9d+Gzg9Xw28HrGDbqeE/66T26eihXK8+hN3fjyzVuY/PpNdGnfEoCd69fi3X6XMn5Qb4Y/dTkNd9guN8/KCQ/nlvfqg//ITb/gtHZMG9qHNV88Sp3tts32W5AViYtHMn2UFYpuxhrDgaQTgdeBPc1shqTGwCFm9nLY3hJoYGbvZlj+90BrM1uUbp5WrVrbp+MmZHK4LWrMJx+z7bbVOPecs5g4eRoAv/32GzVq1ADgsUceZsbXX/HI4/02yjdt6lRO+Xtnvp71LQDjPvuMnXfZhb/s2ZRFy1bm7jdn9mzO6HYq7434kFq1arFw4UJ22GGHjcpasmQJLZo3Yc7386hatWo2TzdttQ64pFiOe+j+u7Nq9e883fcsWp/ybwDaH9ScUZ/PYv36Dfzrss4A3PTwUKpsU5E/1q1n/foN7Fi3BuMG9Wa3Djeyfv0GbrrgWMqXK8dtj7+NJGrXrMriZat46Z5zePeT6bz01jgOP6AZZ51wEL1ufh6AXz/9D9sfetUmddp3j0Ys/W01Hzx9OYd2v4fFy1bF94bk4/eZg9mwemGR+iJ232tfu/Pl91LvWIDT9ms40czyuwdfqRLnH6BuwBjCHWCBxsDpSdtbAsfGWJ8S47C27ahdu/ZGaYmADLB69ap8++IGD3qFU0/rlvv6wIMOon79+pvs9+wzT/GPCy+mVq1aAJsEZIA3XhtCh47HlJiAXJw+nfQNS5av3iht5GczWL9+AwDjp35Hw3pR63bN2nW56ZUrVSS5kdOj88Hc++wHAJhZbiBtvlt9Ro2bCcDoz2dx3BF/SVmnKTPnMXdBxjdILjG0GY+yIpagLKkacCjQiz+D8l1AW0mTJV0H3A6cFl6fJqmNpP9J+iL8v0coq7yk+yRNlfSlpEvzHKuKpPclnRfHuWVTojti4CsvcfOtt2+yfcirgzYKygWZPXsWs2fP4q/tDqXdoQfxwfD3N9nn1cEDObVr6rIcnNX5YIZ/+lXu6wNa7MLEITcy4dUbuOyOgaxfvyG3e6PPxcfxv5ev46V7zmGH2tUBmDrrp9yujM5H7kuNalWoXTPqktimUgXGvHQtowdcxfFH7EOpEi6zzvRRVsTVUu4CvG9ms4AlkvYHrgc+MbOWZnY3cAswKLweBMwA2pnZfmHbv0NZ5wO7AvuZ2T7AS0nHqQa8BbxsZk/lVxFJ50uaIGnCr4t+zcKpbjm39b2DOd/9SNdu3en3+KMbbRs/bhxVq1Rl7xab3Al9E+tzcpgzZzYfjBzF8y++woX/OJdly5blbl+wYAHTp03l6A4dt/g5lDbX9urI+vUbGPju57lpn0/7gVYn38FhZ9zDNed0oHKlClSoUI5GO9Zi7ORvOeT0uxn35ffc+c8TAej9wBu0bdWEsa9cR9tWTfjpl6XkrF8PQLNjb+Gw7vfQ44b+3HvN39m1Ud1iOc9s8D7l9MR1rt2AgeH5wPA6lZrAq5KmAQ8Ae4f0o4B+ZpYDYGbJ3+mGAs+Z2fMFFWpmT5pZazNrvX3d7Yt4GsXj1K6n8+Ybr22UVpSWbcOGjTj++M5UrFiRxrvuSrNmezBn9uzc7a+9OpgTOp9IxYoVt2i9S5vuxx/Ise1a0PPG/vlun/ndL6xa8wd7N2nA4mWrWLXmd4Z+OAWA10dMouWeOwGw4NfldL36aQ7udjd9Hn0LgN9Wrs3dBvD9T4v5eMJsWjZvlOWzciVN1oOypDrAkcDTYTDuGuA0UncT9QU+MrMWwPHANokigYJGJz8FjlEp+K6THDTfeWsYzfZonvt6w4YNvP7aq5xyatf8sm7i+M5dGD3qIwAWLVrE7Nmz2HW33XK3Dx70inddpHD0IXtyVc+jOPmK/2PN2nW56bs0qEP58tGv0c71a9GscT1+mL8YgHc/nka71k0BOKLNHsz4dgEAdbbbNvfr+DXndGTA0M8A2K56FSpVrJC7z8Etd+Prb3+O5wRj4t0XqcVx8cjJwPNmlju/R9JoYANQPWm/FXle1wR+Cs97JqV/AFwgaZSZ5UiqndRavgW4GXgcuHCLnkUWnXVGNz4ZPYpFixaxe+NG3HzLbbz//rvMnjWTcirHzrvswsOP/TnzYswnH9OwYaONAivADddfy6CBL7N69Wp2b9yIs885l5tuuZWjO3TkvyM+YL999qJ8ufL8+657qVOnDgA/fP898+b9SNt2h8d6ziXZgDt70rZVU+puV4057/elb793uebsqFvi7SeiGSHjp37PZXcM5JD9duPqszuwLmc9GzYYl/97UO6A3k0Pvckz/+rBvVf/nUVLV/KPW18EoF3rptx+6QmYwZhJc7jizsEANN9tRx65sRsbbAPlVI77nhvBjBCUL+p2OFf2OIp6dWrw+eAbeH/MdC66/eVieHc2T9kJrZnL+pQ4SaOAu8zs/aS0y4A9gWZAXaA/MAAYDlQE7gTmhrRfgQ+BM82ssaQKwD1AJ2Ad8JSZPZqYEgcsBp4FfjWzawurW0mZEufyV1xT4lxqmUyJa7L3vvafgcMzPmaXfeqXiSlxWW8pm9kR+aQ9XMDuB+R53Szp+c0hbw5wZXgkl9k46eXZRa2ncy67ooE+byun4mtfOOdiU4a6hjNWlmaaOOdciectZedcTIS8+yIlD8rOudh490VqHpSdc7Hwgb70eFB2zsVD3lJOhwdl51xsPCin5rMvnHOuBPGWsnMuNj77IjUPys65WAgo5zE5JQ/KzrnYeEs5NQ/KzrnY+EBfah6UnXOx8ZZyaj77wjnnShBvKTvnYuEDfenxoOyci4kvSJQOD8rOuXj4ZdZp8aDsnIuNx+TUPCg752IR9Sl7WE7FZ18450oNSeUlfSHp7fC6tqQRkmaH/2sl7dtb0hxJMyV1TEpvJWlq2P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misclassifiedtotalpercent_misclassified
Infilteration63138809670.779799
Brute Force -XSS591150.513043
SQL Injection22430.511628
Bot671871430960.469524
Brute Force -Web1423050.465574
DoS attacks-Slowloris111554950.202912
Benign19602113484710.145365
DDoS attacks-LOIC-HTTP54982880960.019084
DDOS attack-LOIC-UDP58650.005780
DoS attacks-GoldenEye1207540.000048
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 63138 80967 0.779799\\n\",\n \"Brute Force -XSS 59 115 0.513043\\n\",\n \"SQL Injection 22 43 0.511628\\n\",\n \"Bot 67187 143096 0.469524\\n\",\n \"Brute Force -Web 142 305 0.465574\\n\",\n \"DoS attacks-Slowloris 1115 5495 0.202912\\n\",\n \"Benign 196021 1348471 0.145365\\n\",\n \"DDoS attacks-LOIC-HTTP 5498 288096 0.019084\\n\",\n \"DDOS attack-LOIC-UDP 5 865 0.005780\\n\",\n \"DoS attacks-GoldenEye 1 20754 0.000048\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pred_test_denoising_ae = predict(denoising_ae, X_test, y_test.label_is_attack)\\n\",\n \"\\n\",\n \"test_avg_pr_score = average_precision_score(pred_test_denoising_ae.y_true, pred_test_denoising_ae.rec_error)\\n\",\n \"print('Average Precision Score: {}\\\\n'.format(test_avg_pr_score)) \\n\",\n \"print_performance(y_test, pred_test_denoising_ae, threshold=threshold_denoising_ae)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We obtain similar results as observed on the validation set:\\n\",\n \"\\n\",\n \"|PR Score|Precision|Recall|F1 |Precision Attack|Recall Attack|\\n\",\n \"|--------|---------|------|----|----------------|-------------|\\n\",\n \"|0.93419 |0.88 |0.88 |0.88|0.86 |0.90 |\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Conclusion\\n\",\n \"\\n\",\n \"Summarizing our experiments using anomaly detection, we can conclude that the performance of the resulting model is not sufficient for real-world usage and is considerably worse than the performance of the machine learning models created in previous experiments. \\n\",\n \"Moreover, the predictions of this estimator are very sensitive to the chosen value of the decision boundary, which may only be reliably determined given sufficient malicious data. This circumstance mitigates the usefulness of this approach somewhat, as the biggest advantage of this method lies in the assumption that only benign data is necessary to create the estimator and collection of malicious data is not required or severly limited.\\n\",\n \"\\n\",\n \"Nevertheless, the demonstrated approach could be useful in situations where malicious training data is only available in low volumes or not available at all. If no malicious training data is available, choosing a decision boundary could be done by defining a sensible confidence interval given the distribution of benign samples and adjusting the boundary on the arrival of new data.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/05_anomaly_detection/models/denoising_autoencoder_model.h5", "content": "version https://git-lfs.github.com/spec/v1\noid sha256:13f9ca921d4d76f3a745450fa844e22c2d5716440efcc22c2170f3bc0f21f179\nsize 13411104\n" }, { "path": "notebooks/05_anomaly_detection/models/simple_autoencoder_model.h5", "content": "version https://git-lfs.github.com/spec/v1\noid sha256:18216e715acf520b92ba511d4a27f37b90377887540a1d2b1217d46b41d7d93a\nsize 70464\n" }, { "path": "notebooks/05_anomaly_detection/models/stacked_autoencoder_model.h5", "content": "version https://git-lfs.github.com/spec/v1\noid sha256:db4d61f4d8ee4e9d43db255afcac4c2443aea48268ea9ea867783460cdfa065d\nsize 204328\n" }, { "path": "notebooks/05_anomaly_detection/notebook_utils.py", "content": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import classification_report, average_precision_score, roc_auc_score, precision_recall_curve, \\\n roc_curve\nfrom IPython.display import display\nfrom ml_ids.visualization import plot_confusion_matrix\n\n\ndef predict(model, X, y):\n preds = model.predict(X, batch_size=8196)\n mse = np.mean(np.power(X - preds, 2), axis=1)\n\n return pd.DataFrame({'y_true': y, 'rec_error': mse})\n\n\ndef evaluate_pr_roc(pred):\n pr_auc = average_precision_score(pred.y_true, pred.rec_error)\n roc_auc = roc_auc_score(pred.y_true, pred.rec_error)\n return pr_auc, roc_auc\n\n\ndef plot_evaluation_curves(pred):\n fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 8))\n\n precisions, recalls, thresholds = precision_recall_curve(pred.y_true, pred.rec_error)\n fpr, tpr, _ = roc_curve(pred.y_true, pred.rec_error)\n pr_auc, roc_auc = evaluate_pr_roc(pred)\n\n # plot precision / recall curve\n ax1.plot(recalls, precisions, label='auc={}'.format(pr_auc))\n ax1.set_title('Precision / Recall Curve')\n ax1.set_xlabel('Recall')\n ax1.set_ylabel('Precision')\n ax1.legend(loc='lower right')\n\n # plot ROC curve\n ax2.plot(fpr, tpr, label='auc={}'.format(roc_auc))\n ax2.set_title('ROC Curve')\n ax2.set_ylabel('True Positive Rate')\n ax2.set_xlabel(\"False Positive Rate\")\n ax2.legend(loc='lower right')\n\n\ndef plot_pr_threshold_curves(pred, pr_plot_lim=[0, 1]):\n fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 8))\n\n precisions, recalls, thresholds = precision_recall_curve(pred.y_true, pred.rec_error)\n\n # plot precision / recall for different thresholds\n ax1.plot(thresholds, precisions[:-1], label=\"Precision\")\n ax1.plot(thresholds, recalls[:-1], label=\"Recall\")\n ax1.set_title('Precision / Recall of different thresholds')\n ax1.set_xlabel('Threshold')\n ax1.set_ylabel('Precision / Recall')\n ax1.legend(loc='lower right')\n\n # plot precision / recall for different thresholds\n ax2.plot(thresholds, precisions[:-1], label=\"Precision\")\n ax2.plot(thresholds, recalls[:-1], label=\"Recall\")\n ax2.set_title('Precision / Recall of different thresholds')\n ax2.set_xlabel('Threshold')\n ax2.set_ylabel('Precision / Recall')\n ax2.set_xlim(pr_plot_lim)\n ax2.legend(loc='lower right')\n\n\ndef best_precision_for_target_recall(pred, target_recall):\n precisions, recalls, thresholds = precision_recall_curve(pred.y_true, pred.rec_error)\n return thresholds[np.argmin(recalls >= target_recall)]\n\n\ndef get_misclassifications(y, pred_binary):\n misclassifications = y[y.label_is_attack != pred_binary]\n\n mc_df = pd.merge(pd.DataFrame({'misclassified': misclassifications.label.value_counts()}),\n pd.DataFrame({'total': y.label.value_counts()}),\n how='left', left_index=True, right_index=True)\n mc_df['percent_misclassified'] = mc_df.apply(lambda x: x[0] / x[1], axis=1)\n return mc_df.sort_values('percent_misclassified', ascending=False)\n\n\ndef print_performance(y, pred, threshold):\n pred_binary = (pred.rec_error >= threshold).astype('int')\n\n print('Classification Report:')\n print('======================')\n print(classification_report(pred.y_true, pred_binary))\n\n print('Confusion Matrix:')\n print('=================')\n plot_confusion_matrix(pred.y_true, pred_binary, np.array(['Benign', 'Attack']), size=(5, 5))\n plt.show()\n\n print('Misclassifications by attack category:')\n print('======================================')\n mc_df = get_misclassifications(y, pred_binary)\n display(mc_df)\n\n\ndef filter_benign(X, y):\n return X[y.label_is_attack == 0]\n" }, { "path": "notebooks/06_dl_classifier/dl-classifier.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Deep Learning Classifier\\n\",\n \"\\n\",\n \"### Achievement\\n\",\n \"Using a deep neural network in combination with hyperparameter search, we obtain a binary classifier able to distinguish benign from malicious network traffic with the following performance characteristics:\\n\",\n \"\\n\",\n \"|PR Score|Precision Positive|Recall Positive|False-Positives|False-Negatives|\\n\",\n \"|---|---|---|---|---|\\n\",\n \"|0.97816|0.967|0.954|8966|12629|\\n\",\n \"\\n\",\n \"\\n\",\n \"## 1. Introduction\\n\",\n \"\\n\",\n \"In this notebook, we will pursue a supervised learning approach, to create a binary classifier that is able to distinguish benign from malicious network traffic. \\n\",\n \"We will train multiple deep neural network models, using the network traffic data taken from the [CIC-IDS-2018 dataset](https://www.unb.ca/cic/datasets/ids-2018.html), and will evaluate their respective performance. \\n\",\n \"For this purpose, values of the target variable of the dataset are grouped into two classes, benign and attack, with the attack class subsuming all types of malicious network traffic. As the dataset is severly imbalanced, containing 83% of benign and only 27% of malicious samples, we will have to take this class imbalance into account during training.\\n\",\n \"\\n\",\n \"In the first part of the notebook, we train a rather simple deep network, using two different approaches.\\n\",\n \"The first approach does not take class imbalance into account, while the second approach uses the class weights to weight the losses of the under-represented samples more heavily during training. \\n\",\n \"Comparing the results of both training runs, we decide on using one of the two approaches going forward.\\n\",\n \"\\n\",\n \"In the second part of the notebook, we concern ourselves with finding the optimal model architecture and parameter configuration for the classifier, by employing hyperparameter optimization using the [Hyperopt](https://github.com/hyperopt/hyperopt) library.\\n\",\n \"\\n\",\n \"The deep learning models are trained using the [Keras](https://keras.io/) library, employing [Tensorflow 2](https://www.tensorflow.org/) as the auto-differentiation framework in the backend.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Dataset Preparation\\n\",\n \"\\n\",\n \"In this section we load the dataset and prepare the data for training. The preprocessed dataset from CIC-IDS-2018 can be found at [Google Drive](https://drive.google.com/open?id=1HrTPh0YRSZ4T9DLa_c47lubheKUcPl0r).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dataset_base_path = r'/home/glados/Development/Projects/ids-2018/processed'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Using TensorFlow backend.\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"import tensorflow as tf\\n\",\n \"import gc\\n\",\n \"import shap\\n\",\n \"from ml_ids.data.dataset import load_dataset\\n\",\n \"from ml_ids.data.metadata import FEATURES_NO_VARIANCE\\n\",\n \"from ml_ids.visualization import plot_hist, print_binary_performance, plot_pr_curve\\n\",\n \"from ml_ids.keras.metrics import AveragePrecisionScoreMetric\\n\",\n \"from ml_ids.keras.evaluation import evaluate_model\\n\",\n \"from ml_ids.keras.prediction import predict, predict_proba\\n\",\n \"from ml_ids.tf_utils import enable_gpu_memory_growth\\n\",\n \"from notebook_utils import transform_data\\n\",\n \"from sklearn.preprocessing import StandardScaler, MinMaxScaler\\n\",\n \"from tensorflow import keras\\n\",\n \"from tensorflow.keras import layers, models, optimizers, callbacks, regularizers, metrics\\n\",\n \"from collections import Counter\\n\",\n \"from hyperopt import fmin, hp, tpe, atpe, Trials, STATUS_OK\\n\",\n \"from hyperopt.plotting import main_plot_history, main_plot_vars\\n\",\n \"import uuid\\n\",\n \"\\n\",\n \"K = keras.backend\\n\",\n \"\\n\",\n \"rand_state = 42\\n\",\n \"tf.random.set_seed(rand_state)\\n\",\n \"np.random.seed(rand_state)\\n\",\n \"\\n\",\n \"enable_gpu_memory_growth()\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Data Loading & Preparation\\n\",\n \"\\n\",\n \"In this part we load the dataset, specifing the features to omit. These encompass all features having no variance and features which should not be taken into account, in order to obtain a better generalization of the model.\\n\",\n \"\\n\",\n \"We use `median` imputation as the strategy to impute missing values, as the distributions of all features, for which imputation is necessary, are highly skewed. \\n\",\n \"Futhermore, we scale the data using a `StandardScaler` and upsample the malicious network traffic to obtain `100000` samples per attack category. We perform upsampling by using the technique of Synthetic Minority Over-sampling provided by the [SMOTE](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.over_sampling.SMOTE.html) library.\\n\",\n \"\\n\",\n \"Furthermore, the dataset is splitted into:\\n\",\n \"* training set,\\n\",\n \"* validation set and\\n\",\n \"* test set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Samples:\\n\",\n \"========\\n\",\n \"Training: (12986354, 68)\\n\",\n \"Val: (1623294, 68)\\n\",\n \"Test: (1623295, 68)\\n\",\n \"\\n\",\n \"Training labels:\\n\",\n \"================\\n\",\n \"Benign 10787766\\n\",\n \"DDOS attack-HOIC 548809\\n\",\n \"DDoS attacks-LOIC-HTTP 460953\\n\",\n \"DoS attacks-Hulk 369530\\n\",\n \"Bot 228953\\n\",\n \"FTP-BruteForce 154688\\n\",\n \"SSH-Bruteforce 150071\\n\",\n \"Infilteration 129547\\n\",\n \"DoS attacks-SlowHTTPTest 111912\\n\",\n \"DoS attacks-GoldenEye 33206\\n\",\n \"DoS attacks-Slowloris 8792\\n\",\n \"DDOS attack-LOIC-UDP 1384\\n\",\n \"Brute Force -Web 489\\n\",\n \"Brute Force -XSS 184\\n\",\n \"SQL Injection 70\\n\",\n \"Name: label, dtype: int64\\n\",\n \"\\n\",\n \"Validation labels:\\n\",\n \"==================\\n\",\n \"Benign 1348471\\n\",\n \"DDOS attack-HOIC 68601\\n\",\n \"DDoS attacks-LOIC-HTTP 57619\\n\",\n \"DoS attacks-Hulk 46191\\n\",\n \"Bot 28619\\n\",\n \"FTP-BruteForce 19336\\n\",\n \"SSH-Bruteforce 18759\\n\",\n \"Infilteration 16193\\n\",\n \"DoS attacks-SlowHTTPTest 13989\\n\",\n \"DoS attacks-GoldenEye 4151\\n\",\n \"DoS attacks-Slowloris 1099\\n\",\n \"DDOS attack-LOIC-UDP 173\\n\",\n \"Brute Force -Web 61\\n\",\n \"Brute Force -XSS 23\\n\",\n \"SQL Injection 9\\n\",\n \"Name: label, dtype: int64\\n\",\n \"\\n\",\n \"Test labels:\\n\",\n \"============\\n\",\n \"Benign 1348471\\n\",\n \"DDOS attack-HOIC 68602\\n\",\n \"DDoS attacks-LOIC-HTTP 57619\\n\",\n \"DoS attacks-Hulk 46191\\n\",\n \"Bot 28619\\n\",\n \"FTP-BruteForce 19336\\n\",\n \"SSH-Bruteforce 18759\\n\",\n \"Infilteration 16194\\n\",\n \"DoS attacks-SlowHTTPTest 13989\\n\",\n \"DoS attacks-GoldenEye 4151\\n\",\n \"DoS attacks-Slowloris 1099\\n\",\n \"DDOS attack-LOIC-UDP 173\\n\",\n \"Brute Force -Web 61\\n\",\n \"Brute Force -XSS 23\\n\",\n \"SQL Injection 8\\n\",\n \"Name: label, dtype: int64\\n\",\n \"Samples:\\n\",\n \"========\\n\",\n \"Training: (12986354, 68)\\n\",\n \"Val: (1623294, 68)\\n\",\n \"Test: (1623295, 68)\\n\",\n \"\\n\",\n \"Missing values:\\n\",\n \"===============\\n\",\n \"Training: 0\\n\",\n \"Val: 0\\n\",\n \"Test: 0\\n\",\n \"\\n\",\n \"Scaling:\\n\",\n \"========\\n\",\n \"Training: min=-2.338481849667957, max=3467.6006434451638\\n\",\n \"Val: min=-2.338481849667957, max=1919.8280533361315\\n\",\n \"Test: min=-2.338481849667957, max=2698.612041157329\\n\",\n \"Samples:\\n\",\n \"========\\n\",\n \"Training: (13542229, 68)\\n\",\n \"\\n\",\n \"Training labels:\\n\",\n \"================\\n\",\n \"Counter({0: 10787766, 8: 548809, 10: 460953, 5: 369530, 1: 228953, 11: 154688, 14: 150071, 12: 129547, 6: 111912, 2: 100000, 4: 100000, 7: 100000, 9: 100000, 13: 100000, 3: 100000})\\n\"\n ]\n }\n ],\n \"source\": [\n \"dataset = load_dataset(dataset_base_path,\\n\",\n \" omit_cols=FEATURES_NO_VARIANCE + ['timestamp', 'dst_port', 'protocol'],\\n\",\n \" preserve_neg_value_cols=['init_fwd_win_byts', 'init_bwd_win_byts'])\\n\",\n \"\\n\",\n \"X_train, y_train, X_val, y_val, X_test, y_test, column_names = transform_data(dataset=dataset,\\n\",\n \" imputer_strategy='median',\\n\",\n \" scaler=StandardScaler,\\n\",\n \" attack_samples=100000,\\n\",\n \" random_state=rand_state)\\n\",\n \"\\n\",\n \"del dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Target Grouping & Class Weight Calculation\\n\",\n \"\\n\",\n \"In this step we create the binary target variable by grouping all malicious network traffic into a single attack category represented by the positive class `1`, whereas benign traffic is represented as the negative class `0`.\\n\",\n \"\\n\",\n \"Furthermore, we calculate the weights for each class, given by the proportion of samples of this class in the training set. \\n\",\n \"For the majority class, the class weight is `1`. \\n\",\n \"For the minority class, the class weight is calculated with the following formula:\\n\",\n \"\\\\begin{align}\\n\",\n \"\\\\frac{(sum\\\\_majority\\\\_class)}{(sum\\\\_minority\\\\_class)} \\\\\\\\\\n\",\n \"\\\\end{align}\\n\",\n \"In our case this translates to:\\n\",\n \"\\\\begin{align}\\n\",\n \"\\\\frac{(sum\\\\_negative\\\\_class)}{(sum\\\\_positive\\\\_class)} \\\\\\\\\\n\",\n \"\\\\end{align}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y_train_is_attack = (y_train != 0).astype('int')\\n\",\n \"\\n\",\n \"minority_class_weight = len(y_train_is_attack[y_train_is_attack == 0]) / len(y_train_is_attack[y_train_is_attack == 1])\\n\",\n \"\\n\",\n \"class_weights = { \\n\",\n \" 0: 1, \\n\",\n \" 1: minority_class_weight\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Static Model Training\\n\",\n \"\\n\",\n \"In this section, we train a static model, comprised of `3` layers, each containing `400` units, using the `Adam` optimizer. \\n\",\n \"We execute two training runs. In run one we ignore the class imbalance, whereas in run two we use the class weights to take the class imblance into account. \\n\",\n \"Afterwards we compare the results and select one approach to use for hyperparameter optimization.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def create_model(input_dims, \\n\",\n \" nr_layers, \\n\",\n \" nr_units, \\n\",\n \" activation, \\n\",\n \" kerner_initializer,\\n\",\n \" optimizer,\\n\",\n \" dropout_layer=None):\\n\",\n \" model = models.Sequential()\\n\",\n \" model.add(layers.Input(shape=[input_dims]))\\n\",\n \" \\n\",\n \" for l in range(nr_layers):\\n\",\n \" model.add(layers.Dense(nr_units, activation=activation, kernel_initializer=kerner_initializer))\\n\",\n \" \\n\",\n \" if dropout_layer:\\n\",\n \" model.add(dropout_layer)\\n\",\n \" \\n\",\n \" model.add(layers.Dense(1, activation='sigmoid'))\\n\",\n \" \\n\",\n \" model.compile(optimizer=optimizer, \\n\",\n \" loss='binary_crossentropy', \\n\",\n \" metrics=[metrics.AUC(curve='PR'),\\n\",\n \" metrics.Precision(), \\n\",\n \" metrics.Recall()])\\n\",\n \" return model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def train_static_model(X_train, y_train, X_val, y_val, model_path, class_weights=None): \\n\",\n \" input_dims = X_train.shape[1]\\n\",\n \" epochs = 20\\n\",\n \" batch_size = 4096\\n\",\n \" \\n\",\n \" K.clear_session()\\n\",\n \" gc.collect()\\n\",\n \"\\n\",\n \" model = create_model(input_dims=input_dims,\\n\",\n \" nr_layers=3,\\n\",\n \" nr_units=400,\\n\",\n \" activation='selu',\\n\",\n \" kerner_initializer='lecun_normal',\\n\",\n \" optimizer=optimizers.Adam())\\n\",\n \"\\n\",\n \" print(model.summary())\\n\",\n \" \\n\",\n \" mc = callbacks.ModelCheckpoint(filepath=model_path, save_best_only=True)\\n\",\n \"\\n\",\n \" lr_scheduler = callbacks.ReduceLROnPlateau(factor=0.2, patience=3)\\n\",\n \"\\n\",\n \" hist = model.fit(x=X_train, \\n\",\n \" y=y_train,\\n\",\n \" validation_data=(X_val, y_val),\\n\",\n \" batch_size=batch_size,\\n\",\n \" epochs=epochs,\\n\",\n \" class_weight=class_weights,\\n\",\n \" callbacks=[\\n\",\n \" lr_scheduler,\\n\",\n \" mc\\n\",\n \" ])\\n\",\n \" \\n\",\n \" return model, hist\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.1 Static Model without class weights\\n\",\n \"\\n\",\n \"In this step, we train the static model withouth using class weights.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"model_no_class_weights, hist = train_static_model(X_train, \\n\",\n \" y_train_is_attack, \\n\",\n \" X_val, \\n\",\n \" y_val.label_is_attack.values,\\n\",\n \" 'models/model_no_class_weights.h5')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[Output omitted for brevity]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Validation\\n\",\n \"\\n\",\n \"Inspecting the metrics and learning curves, we observe that the model does not overfit the training set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 269,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Evaluation:\\n\",\n \"===========\\n\",\n \" Loss / PR AUC / Precision / Recall\\n\",\n \"Train: [0.04862239808849553, 0.98295105, 0.99410915, 0.94906664]\\n\",\n \"Val: [0.048875621330395747, 0.97678155, 0.9924735, 0.93851316]\\n\"\n ]\n }\n ],\n \"source\": [\n \"model_no_class_weights = models.load_model('models/model_no_class_weights.h5')\\n\",\n \"evaluate_model(model_no_class_weights, \\n\",\n \" X_train, \\n\",\n \" y_train_is_attack,\\n\",\n \" X_val, y_val.label_is_attack.values, \\n\",\n \" metric_title='Loss / PR AUC / Precision / Recall')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 270,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fix, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(20, 8))\\n\",\n \"\\n\",\n \"plot_hist(hist, ax=ax1)\\n\",\n \"plot_hist(hist, metrics=['auc', 'val_auc'], ax=ax2)\\n\",\n \"plot_hist(hist, metrics=['precision', 'val_precision'], ax=ax3)\\n\",\n \"plot_hist(hist, metrics=['recall', 'val_recall'], ax=ax4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Performance\\n\",\n \"\\n\",\n \"The performance of the model is quite compelling with a PR score of `0.97718`. \\n\",\n \"Looking at the classification report we notice that the precision of the positive class is quite high (`0.992`), while having a lower recall (`0.939`). This effect is most likely caused by the overwhelming amount of samples from the negative class in the training set. \\n\",\n \"Consequently, the confusion matrix reveals a low amount of false-positives (`1956` samples) but a high amount of false-negatives (`16898` samples). \\n\",\n \"The misclassification statistic shows that the majority of false-negative results originate from the attack category of `Infiltration`, with a misclassification rate of `98%`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 272,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Average PR Score 0.9771876391440746\\n\"\n ]\n }\n ],\n \"source\": [\n \"pred_val_score = predict_proba(model_no_class_weights, X_val)\\n\",\n \"plot_pr_curve(y_val.label_is_attack.values, pred_val_score)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 289,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.988 0.999 0.993 1348471\\n\",\n \" 1 0.992 0.939 0.965 274823\\n\",\n \"\\n\",\n \" accuracy 0.988 1623294\\n\",\n \" macro avg 0.990 0.969 0.979 1623294\\n\",\n \"weighted avg 0.988 0.988 0.988 1623294\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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misclassifiedtotalpercent_misclassified
Infilteration15884161930.980918
SQL Injection290.222222
Brute Force -Web3610.049180
Brute Force -XSS1230.043478
Bot905286190.031622
DoS attacks-Slowloris610990.005460
DDoS attacks-LOIC-HTTP87576190.001510
Benign195613484710.001451
DDOS attack-HOIC8686010.000117
SSH-Bruteforce2187590.000107
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 15884 16193 0.980918\\n\",\n \"SQL Injection 2 9 0.222222\\n\",\n \"Brute Force -Web 3 61 0.049180\\n\",\n \"Brute Force -XSS 1 23 0.043478\\n\",\n \"Bot 905 28619 0.031622\\n\",\n \"DoS attacks-Slowloris 6 1099 0.005460\\n\",\n \"DDoS attacks-LOIC-HTTP 87 57619 0.001510\\n\",\n \"Benign 1956 1348471 0.001451\\n\",\n \"DDOS attack-HOIC 8 68601 0.000117\\n\",\n \"SSH-Bruteforce 2 18759 0.000107\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pred_val = predict(model_no_class_weights, X_val)\\n\",\n \"print_binary_performance(y_val, y_val.label_is_attack, pred_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.2 Static Model using class weights\\n\",\n \"\\n\",\n \"In this step, we train the static model using class weights to take the class imbalance into account.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"model_class_weights, hist = train_static_model(X_train, \\n\",\n \" y_train_is_attack, \\n\",\n \" X_val, \\n\",\n \" y_val.label_is_attack.values,\\n\",\n \" 'models/model_class_weight.h5',\\n\",\n \" class_weights)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[Output omitted for brevity]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Validation\\n\",\n \"\\n\",\n \"Inspecting the metrics and learning curves, we observe that the model does not significantly overfit the training set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 169,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Evaluation:\\n\",\n \"===========\\n\",\n \" Loss / PR AUC / Precision / Recall\\n\",\n \"Train: [0.06124674869864, 0.9829123, 0.97260857, 0.96141064]\\n\",\n \"Val: [0.06319707397520061, 0.9765367, 0.96547586, 0.953974]\\n\"\n ]\n }\n ],\n \"source\": [\n \"model_class_weights = models.load_model('models/model_class_weight.h5')\\n\",\n \"evaluate_model(model_class_weights, \\n\",\n \" X_train, \\n\",\n \" y_train_is_attack, \\n\",\n \" X_val, \\n\",\n \" y_val.label_is_attack.values,\\n\",\n \" metric_title='Loss / PR AUC / Precision / Recall')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 168,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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AJWEuDP2R/wNIQCOy17g80s99v92o33mtIT4onPSnOvRIb3+/ZlpG077bG9209s1e9P0BNrZ+ddQ3U1DWws/H97m1+dtYGl8HtC0t28HVJBQO7pfGXa0Zz1pDuBCd1EokoJYfCrXylK0idO7b5/ca40UOlc9s3LhERiSmXX345d9xxx+4vzK+88grvv/8+d955JxkZGZSVlXHCCSdw4YUXtupD5+OPPw7AokWLWLZsGWeddRYrVqxg6tSp3H777Vx11VXU1dXh9/t599136dWrF++88w4AFRUV4e/oEcAYcw7wGG5W2KestQ802d8FeAYYAPiA6621i4P77sRNCmKBRcB11lqfMSYT+AfQF1gHXGqt3d4uHQqzcN7rSUlJvPHGG/ucdyBXXXUV99xzD5MmTcLn8xEIBMLWN+n4GvwBttXUsW1nHeXVdZTvrKO8upZtO+soq67j25Ja3tg0n4B1yZqAdYmPgIWA3bNug+uN7/3BfYHgsf7gue4V0pa1BALu3PomSZyGgA17f41x/1AllK7H6zHEeQxejye4NCHbgu+9br/XQJzHQ5zHQ1L8vseFthG6vd4foNLXQJWvgSpfPZsra6ny1VPlcwmXg0nwevZJGO39Pp6MpDjSEuP4Zn09y6etZmedn5raBrcMJnxq6hp2bw9N9NT5W/7vQWKch9TEOLqmJfDgJcdy8agc4ryeg58o0k6UHAq34iK3zNtPcgjc42QrP4TaKkhMb5+4RESk7Rxk5ENbGDlyJFu2bGHDhg1s3bqVLl260LNnT+68806mT5+Ox+OhtLSUzZs306NHjxa3O2PGDG699VYABg8eTJ8+fVixYgXjxo3jN7/5DSUlJVx88cUcddRRDBs2jJ/+9Kf87Gc/44ILLuCUU05pq+7GLGOMF3gcOBMoAWYbY96y1n4Tcth9wAJr7SRjzODg8acbY3KA24Ah1tpdxphXgMuBZ4F7gE+stQ8YY+4Jrv/ssIKNwH0O4b3XrbXcd999+5y3P1VVVZSWljJp0iTAJZcktvkDlh3BZE9ZdTDps7M2+L52nwTQ9pr6ZtvxGMhMTcD4/ZTW7sBrjEuseAweE3x52PM+uM8YlxSJDznOncNe6/u0ZSA+zkOC10O815AQ5yHe616N2xr3h+6L95p9tiXEmeBy3za8HsO0adMoKCho3z+YZjT4A1TXusRRZTBhVO1roKq2PphM2rO9MblU5WugrGzn7m3VtQ17N/rNMgBSE7ykJMa5ZUIcqYleOifHk9M5ya03s3+vZUIcKYne3cuUeK8SQRL1lBwKt5LZrtZQ1lH7PyZnNGBhwwLopw/SIiJyaL773e/y6quvsmnTJi6//HL+/ve/s3XrVubOnUt8fDx9+/bF5/O1qk1rm/9N85VXXsnxxx/PO++8w9lnn81TTz3Faaedxty5c3n33Xe59957Oeuss7j//vvD0bUjyVhglbV2DYAx5mVgIhCaHBoC/A7AWrvMGNPXGNM9uC8OSDbG1AMpwIbg9olAQfD9c0Ahh5sciqBw3ev7Oy8uLm6vEUGNbe3v74N0PL56PyXba1i/rYYNO3yUB5M9ZTvr2FbtEkDl1XVsr6mjuQE3xkDn5Hiy0hLJTE0gv0c6Wanufde0BDJTE8lKSyArNYGstEQ6J8fj8RgKCwujIpESi+K8HjqnJNA55dBngPYHLNW1Lkk056tZnHnqKSTFefF49JiXHHmUHAq3ktluCnvPATLDvYKFqDfMU3JIREQO2eWXX85NN91EWVkZ06ZN45VXXqFbt27Ex8fz2Wef8e2337a6zfHjx/P3v/+d0047jRUrVrB+/Xry8/NZs2YN/fv357bbbmPNmjV8/fXXDB48mMzMTK6++mrS0tJ49tlnw9/J2JcDFIeslwDHNzlmIXAxMMMYMxboA+Raa+caYx4G1gO7gA+ttR8Gz+lurd0IYK3daIzp1padaGvhutcrKiqaPa979+5s2bKF8vJy0tLSePvttznnnHPIyMggNzeXf/3rX1x00UXU1tbi9/tJSUlpy+7KIbDWUlZdx/ptNazftpP15btYv62G4m0uIbSpct/kYUZSHF3TXFKnX9dUjuub6ZI7qQlkpiXSNZjoyUxNoEtKvEZ+xCCvx9ApOZ5OyfGsTDSkJOjrsRy5dPeHk68CtiyFIRcd+LjULOjcR3WHRETksBxzzDFUVVWRk5NDz549ueqqq/jOd77Dcccdx4gRIxg8eHCr27z55puZMmUKw4YNIy4ujmeffZbExET+8Y9/8MILLxAfH0+PHj24//77mT17NnfddRcej4f4+HieeOKJNuhlzGvu19NNxy08ADxmjFmAqys0H2gI1iKaCPQDdgD/NMZcba19ocUXN2YyMBkgOzubwsLCvfZ36tSJqqqqljbXZnr37k1FRQU9evQgLS2NiRMncumllzJq1CiGDRvGoEGDqK6u3h1raMx+v3/3+v7O8/l83H333YwZM4a+ffsyYMAAamtrqaqq4oknnuCOO+7g5z//OfHx8Tz33HP069dvr/h8Pt8+P7u2VF1d3a7Xaw8t6VOd31K2y7J1V4CtNZatNQG27NqzbFqCpkuiITvFMDDdw7hu8WSneOiWbMhKNqQnuNo2wZaDr2q3Wg9sh7rtsBH3ast+dTSx2CeIzX6pTx1HNPRLyaFwKp0LWMgbc/Bjc0ZBiZJDIiJyeBYt2jN7VNeuXZk5c2azx1VXV++3jb59+7J48WKqqqpISkpqdgTQvffey7333rvXtrPPPpuzzz770AKXRiVAXsh6LnseDQPAWlsJXAdgXMXltcHX2cBaa+3W4L7XgROBF4DNxpiewVFDPYEtzV3cWvsk8CRAfn6+bfr4y9KlS0lPj476iEuWLNn9Pj09naKiomaPa3qvV1VV7e7Dgc67++67ufvuu/fZPnLkSKZNm3bA2JKSkhg5cuQBjwmnWHxUqbCwkAkTJlC+s45vy/eM+Nn9Kt939E9yvJfemSkMzkvhrMwUemcm0ycrlbzMFHK7JLf5TFUtEat/VrHWJ4jNfqlPHUc09EvJoXAqng0EZyM7mJzRsOQNqN4KadltHpqIiIhEpdnAUcaYfkAprqD0laEHGGM6AzXW2jrczGTTrbWVxpj1wAnGmBTcY2WnA3OCp70FXIsbdXQt8GZ7dEbanj9gqfV3zFpIgYClrLqWkh272LBjF6Xbd1EaXC4vqWHbpx/sMwNVj4wkememcNLArvTOTKF3VrJbZqbSNS1BU4CLiISJkkPhVFIE2YNdQeqDCa07NEi/dRURkba3aNEirrnmmr22JSYm8tVXX0UoIrHWNhhjbgE+wM0Q/Yy1dokxZkpw/1TgaOB5Y4wfV6j6huC+r4wxrwLzgAbc42ZPBpt+AHjFGHMDribR99qxWxEXeq8HAgE8Hk+HvdcDAcvSTZXMXF3OrDXlfLV2G1W+BlKnvU92euKeV5pbdk1L3Gt7VmoiCXHtUyun3h9gU4WPkpCkT+mOmt3vN1T4qGvYe+rvjKQ4enVOpmuyh7OG96Z3ZjK9s1LonZlCbpeUqBj9IyJyJFByKFwCAVeMesjElh3fczgYj3sUTckhERFpB8OGDWPBggWRDkOasNa+C7zbZNvUkPczgWanQbXW/gL4RTPby3EjiY5Iofd66GNlHYG1llVbqvlydTkzV5fz1dry3dOl98lK4fxhPanfsYmMbjmUVdextcrH8k1VzKgqo9LX0GybXVLi904cNUkgNe7LTEk44CxNNXUNlG7fRUmTUT8bdrj3myt9+8z0lZ2eSE7nZI7J6cTZx/Qgp0syOZ2Tdy/Tk+KBxkcqhoTnhygiIq2m5FC4lK90Balzx7bs+MQ0N8qodF7bxiUiIm3GWqtHGg5CU4F3fLrPD+xw73FrLevKa/hydVlwdNA2yqprAcjpnMzpR3dnXP8sxg3IolfnZAAKC7dRUHDMPm356v2UVdcGk0a1e17VPrZWue3z1+9gS5UPX31gn/O9HkNWasJeo46qa+t3J4Eak1SN4jyGnp2TyOmczIkDupLTJZnckMRPj05JGvkjItJBKDkULsXB4oZ5LUwOgStKvfw9sBb0oUtEpEPx+/2Ul5eTlZWlL877Ya2lvLycpKSkSIcihygpKUn3+QEc6j1evK2GmWvcyKCZq8t3F1runpHIyQNdImhc/67kZSa36ueeFO8lt4t7HOtgce+s8wcTRrV7J5Kqatla7bYv31RFWmIcOV2SGZ7beXfSJ7dLMr06J9MtPQnvAUYaiYhIx6HkULiUFLlaQ1nNjvpuXq9RMP8F2PEtdOnbZqGJiEj47dy5k6qqKrZu3RrpUMLG5/OFPZGTlJREbm5uWNuU9pObm0tJSUmHvs/b4r4O1ZJ7fFOFj5lryvhyVTkz15RTsn0XAFmpCZwwIGv3yKD+XVPbJQlnjCEtMY60xDj6dU1t8+uJiEj0U3IoXIpnQ+4Y8LSi4F9OsCh16Twlh0REOhhrLf369Yt0GGFVWFjYrtNxS/SLj4/v8Pd5JO7rrVW1zFpTzpfBItJry3YC0Ck5nhP6Z3Ljyf04cWBXjuqWphFZIiISFZQcCgdfBWxdBsdMat153Y4Bb6IrSj304raJTUREREQOiT9gqfcHqPMHqG8IUO8PWfcHqGtoXFq27ayjaK1LCK3cUg1AemIcY/tlctXxvRk3IIuje2QcsOCziIhIpCg5FA4lcwALeWNad15cAvQYBhvmt0lYIiIiIkei6toGZqws44tVZSxf5+Ol4jl7EjvBhE7TRE99g92dBGrc1nTmrYNJSfByXN9MLhmdy7j+WRzTK4M4b/tMIy8iInI4lBwKh5I5gIGc41p/bs5oV3co4AePZnMQERERORTrynby6bItfLZ8C1+t2UadP+Dq6ngDVNga4uMM8V4P8V4PKQlxxHsNCXFuPSG4vfGYxvXG/aHH7l7fa7+HtMQ4BvdMJ17JIBER6YCUHAqHkiLodjQkZbT+3JxRUPQX2Locug8Jf2wiIiIiMaiuIcCcddv4dNkWPl22hTXBuj4DslO59sQ+nDq4G2P6ZvLF59MpKBgf4WhFRESim5JDhysQgJLZMOSiQzs/Z7Rbls5VckhERETkALZW1fLZ8i18tmwLn68so7q2gQSvh+P7Z3LNuD6cNrgbfbI0+5aIiEhrKTl0uMpXuoLUeWMP7fzMAZCYARvmwahrwhubiIiISAcWCFgWb6hwj4st28LCkgoAumck8p3hPTk1vxsnDexKaqI+0oqIiBwO/U96uIqL3DL3EJNDHg/0GulGDomIiIgc4Vwx6a3B+kFb2VpVizEwIq8zPzlzEKcO7sYxvTI0BbyIiEgYKTl0uEqKIKkzZA089DZyRsGXf4J6H8QnhS82ERERkQ5gbdlOPlm6mc+Wb6Fo7Tbq/Zb0pDjGD8rm9MHdmDAom6y0xEiHKSIiErOUHDpcxbMh9zg3AuhQ5YyGQANsWgR5Y8IXm4iIiEgUqmsIULR22+7ZxdYGi0kf1S2N60/qx6mDuzG6TxfN/CUiItJOlBw6HL4K2LoMjpl0eO30GuWWG+YpOSQiIiIx7fcfreDpGWtdMek4D+P6Z/GDE/ty2uBu5GWmRDo8ERGRI5KSQ4ejZA5gDz+hk9EL0nqo7pCIiIjEtHe+3shjn6zkjKO7c/mYPE4cmEVKgj6OioiIRJr+Nz4cJbMBAznHHV47xri6Q6XzwhKWiIiISLQp3lbDPa9/zYi8zjxx9Sg9MiYiIhJFWvS/sjHmHGPMcmPMKmPMPc3sv8oY83Xw9aUxZnhLz+3Qioug29GQlHH4beWMgvKVsGvH4bclIiIiEkXq/QFufWk+WPjTFSOVGBIREYkyB/2f2RjjBR4HzgWGAFcYY4Y0OWwtMMFaeyzw38CTrTi3YwoE3GNluWGqEdRYd2jjgvC0JyIiIhIlHvlwBQuKd/Dbi4eprpCIiEgUasmvbcYCq6y1a6y1dcDLwMTQA6y1X1prtwdXZwG5LT23wypbAbUVkDc2PO31GumWerRMREREYsj0FVuZOm01V4zN4zvDe0U6HBEREWlGS2oO5QDFIeslwPEHOP4G4L3WnmuMmQxMBsjOzqawsLAFoUVOj40fMxgo2gA1FYUHPb66uvqgfRqb3JOdCz9giYIbrvkAACAASURBVH9UWGJsay3pU0cUi/2KxT5BbPZLfeo4YrFfsdgniawtVT5+/MoCjuqWxv0XHBPpcERERGQ/WpIcMs1ss80eaMypuOTQya0911r7JMHH0fLz821BQUELQougt16DpM6MPfdK8Bx8AFZhYSEH7VP5yaR8++XBj4sSLepTBxSL/YrFPkFs9kt96jhisV+x2CeJnEDA8pNXFlLla+DvN55AcoI30iGJiIjIfrQkOVQC5IWs5wIbmh5kjDkWeAo411pb3ppzO6Ti2a7eUAsSQy3WaxQs+idUbYL0HuFrV0RERKSd/WX6Gj5fWcZvJw0jv0d6pMMRERE58vgqYdPXsGGBq2+8Yf81jluSHJoNHGWM6QeUApcDV4YeYIzpDbwOXGOtXdGaczskXwVsXQZDLw5vuzmj3bJ0Hgw+L7xti4iIiLSTeeu38/CHyzl/WE+uGJt38BNERERCWQv+egjUg7/OvffXkejbCg11EJcQ6Qijj68SNi7ckwTauADKV+3Zn94Leo0A5jZ7+kGTQ9baBmPMLcAHgBd4xlq7xBgzJbh/KnA/kAX82RgD0GCtPW5/5x5Gd6NDyRzAhm+mskY9hoHxQulcJYdERESkQ6rYVc9tL82nR0YSv714GMHPhiIi0t5sABpqQ5IsDSHJlsb3TffVQ6AhZHvT9ZDj9jqmfncCZ/c+f91eiR3XTpNt+xzfeGx9s10aBzDrJvekTac86JwXsuy9Zz0xrV1/1O3OVwEbvw4mgua7ZNC21Xv2Z+RAzxFw7GVu2WsEpHVz+658udkmWzJyCGvtu8C7TbZNDXl/I3BjS8/t8EpmA2bPSJ9wSUiBbkNgg2YsExERkY7HWst9ry9iY4WPf04ZR6fk+EiHJCJyZLEWVrwP0x6kYMM8mNYO1zReiEsETzx448GbELJsfB9cj0+GpE57H7Pf8+KatJHA8mXLyO+VATuKoWK9e+rmm7f2TSYldwkmjXo3n0RKyYSO8ssLX4UbERT6aNheiaBcl/wZfoVb9hwBadmtvkyLkkPSRHGRS+IkZYS/7ZxR8M2b7i91R7lZRURERICXiop5Z9FGfnbOYEb17hLpcEREjhzWwvJ3Ydr/uERCl76s63MpfQfku8RKYwLGE7cn2eKJC9kXF3JM0/W4ZtoIWW/H760bKwvJbzp5RiAA1Zuhohh2rA8ui92yfDWsKYS66r3PiU858Mij9B7gicBECrt27Pto2LY1e/Y3JoJGXAE9R0LP4YeUCGqOkkOtFQi4x8qGTmqb9nNGwbzn3A2QNaBtriEiIiISZss3VfGrfy/hlKO68sPx/SMdjojIkSEQgOXvuKTQpkXQpR9M/DMceynrPv+CvuMLIh1h2/N4IKOne+WN3Xe/tbBr+95Jo8aRRzuK3WNZNeVN2oyDlCyXAPN4g6849zJN1kP3G+++2/Y5p8m5xsOQ5bPh6zv3TgR1ynPJnxFXukRQrxGQ2rXNfoxKDrVW2QqorQh/vaFGoUWplRwSERGRDmBXnZ9bXpxHelIcj1w6HI9Ho59FRNpUIADL/g3THoTNiyFzAFw0FYZ9z436kT2McY+RpWS6ZEtz6nZCRcneSaOacgj4wfpdvaRAg1sPhKzb4Lq/Hup9IdsDIec0uD+vvc5p2KudjPgu0P8EGHHVnkfD2jAR1BzdNa1VUuSWuc1kJMMh+2iIS3Z1h479XttcQ0RERCSM/uvtb1i5pZrnrx9Lt/SkSIcjIhK7AgFY+qZLCm35BrIGwqQnYeglSgodjoRUyM53rwiYVVhIQdPH5dqZ7p7WKi6CpM7uL2Fb8Ma5bGZp89PLiYiIiESTt7/ewEtF65kyYQDjB4Wn7oGIiDQR8MM3/4JpD8HWpdB1EFz8FAy9ODK1cSTmeCIdQIdTMts9UuZpwx9dzig3LZ2/+en7REREJHYYY84xxiw3xqwyxtzTzP4uxpg3jDFfG2OKjDFDg9vzjTELQl6Vxpg7gvt+aYwpDdl3XlvEXrythntfW8SIvM785KxBbXEJEZEjW8APi16FP4+DV693jytd8jTcPMs9aaLEkISJRg61xq4dsHWZG7LXlnJGw6w/w5al0PPYtr2WiIiIRIwxxgs8DpwJlACzjTFvWWu/CTnsPmCBtXaSMWZw8PjTrbXLgREh7ZQCb4Sc93tr7cNtFXu9P8CtL80HA3+6YiTxXv3OUUQkbAJ+WPwaTH/I1b3NPhq++wwMuUgJIWkTSg61Rukct2yrYtSNeo10yw3zlBwSERGJbWOBVdbaNQDGmJeBiUBocmgI8DsAa+0yY0xfY0x3a+3mkGNOB1Zba79tp7h55MMVLCjeweNXjiIvM6W9LisiEtv8DbD4VZcUKl8F3YbA956Foye27dMrcsTT3dUaJXMAs2dGsbaS2d/VNSqd17bXERERkUjLAYpD1kuC20ItBC4GMMaMBfoAuU2OuRx4qcm2W4KPoj1jjOkSvpBh+oqtTJ22mivG9ub8Y3uGs2kRkSOTvwEWvAiPj4U3fghxSXDp8zDlCzhmkhJD0uY0cqg1iotc5jYpo22vY4yrO6TkkIiISKxrbs5322T9AeAxY8wCYBEwH2jY3YAxCcCFwL0h5zwB/Hewrf8GHgGu3+fixkwGJgNkZ2dTWFh40IB31Aa4/4td5KQZCjqVteicSKquro76GFtLfYpi1pK8ayPpVStIr1pNl7juFH7mBxM7jwHFzJ9VUELtNgas/huD6n0Ur3qa2sSuu1++pK7UJXRu0z8/E2ig++ZC+nz7T5J9m6hK68e3x9xDWdfjYYsHtkw/5LZj7c8KYrNPEB39UnKopQIBN3Jo6KT2uV7OaPj8UairgQQN1RYREYlRJUBeyHousCH0AGttJXAdgDHGAGuDr0bnAvNCHzMLfW+M+SvwdnMXt9Y+CTwJkJ+fbw82jW4gYPn+M0XU2Vr+dtPJDOqefrD+RVxhFEwPHG7qUxSp3uJmGd79mge+HW6fJ568QD3UfAnn/Bb6F0Qy0rDpsH9Wzdn4Nbx0M+zaTk1cZ1KqFkHDrr2P8cRBek/IyIFOOZDRCzJyg+9zoFMupHRt/cgefz0sfAk+fwS2r4Mex0LBo6Tnn8dQ09zvDVovpv6sgmKxTxAd/VJyqKXKVkBtBeSObZ/r9RoF1g+bvobeJ7TPNUVERKS9zQaOMsb0wxWUvhy4MvQAY0xnoMZaWwfcCEwPJowaXUGTR8qMMT2ttRuDq5OAxeEIdur01cxYVcbvLh7WIRJDImFVWw0bF+ydCKoIPhVqvO4JgyET3S95c0ZD9mCWvPY/HLPhZXh+Igw6F876NXQdGNl+HA5r3WxZsWDZO/DaTZDcGW74kKJl5RRMmAC7tkNlKVSUQmUJVG4Ivi91f+ZL3wZ/7d5teRNcAqlTbkgSKSfkfS6kZLonRBrqYOGLLim0Yz30HAFXvAyDznH7RSJEyaGWKilyy7x2Sg7ljHLL0nlKDomIiMQoa22DMeYW4APACzxjrV1ijJkS3D8VOBp43hjjxxWqvqHxfGNMCm6msx82afpBY8wI3GNl65rZ32pzv93OIx+u4PxhPbl8TN7BTxDpyPz1sOWbvRNBW5ftSYx06eu+Fxw/xSWCeh4LCan7NLO120kw6Sfw1RMw/RH48/Ew5iaYcLdLFnQU/gb45l8w4/ecsnUlZDwEo77fMZMZ1sKXf4KP7ncTAV3xEqT3gGWFrj8pme7VY9j+z99Z5pJFoUmkilKXSCqeBUs2QqB+7/Pikt2oo/oaqNroBgOc9zAcdVbH/DlKzFFyqKWKiyC5C2S1U6Y/vYfLNJfObZ/riYiISERYa98F3m2ybWrI+5nAUfs5twbIamb7NeGMsWJXPbe9NJ+enZL47cXDMPoiI7HEWti+1iWAGpNBGxdCg8/tT8lyCaDGUUG9RkHqPn/t9i8+CU6+E0ZcBZ/+Gor+Al+/DAX3wnHXgze+bfoVDg21rkjyF4+5n1HXQVSlD6Dzv2+DdZ/DBb+HxA40irChDt75Mcz/Pzcl/KSpEJ/cujaMgbRs9+o1ovljAgHYuWXPiKPKUqgoccuGWjjuTzDwDCWFJKooOdRSJbPdFPbt+Rc4Z5Sbzl5EREQkQqy13PPa12yu9PHKlHF0So7iL7IiLVG91SWANoQkg3Ztd/vikt0X/jE3us/iOaOhc5/wfAdI6wYX/hHGToYP7oP37obZT7lHzaJt9EhtFcz5G8x8HKo3uRE2Z/4fDL6ABdM+o8A7Dz77rUuofe9ZN3Iq2tVsg1e+75Ja4+92ybm2mgHM43G/7E/vAbTxTNciYaLkUEvs2uGGkQ79bvtet9coWPpv9w9ZRxp2KiIiIjHjxaL1vLd4E/ecO5hRvbtEOhyRQ7ejGN69C1a859aNx9UJOvo7IXWCjgZvG39F6jEUvv8mLH8PPvw5vHgpDDgNzvoNdB/Sttc+mJ3l8NVUKHrSFdbuN96NrulfsCd5Zbww/i7ofSK8dgM8dYYruH3cDdGV4ApVtsr9nCuK4eK/wrGXRjoikaij5FBLlM5xy9zj2ve6OcEs84b5MPD09r22iIiIHPGWb6riv/79Dacc1ZXJp/SPdDgihybgdyN0PvkvVzNowj0u2bGfOkHtwhgYfJ57tGj2UzDtAZh6Eoz+AZz6H5DatX3jqSiBL/8X5j3nauIMvsA9Cneg7z99T4IpM+CNKfDOT2Dt525kVFKn9ou7JdZMg1euAU88XPtv1XMV2Q8lh1qieDZg9iRr2kvjM6yl85QcEhERkXa1q87PLS/OIz0pnkcvHYHHE6UjAkQOZPMSeOs298vegWfA+Y9Clz6RjmqPuAQYdzMMvxwKH3CJokWvupE5x/8Q4hLb9vplK2HGH+Drf7jE2bGXwkl3QLfBLTs/tStc+QrM/BN8/Cs3m9t3/7Zncp1Im/usS1xlDYQr/+EKiYtIs9roIcsYU1LkhpwmZbTvdZM6QddBqjskIiIi7e6/3l7Cyi3V/P6y4WSnt/EXVJFwq/fBJ/8NfxnvCilf/BRc9Wp0JYZCpWTCeQ/CzTPdyJaP/hMePx6+ecsVzA63DfNd/Z3/HQOLX3Ujlm6b7x4ha2liqJHHAyfdDte/72Y1e/osmPVE28TdUgE/fPAf8O/b3SixGz5UYkjkIKJy5FBNQwT/IWkqEICSuTB0UmSu32sUrPnM/eMarc/wioiISEz598INvFRUzI8KBnDKUdmRDkekddbNcEmB8lUw/ApXy6c1s4tFUnY+XPVPWPWxS268cg30ORnO/s3+Z8ZqKWvdz2bGo7D6U0jMcI+OnfAjVyz7cOWNhSmfw5v/D96/xz1mNvF/2792am0VvHYjrHgfxv4Qzv5t29eREokBUTlyaLvP0uAPRDoMp2w51FZA7tjIXD9nFFRvhsoNkbm+iIiIHFGKt9Vw3+uLGNm7Mz8+c1CkwxFpuV073CNkz54P/nq45g03EqajJIZCDTwDpnwB5z8CW5fCkwXwr/8HVZta31YgAMvehafPhOcugE2L4PRfwJ2L4YxfhCcx1CglEy5/Ec7+Haz80I3cKp4dvvYPZkcxPHMOrPwIznvYjcZSYkikRaLyb0p9AF6fX8qlx+VFOhQ3hT24THgkNNY5Kp0LnXIiE4OIiIgcEer9AW55aT4Y+OPlI4n3RuXvEY9cgQDMfYYhS16HytfcyI/E9JBl8JXUKWQ9wxVdjuUR6NbCN2+6qeF3boUTb3XTlEeq2HS4eONgzI1uxuTPH4ZZU2HJG3DKnTDuFohPPvD5/gZY/BrM+L1LMHXu7RImI68++LmHwxhXR6n38fDP6+Bv58Dp98O4W9tu6niAkjnw0hXQ4IOrXnEJNhFpsahMDiV64Q8freDC4b1IivdGNpjiIkju4oqYRUL3oeCJc3WHhlwYmRhERETkiPDwh8tZWLyDP181irzMlEiHI6EqN8C/fgRrCslIzIYVq93jM/U7D36u8UBCY+KoSSIpNMG0z75Orv5MNCdZKjfAOz+F5e9Aj2NdceTDffwq2iR3hrN+DaOvg4/uh09/DXOfgzN+CUMv2TfxV78L5r8AX/4RdqyH7KNh0pPu2PYcRZMzGn44Hd661cW9bgZc1EYjuRa/Bv+6GdK6uxnJWls3SUSiMznUJdHDhgofL8z6lhsjPW1qyWzIHRO537bEJ7kEUencyFxfREREjgi7Gix/mbaGK4/vzXnDekY6HAm1+HV4+07w18EFf2BWVV8KTj3V7fM3QF011Fa6ZFFtFfgq915v+t5XCTXbYPu3e/bV1zR/7fhUyD/XJRYGnt72s2e1VHAUFR/9EgINcOZ/wQn/L7YfIcoaAJf/3dXy+eBeeO0G+Gqqe4Qrbwz4KmD2064Y9M4t7jvMOf8Dg85p2xE7B5LcGS593s3C9sF9MPVk+O7T0OfE8LRvLUx7EAp/C73HwWUvuBnURKTVovJfz6Q4GHtUVx7/bBWXjckjPSk+MoHs2gFbl7mhnJGUM8pNaRkIRO4fdhEREYlpZbssp3RP5/4LhkQ6FGnkq4B373LTjOeMhov/6hIEhYV7jvHGuS/gyZ0P71r+BqhrTCwFE0k15a4w8jdvuhmtkjrB0d9xiaK+4yOXiNmyzBWcLp7lZqK64PeQGeFfKLenfqfA5Gmw4EX49L/h6TNgwOnusaraChhwGpz8Y+h7cnQ8TmgMjL3Jlen45w9cTahT74OTf3J4323qffDWLbDon67w+Hcei57kpUgHFJXJIYC7zs7nwv/9gr9+vjZyxRBL57hl3pjIXL9RzmiY8wxsWw1dj4psLCIiIhKTAhb+dOXIyD/SL866L+CNH7rHpibcA+N/Ct42/IWpN86VUkjusvf2oy+A8x6CNYXu0Z0lb7pHllKzYchFMOy7buKW9vgFZkMtfP4ofP4IJKbBRU+4pEA0JEDam8cLo66BYy5yNYXm/A0GFLjZx3qNjHR0zes53D1m9u873KNx676Ai588tILY1Vvg5augpMjVMzr5x0fmfSASRlGbHDo2tzPnD+vJ05+v4fvj+tA1LQJZ4OLZ7hntxqLQkdJrlFuWzlVySERERNpErzQPg7qnRzoMaaiFz34DX/wRMvvBDR9C7nGRjckbD0ed6V4X7HIzQS1+Feb/H8z+K2TkwtBJbrR9z+Ft8yV9/Sw3E1nZchj2PfcoVVp2+K/T0SSmu+TI6fdHOpKWSUyHS56CfuNdAfGpJ7sRcf0ntLyNzd/Ai5e54uPfe84lyETksEX1M0o/PmsQvoYAj3+2KjIBlBRBtyHuH7FIys53z3uXzotsHCIiIhKz4qP6U+ERYstS+Ovp8MVjMPpa+OHnkU8MNRWf7CZJufR5uGuVK3Tc/RhX5+bJCfCn0fDpb2Dr8vBcz1cBb/8Ynjnb1UW66lWXXFBiqOMyxt3fN33qHlV8fiJ89jsI+A9+7ooP4emzXP2t695VYkgkjFr0McAYc44xZrkxZpUx5p5m9g82xsw0xtQaY37aZN+dxpglxpjFxpiXjDFJLQ1uQHYa3xudy99nrad4236K5LWVQABK5rpCbpHm8bpZF1SUWkRERCT2BAIuufKXCVC1Ea542dVPSUyLdGQHlpgOwy9z04b/dKWLuVMOTH8IHh8LT5zkHgPbvu7Q2l/6Njx+PMz9G5xwM9w8y41ektjQ/Ri46TMYfjlMe8AliSo3Nn+stTBrKrx0mRtRd9Onri6riITNQZNDxhgv8DhwLjAEuMIY07RS4TbgNuDhJufmBLcfZ60dCniBy1sT4O1nHAUG/vDxytacdvjKlruCbnlj2/e6+5MzCjYtgoa6SEciIiIiIuFSuQFemATv3wMDToWbZ7rZwTqalEwY/QM3jfhPlrlZsuJT4JNfwWPD4akzXAJsf1/+Q1VuhH9cDf+4CpIz4YaP4ZzfRX+yTFovMQ0mTXX1o0rnusfMVn2y9zH+enjnJ/D+zyD/PLjuPZeEFJGwasnIobHAKmvtGmttHfAyMDH0AGvtFmvtbKC+mfPjgGRjTByQAmxoTYA9OyXzgxP78vr8EpZvqmrNqYenuMgto2HkELi6Q/5a2LIk0pGIiIiISDgsfh3+PM597rzgD27E0KEU54026T3ghClw40dw+9dwxi/dzFLv3wOPHg3PXuAKKNds2/u8QMBtf/x49/jQ6ffDD6dBboTrf0rbG3GlG0WU1g1euBg+/pWbQW/XDvj792DO03DS7XDp/ylJKNJGWlKQOgcoDlkvAY5vSePW2lJjzMPAemAX8KG19sPWBvmjCQN46av1PPzhcv76/XZ67rqkyM3WkDWwfa53MI1FsUvnRe8MBCIiIiJycPuboj4WdenjZtA6+U5Xh2jxa7DoVXj7Dnj3p27a9aGXkFa1E557EL79Avqe4pJlXaPkc7i0j26D4cZP3AihGY/Ct1/Crm2wbS1MfBxGXh3pCEViWkuSQ81NN2Bb0rgxpgtulFE/YAfwT2PM1dbaF5o5djIwGSA7O5vCwsK99p/Z2/D6N5t56o1PGNil7adYHbO8EF9yfxZNmxaW9qqrq/fpU6tYy4nxGZTPfYflO6Pjw8Nh9ylKxWK/YrFPEJv9Up86jljsVyz2SSTqtPcU9dEkOx9OvQ8K7oVNX7sk0eLXYeWHHAeuOPGFf4KR12ha8iNVQoq7B/qOdwlEbzx8/1/Q9+RIRyYS81qSHCoB8kLWc2n5o2FnAGuttVsBjDGvAycC+ySHrLVPAk8C5Ofn24KCgr32jxnXwPSHCvloSwo3XHQCpi3/w9i1HQpLSD3+BxRMKDjo4S1RWFhI0z612oYT6FlRQs/DbSdMwtKnKBSL/YrFPkFs9kt96jhisV+x2CeRqBGNU9RHijFuyvuew+GMX0HJbFZNe5mBF90D6d0jHZ1Eg2O/5xJCxqN7QqSdtKTm0GzgKGNMP2NMAq6g9FstbH89cIIxJsW4bM7pwNJDCTQ1MY5bTxvIV2u3MX1l2aE00XKNs4LlRUm9oUa9RsHWZVBbHelIRERERKSlOsIU9ZHi8UDv4ynJm6gkgOwto6fuCZF2dNDkkLW2AbgF+ACX2HnFWrvEGDPFGDMFwBjTwxhTAvwY+LkxpsQYk2Gt/Qp4FZgHLApe78lDDfaKsb3J7ZLMg+8vIxBo0ZNth6Z4tstS50RZ8buc0WADsHFhpCMRERERkYPpqFPUi4jIEaclj5VhrX0XeLfJtqkh7zfhHjdr7txfAL84jBh3S4jz8JOzBnHnPxbyzqKNfGd4r3A0u6+SIug2BBLT26b9Q5Uzyi1L50LfkyIbi4iIiIjsX+UG+NePYE0hDDrH1VGJhZnIREQkJrXksbKocuHwHAb3SOeRD5dT7w+E/wKBAJTMjZ4p7EOldoXOvWHDvEhHIiIiIiL7E6tT1IuISMzqcMkhr8dw19n5rCuv4ZU5xeG/QNlyqK2AvLHhbzsceo3aUxNJRERERKKHrwJenwyvXgdZA2HKDDjuOs28JSIiUa/DJYcAThvcjeP6dOGxj1eyq84f3saLi9wyN0qTQzmjYcd62NnGRblFREREpOXWfQFPnOSmZy+4F67/ALIGRDoqERGRFumQySFjDD87dzBbqmp59st14W28pAiSM6P3P/PddYf0aJmIiIiEjydQF+kQOq610+HZ88Eb76aoL7gHvC0q7SkiIhIVOmRyCGBM30xOG9yNJwpXUVFTH76Gi2e7ekPROvy353DAqO6QiIiIhFXqzmKY//dIh9ExLXwZEjM0Rb2IiHRYHTY5BPDTs/Kp9DUwdfrq8DS4a7urOZQXhcWoGyWmQ/ZgjRwSERGRsPJ7k+HNm+Gdn0KDRhG1mL8Blr8Hg87WFPUiItJhdejk0JBeGUwc0Yu/fbGWLZW+w2+wJFjoOVrrDTXKCRaltjbSkYiIiEiMqEnpBSfeCrP/Cs9fCFWbIx1Sx1D8FezaBoPPj3QkIiIih6xDJ4cAfnzmIBr8lj9+uvLwGyspAuPZU9cnWuWMgpoyqGiD2dpERETkyHXWr+GSp2HDAnhyApTMiXRE0W/ZO+BNgIGnRzoSERGRQ9bhk0N9slK5YmxvXi4qZl3ZzsNrrLgIug1xj25Fs16NRak1pb2IiEhHZ4w5xxiz3BizyhhzTzP7uxhj3jDGfG2MKTLGDA1uzzfGLAh5VRpj7gjuyzTGfGSMWRlcdmlxQMO+Czd+5BIefzsX5j0ftr7GHGth+TvQvyD6Pz+KiIgcQIdPDgHcetpA4r0eHv1oxaE3Egi4ZEtuFNcbatR9qPvAprpDIiIiHZoxxgs8DpwLDAGuMMYMaXLYfcACa+2xwPeBxwCstcuttSOstSOA0UAN8EbwnHuAT6y1RwGfBNdbrscwmFwIfU6Ct26Ft+9UHaLmbPkGtq+D/PMiHYmIiMhhiYnkULeMJK4/uS9vLdzAkg0Vh9ZI2XKorYS8KK83BBCX4D60KTkkIiLS0Y0FVllr11hr64CXgYlNjhmCS/BgrV0G9DXGdG9yzOnAamvtt8H1icBzwffPARe1OrKUTLj6NTjpDpjzDDx3AVRtanUzMW3ZO4BRckhERDq8uEgHEC6Txw/ghVnreeiD5Tx73SEkeIqL3DLai1E3yhkNC16EgB883khHIyIiIocmBwgtIlgCHN/kmIXAxcAMY8xYoA+QC4RWjL4ceClkvbu1diOAtXajMaZbcxc3xkwGJgNkZ2dTWFi470Hxp5I9JJ7By/5Iw59OYMkx91DZaXDLexhh1dXVzfcrDEbPeZlAxiDmz10KLG2TazSnLfsUKbHYJ4jNfsVinyA2+6U+dRzR0K+YSQ51So7nRwUDeOC9ZcxaU84J/bNa10BJESRnQtaAtgkw3HqNgqInoWwFdDs60tGIiIjIoTHNbGs6HekDwGPGmAXAImA+0LC7AWMSgAuBe1t7cWvtk8CTAPn5+bagoGA/RxbA5kl4X76KUQt/Duc9BMdd19rLRURhYSH779dhqCiBwtVwxi8pOLkN2j+ANutTBMVinyA2+xWLpASVgQAAIABJREFUfYLY7Jf61HFEQ79i4rGyRteO60v3jEQefH8ZtrXTvBfPdvWGTHOf0aJQzmi31KNlIiIiHVkJkBeyngtsCD3AWltprb0uWFvo+0A2sDbkkHOBedba0JFEm40xPQGCyy2HHWn3Y2DyZ9B/Arx9B7x1GzTUHnazHdby99xy8AWRjUNERCQMYio5lJzg5fbTBzFv/Q4+WdqKz0C7truaQ3kdoBh1o6yBkJihGctE5P+zd+fhUZVnH8e/z8xkISErhBASIKxh32RxYYk77opKUeu+lLq01arVttYuWpeqLW19pYi4r1VUFJQqEgGVRRBUIOwIYd9DWJJM5nn/OAMECBLIJDNz8vtc11xn5pwz59w3CcmTe55FRKLbLKCdMaZVsAfQMGBc5ROMManBYwA3AVOstcWVTrmCg4eUEbzGtcHn1wLvhyTaBmlw5Vsw4Ncw50V44TwoXheSS0edwg+hcXto3C7ckYiIiNSYq4pDAJf3zqFV40T+NnERFYFq9h4qChZYomW+IQCPB5r1gLXqOSQiIhKtrLV+4HZgIs6kNW9Za+cbY4YbY4YHT+sIzDfGFOL0EvrlvvcbYxKAM4Gxh1z6UeBMY8yS4PFHQxa0xwun/wGGvgQbFsCoQbBqesguHxX2bIeV0zQRtYiIuIbrikMxXg+/Pqs9izbs5P25a6r3pqKZYDwHhmpFi2a9YP339btLt4iISJSz1k6w1ra31rax1j4c3DfSWjsy+Pwra207a20Ha+0Qa+22Su/dba1tZK3dccg1t1hrTw++73Rr7daQB97pIrh5EsQmOj2IZo2GYx3WH62WfAIBv4aUiYiIa7iuOARwbpcsumQn89Qniyn1Vxz9DatnQpPOENew9oMLpewTIFDuFIhERERE6lqTjnDzZGhzGoz/NYy7Hcr3hjuq2lf4ITTMjL4PFkVERI7AlcUhj8dw79kdKNq2h9dnrPrxkwMBZ96eaJpvaJ/sXs5W8w6JiIhIuDRIhSvehIH3wjevwPPnwI5q9t6ORv5SWPop5J3jDPMXERFxgYj8jeat2OMUbWpgQLvGnNg6nX99tpSSUv+RT9xUCKXF0TXf0D7J2c6nVpp3SERERMLJ44HTfgc/eQU2L3bmIVr5Rbijqh0rpkBZiYaUiYiIq0RkcShh9xr4R1f49I+wadFxXcMYw72DO7BlVxljpq048olFM51t8ygsDhnjzDuk5exFREQkEnS8AG7+DOJT4KULYcYo981DVPghxDaEVgPDHYmIiEjIRGRxaG+DppDZCb74JzzdF0blw4z/wK7Nx3SdXi3SOKtTJqOmLGfrrrKqT1o9CxqkQ3rrmgceDtknOJ/Q7S0++rkiIiIitS0jzykQtT0TProH3rsVyveEO6rQCARg0UfQ9gzwxYU7GhERkZCJyOJQua8hXPVfuGshnP1XCFTAR/fCk3nw2jCY/161Jzu8++w8dpf5eaZgadUnFM2EnD5OL5xolN0TsLBubrgjEREREXHEp8Cw1yD/fpj3GowZDNtXhzuqmlszG0o2aEiZiIi4TkQWh/ZLyoSTboPhU+HnX8KJtzpFkP9eC0+0hw9+CT989aPdldtnJjGkVw4vfvUDa7cf8qnVnm1Or5tonIx6n2aalFpEREQikMcD+ffBsNdh63JnHqIVU8MdVc0UfggeH7Q7M9yRiIiIhFRkF4cqy+wMZ/0F7pwPV7/rrBDx7Vvw/GD4Zw+Y/IjT8KjCr85oBxZGfLrk4ANFwYJKNE5GvU9COqS10rxDIiIiEpk6nOsMM0toBC9dBNOfid55iBZNgNz+zgptIiIiLhI9xaF9PF5ocxoM+Q/cvQQuHglpufD5Y/DPnvDcWfD1GKdXUFBOWgI/PbEl/529mqUbSw5cq2gmGI8zb080y9ak1CIiIhLBGreDmyY5H+59fB988odwR3TsNi9xepznnRfuSEREREIu+opDlcU1hB5XwDXvOz2Kzvgj7N0BH97pDDt782ooHA/+Mm47tQ0NYrw8+b9Kq5+tnglNOjvXiWbZJ0BxEezcEO5IRERERKoWnwxDX4Zuw2DGyGNeaCTsCsc72w7nhjcOERGRWhDdxaHKUrKh/51w63S45XPocxOs+greuBKezKPRlN/zux57+Oj7dcxbvd1ZbWLN7Oieb2ifffMOrVXvIREREYlgHg8MuAsqymDOi+GO5tgUjoesHpCSE+5IREREQs49xaF9jIFmPWDwI85qZ1e+Ba3zYfaLXPntdUyOv4dF//0DLP0USouje76hfbK6gfFqaJmIiIhEvow8aDUIvn7eWZE2GuzcAEWzoIOGlImIiDtVqzhkjBlsjFlkjFlqjLmviuMdjDFfGWNKjTF3H3Is1RjztjGm0Biz0BhzUqiCPypvDLQ/Gy5/Hu5eDBf8k/jULIYWvwivXe6c09wFxaHYRGjSUSuWiYiISHToezPsWA2LPw53JNWz+CPAqjgkIiKuddTikDHGCzwNnAN0Aq4wxnQ65LStwC+AJ6q4xAjgY2ttB6A7sLBGER+vBqlwwrWk3fYJl8WO5JWEq7En3grprcMSTshl93KGlUXr6h8iIiJSf7Q/B5JzYOaocEdSPYXjIbUlNDm0CSwiIuIO1ek51BdYaq1dbq0tA94ALqp8grV2o7V2FlBeeb8xJhkYCDwXPK/MWrs9JJEfp/gYLz85qz+/33oOH2f/whmG5gbNejkrtG1bEe5IRNxlzkswsj/sWBPuSERE3MPrg97Xw/ICZxWwSFa604mzw/nuaTeKiIgcojrFoWxgdaXXRcF91dEa2AQ8b4z5xhgz2hiTeIwxhtyQXjm0a9KQhycsZNLCDVQEXNDbJvsEZ6t5h0RCZ8ca+Ph+WP8dvP4TKC0Jd0QiIu7R61rwxsKs0eGO5MctneRMoK0hZSIi4mK+apxT1Uck1a2m+IBewB3W2hnGmBHAfcADh93EmFuAWwAyMjIoKCio5i2Oz2W5FTwzr5QbX/yajAaG01rEMDDHR2JM7XwiVFJSUqs5mYCf/p5Y1s54n2VbGtfafSqr7ZzCxY15uTEnqP28On//KOn+Mpa1G067JaPYMupivu9yvzMBfC1x49fKjTmBO/NyY04SwRpmQKeLYe5rcNoDENcw3BFVrXA8NEiH5v3CHYmIiEitqU5xqAhoXul1DrC2mtcvAoqstTOCr9/GKQ4dxlo7ChgFkJeXZ/Pz86t5i+OTD9xwUYD/zd/Ai1+t5M1FW3l/uZ9LemZzzUm5dMxKDun9CgoKqO2cWNGT5mYTzWv7PkF1klMYuDEvN+YEtZzXkk+g4Cs47QHaD7wbZral8YS7yS+dBIP/Wjv3xJ1fKzfmBO7My405SYTrewt89xZ8+yb0uTHc0RyuohyWTHSGlHmr02wWERGJTtX5LTcLaGeMaQWsAYYBV1bn4tba9caY1caYPGvtIuB0YMFxRxtiMV4P53XL4rxuWSxYW8zL01fy7jdreH3mavq2Sufak3I5q3MmMd5qLeoWfs16wewXoMKvBoxITZTvgQl3Q6N2cPIdzr6+N8OWZTD9aWjUGvrcFN4YRUTcIKc3ZHV3hpb1viHy5vT54QvYu0NDykRExPWOWvWw1vqB24GJOCuNvWWtnW+MGW6MGQ5gjGlqjCkC7gJ+b4wpCk5GDXAH8Kox5lugB1B7H7nXQKdmyTwypBvT7z+d353bkXU79nDba3MY8Nhk/jVpCZtLSsMd4tFlnwD+PbCpMNyRiES3aX+HbSvhvCfBF3dg/9kPQ7uzYcK9sPTTsIUnIuIaxkCfm2HjAqcQE2kKx4OvAbQ+NdyRiIiI1KpqdYmx1k6w1ra31rax1j4c3DfSWjsy+Hy9tTbHWptsrU0NPi8OHptrre1tre1mrb3YWrut9tKpudSEWG4e2JqCu0/luWt70y6zIU9+spiTH/mMO9+cy9zVYV1s7cdl93K2U5/UxLkix2vzUqc41HUotB508DGPFy57zlnK+L/Xw4aI6QgpIhK9ulwK8akw89lwR3Iwa6FwArQ5DWITwh2NiIhIrYqS8VJ1z+sxnN4xk5dv7MekXw/iyn4t+GTBBi5++gsu+vc0xs4potRfEe4wD9aoDQy6D+a/C/8ZCGtmhzsikehiLUz4tfMp8VkPVX1OXBJc+QbEJMBrP4GSjXUbo4iI28QmQK+rofBDKK7utJZ1YN08KC6CDueGOxIREZFap+JQNbTJaMgfL+zM9N+ezp8v6kxJqZ+73prHyY98xhMTF7Fux55wh3jAqffDdR+CvxSeO8vpRRSIsCKWSKSaPxaWF8DpD0BS5pHPS8lxCkS7N8PrVzhzFImIyPHrfaPTXpn9QrgjOWDRBDAeaD843JGIiIjUOhWHjkHDOB/XnJTLp3cN4pUb+9GrZRpPFyyl/2OTufXV2UxfvgVrbbjDhNz+8PNp0PFCmPRnePEC2L463FGJRLa9xfDxbyGrhzMp6tE06wlDnnV66L07HAKB2o9RRMSt0ltBuzOd4pC/LNzROArHQ4uTILFxuCMRERGpdSoOHQdjDP3bNebZa3oz5Z5Tual/K75YuoVho6ZzzoipvDZjFbvL/OENskEaXDYGLvkPrPsWnjkFvn8nvDGJRLLJf4WSDXD+3525haqj4/lw5p9hwXsw+eHajU9ExO363uL8HC78INyROIsSbPge8jSkTERE6gcVh2qoeXoC95/bken3n85jl3bFGMNv3/2OE/86iYc+XMCqLbvDF5wx0H0YDJ8KGXnw9g1OD4e9xeGLSSQSrZsHM/8DfW48MLF7dZ18B/S6FqY+AXNfq534RETqgzanQ1qryJiYunCCs9V8QyIiUk+oOBQiDWK9/KRPCyb8oj//HX4SA9tn8MKXKxn0xGRueGEWX671s2TDTvwVYRh6kt4Krv/Imaz62zdhZH9YPbPu4xCJRIEAfHgXJDSC0x449vcb4yx532oQjPsFrJwW+hhFROoDj8cp0q/6CtZ/H95YCsc7K1Omtw5vHCIiInXEF+4A3MYYQ5/cdPrkprOheC+vzljFazNW8VlJKaO+nUKcz0Ne0yQ6Nk2mUzPn0aFpEknxMbUbmNfnTFbd5jQYexOMGQyD7oUBdzvHROqrOS/Cmq/hklHQIPX4ruGNgaEvwXNnwhtXwU2ToHHb0MYpIlIf9LgKPnsYZj0LF4wITwy7tsCqL2HAr8NzfxERkTBQVaAWZSbHc9eZ7fnFaW15Y0IBiTntWbC2mIXrdvLJwg28+fWBSaJbpCfQMSuJTlkpdGqWTMesJLJTG2CMCW1QLfrB8C9gwj1Q8AgsnQRDRjm9i0Tqm5JN8OkfIXcAdBtas2s1SIUr34LRp8NrQ+GmTyEhPSRhiojUGwnp0PUy+PYtOONPx1+0r4klE8EGoMN5dX9vERGRMFFxqA74vB5ykjzk98zhkp7OPmstG3eWsmBtMQvWOY+Fa4v534IN7FvwLDneFywUJdMpy9m2y2xInK+ak+UeSXwyDPmPsyrIh3fByAFw3hPQ7SfOEBmR+uLTB6GsxBkWForv/fRWMOx1Z4XAN38KV78HvtiaX1dEpD7pezN887Izj9tJt9b9/QvHQ3K2s3qliIhIPaHiUJgYY8hMjiczOZ5TOzTZv393mZ/C9TuDPYycotEbM1ezp7wCAJ/H0LZJQzplJe8vHHXMSiY98Tj+AO16GTTvC2N/Bu/+DJb8D857Kjyf0ok7WBs9BcYfvoS5r0L/O50J20OlRT+4+P/gnRvhg186z6Pl30REwsIYMxgYAXiB0dbaRw85ngaMAdoAe4EbrLXfB4+lAqOBLoANHvvKGPNH4GZgU/Ayv7XWTqiDdGouqzs07wezRkO/4c5cRHWlbLfTq7rnT/WzW0RE6hUVhyJMQqyPXi3S6NUibf++ioDlhy27nN5F64pZsLaYL5ZtZuw3a/af0zQ53pnDKCuZ0zo2oWfz1OoNSUttAdd9CNP+7gwzWz0TLvkP5J5SG+mJm21dDq9eDpldYMizkd1jpqLc6TWX0gIG3hv663e9DLYsg4K/QqM2MPDu0N9DRFzBGOMFngbOBIqAWcaYcdbaBZVO+y0w11p7iTGmQ/D804PHRgAfW2svM8bEAgmV3vd3a+0TtZ9FLehzszNH4vLPoO0ZdXff5QXg36MhZSIiUu+oOBQFvB5D64yGtM5oyPndmu3fv6WklIXrdu7vYbRgbTGfL97EvycvpVXjRIb0zObintk0T0/4kasDHq/zx2vrU52G2AvnwYC7IP9+Z6LdMCmvCPDlsi2Mn7eGvaWlnNSuKQPzmpCd2iBsMckRbFgAL1/iDNHashQCfrjs+cgtEE3/P9i0EK54A2KP8v/jeA26F7Yug8/+4qx202VI7dxHRKJdX2CptXY5gDHmDeAioHJxqBPwCIC1ttAYk2uMyQT2AAOB64LHyoCyugu9FnW6ECZmwMzRdVscKhwPcSmQ27/u7ikiIhIBVByKYo0axtG/XRz92zXev2/n3nI++n49Y+cU8eQni3nyk8X0a5XOpSfkcE6Xpj++KlrOCfCzqfDxb2Dqk7BsMlw62un5UEcqApZZi1fz7czJlK6YTgd/Ifd5lpBudsJS8E/wUGp84PHh8cXii4nFeGPA43MKWZ4YZ/U1T8wRXlc+L+bg5x4feGNJ3tkIyK+znKNe0Wx49VLwxjmTMC//3Pkeevv6yCwQbV8NBY9C3rmQd07t3ccYuPBfsH0VvDscUppD8z61dz8RiVbZwOpKr4uAfoecMw8YAkwzxvQFWgI5QAXOsLHnjTHdgdnAL621u4Lvu90Ycw3wNfBra+222ksjxHxxcMJ1MOUJ2PYDpLWs/XsGKmDxR9D+rLB+OCYiIhIOKg65TFJ8DEN7N2do7+as3rqb9+eu4Z05a7j37W/5w/vfc3bnpgzplcMpbRrh81Yxhj+uIVz0NLQ905kvZeQAOOex2ht7by2BbT+wYu5kNi+YSvLmb+htV3KiCQCwK7UVca3Ox6a3YuvO3azeXMyarcVs2r4LT5mfeE8FWUk+miX4yGzoJdFnMQG/M2woUA4VfijbFXx9yP5AeZWve2GhZBKc+lto1jP0ObvJiqnw+jBIaATXvO9MyNyko3MsUgtEH9/nzI10zmO1fy9fHPzkVRh9GrxxhbPEfV38gSMi0aSqX672kNePAiOMMXOB74BvAD8QA/QC7rDWzjDGjADuAx4AngH+ErzWX4AngRsOu7kxtwC3AGRkZFBQUBCClEIjrqwDJ2JYPfaPLG9z7XFfp6SkpFp5pWyfT8/dW5hfkcumCPp3qEp1c4ombswJ3JmXG3MCd+alnKJHJOSl4pCLNU9P4PbT2nHbqW2Zu3o778wp4oN563h/7lqaJMVxcc9shvTKpkPT5MPf3PliyOnjTFQ97nZnsuoLRtR8ae7yvbBuLnb1THYsnoZ37dcklW+hDdDUxlGU0JEVLW6hRfd84nL7kVjpfo2Cjx44E3fPWL6Vzxdv4pnFm1ix0vmQtEV6AoPaZzCofQYntWlEYtwxfouX7WLZG/fRZvU4GJUPHc53ikSZnWuWtxst+hjeusYpCF39HiRnHTh24nBnG2kFosUTofBDOP1BZ76tupDYCK78Lzx3Brz2E7hxIsSn1M29RSQaFAHNK73OAdZWPsFaWwxcD2CcCQVXBB8JQJG1dkbw1LdxikNYazfse78x5lngw6pubq0dBYwCyMvLs/n5+TVOKKR2vEeLlQW0uOYZiIk/rksUFBRQrbwmfgLeWDpf+AtnZdcIVu2coogbcwJ35uXGnMCdeSmn6BEJeak4VA8YY+jZIo2eLdJ44PxOTC7cyDtz1jBm2gpGTVlOp6xkhvTK5sIezWiSVKnhlZIN14yDr/4Fk/4CRV/DJSOh9aDq33zHGiiaCatnYlfPxK6bhydQjgF2BJrwDR0pyehJdtd8+vTrT16D6jX8EmJ9nNqhyf6V3n7Ysospizfx+eJNvDOniJen/0CM19AnN90pFuVlkJeZdPRJumMTWd3iUtoMfQimPwNfPe3MP9BliDMHU+N21c/dzb572ykcNu0KV73jFEAOFWkForLdMOFuyOgAJ91et/fOaA9DX4ZXhsB/r3OKRV79+BURAGYB7YwxrYA1wDDgysonBFck2x2cU+gmYEqwYFRsjFltjMmz1i7CmaR6QfA9WdbadcFLXAJ8XzfphFjfW2DhBzB/LPS48ujnHy9rnd/3rQZFfGFIRESkNuivk3omzudlcJcsBnfJYuuuMj6Yt5axc4p4aPxCHvmokIHtGjOkVw5ndsokPsbrLB97yi+dxtI7N8FLF8HJd8BpDxx+cX8ZrP8OVs8IFoRmQXGRc8gTxwLa8GXZYL6x7YnJ7cfAnp04u1NTUhJqPq6/ZaNErj4pkatPyqXUX8Hsldv4PFgseuSjQh75qJDM5Lhgr6Im9G/b+MfvG58C+fc5jdIv/wUz/gPz34XuVzgTDafl1jjmqPX18/DhndDyZGdC5x9rREdSgWjqk878P9eND08MrQfB+X+HcXfAR/fAeU9pmWQRwVrrN8bcDkzEWcp+jLV2vjFmePD4SKAj8JIxpgKn+HNjpUvcAbwaXKlsOcEeRsDjxpgeOMPKVgI/q4t8Qi53gFPUnzmqdotDGxfCthVwyi9q7x4iIiIRTMWheiw9MZZrT87l2pNzWbpxJ2PnrOHdb9Zwx+vfkBTn47xuWQzplUPvlml4mvWAn02B//0OvvwnLC8gOesqWFgSLAbNgrXfgH8vAP6kbJbHdeLT+HP4eEcLFtGSE1pncn63ZjzSpSnpibX3x3mcz8vJbRtzctvG3H9uR9bv2Lu/V9HH36/nra+L8Bjo2SJt/xC0rtkpeDxV/KGekA5nPAgn3gpf/ANmPgvfvgk9r4aB9zi9q+qTL0bAJ3+AdmfB0Jcgphorx0VCgWjTYif2bsPCuwJNr2ucJe6/+Ac0agcn3Rq+WEQkYlhrJwATDtk3stLzr4Aqu65aa+cCvavYf3WIwwwPY6DPTU7Pz6LZzuIZtWHReGebd27tXF9ERCTCqTgkALRtksS9gztw91l5TF++hXfmrGHcvLW8MWs1zdMbcEnPHIb0zCb3/L87k1WPu51e63/jTInpjYWs7uzsei1flbXh1TVN+Hy988d/n9w0Lh3YjHO6Nj14yFodapoSz9A+zRnapzn+igDzinbs71X0908X89Qni0lPjKV/28YMap9B+a4AFQGLt3KxqGEGnP0wnHSb0wNl9osw9zXofT30vwuSMsOSW52x1lmSfeqT0HkIXPKfYyvwhLNAZC1M+LWzZP1Zf6mbe/6Y0x90lrif+FtnvqbaXDFNRMQNug+DT/8Es56tveJQ4XjI7g1JTWvn+iIiIhFOxSE5iMdj9ve6+cvFnZk4fz1j56zhX58t4Z+TlnBCyzSG9OrMBdd9zrpPn6Fxz3P4YGMG78/fwjdfbQege/NUfn9eFud2zaJZajV6ltQhn9fDCS3TOKFlGned2Z6tu8qYusQpFE1ZvIlx85w5QB/86mPaZTakfWYSeZlJ5DV1Hk2TszDnPekMtfv8cacn0ewXod8tcMqvaj5hdyQKBJyizsxR0OtaZ2iUx3vs1wlXgei7t2HFFDjvSWjYpPbvdzQeD1wyCnacC2/fCDd8BFndwx2ViEjkiktyCkRzXoSzHoLExqG9/o41Tu/n0x8M7XVFRESiiIpDckQJsT4u6ZnDJT1zWLdjD+/PXcs7s4v43bvf8yevh6zEAaz6rhhri+ncLJnfDO7A+d2yaJ6eEO7Qqy09MZaLemRzUY9sAgHLwvXFjP1sJiY1m0UbdvLF0s2MnbNm//lJ8T7yMpNo3zSJDk3vputF19Np8TPEffFPmDUGTvy507uoQWoYswqhCj+8fxt8+4Yz19SZf6nZPDmHFogufwG8NZ9z6oj27nB66DTrBSdcf/Tz60psgjNf07Onw2vD4OZJkNws3FGJiESuPjc5PYfmvAQD7grttRcFR/R1OD+01xUREYkiKg5JtWSlNGD4oDb8bGBr5q8t5p05RUybv4o7z2jP+d2yaJ3RMNwh1pjHY+jcLIVNOTHk53fav3/brjIWb9jJ4g07WbRhJ4vXl/DhvLW8NsMfPOMS+jU8mbu8Y+k35XHKvhzJlu63kDzodhKT08KTTCj4S+HtG5yl30/7PQy4OzQTKJ84HLDw8X3Oyl21WSD67CHYvRmueuv4ejvVpqSmcOWbMOZsZ4n76z+CuOj/fyQiUiuadIBWA+HrMU7v3VD+TC8cD43aOitLioiI1FMqDskxMcbQJTuFLtkpFCRtIj/f/Uu7pyXG0q91I/q1PrBcu7WWDcWlwWLRThZtyOHhDe3xlJzHbRVvcebsJ9jy9X94PvZS5jcbSutmjZwhak2TaN24IbE+TxgzqobSEnjzKlheAOc8Dv1CvMjNiT93trVZIFr7Dcwa7Xza3KxnaK8dKk27OMPrXv8JjL2ZPZe8SKnfhjsqEZHI1OdmeOtqWPwxdDgvNNfcsx1WTnV6/YqIiNRjKg6JHAdjDE1T4mmaEs+g9hn791cETmH11muYvmAaWXOe4vbtL7Bl1Tj+texC7vGfShkx+DyG1hmJtG3SkIyGcaQmxJKeGEtqQgzpibGkJcSSlhhLWkIMDWK8mLpe7nzPNnh1KKz5Gi4eCT2uqJ371GaBKFABH94FCY2dXk8RpNRfweqtu1mxeTcrNpewYnMW7ZOHc/2i/+Plh67jr/6rSJo6kYzkODKT4slMjqNJcjxNkpxtZlIcmcnxNEmOIyFWP8JFpB7JOxeSs535/kJVHFr6KQT8GlImIiL1nv6yEAkhr8eQ2ziR3IFnw8CzYeUXNPrsIf646gV+l/o/vm87nM/iTmfhxj0sXLeTL3ZtYcee8iNeL87nIS3h0MJRjLM95Pm+AlPDON/xF5RKNsLLQ2DzImep+o4XHOe/RDXVVoFo9vOwdg4MGQ3xKTW/3jHyVwQo2rZJ3gNKAAAgAElEQVSHFVt2sXLzLlYEHyu37GLNtj0EKnUOSkuIobDx+bRNW88t28bSMTXAsqxz+SbQlqKSALNXbWNDcSll/sBh90mK8+0vIjVJDhaNKhWRmiQ7xSUVkUTEFbw+Z5XQzx6CzUugcQh6Lxd+CIlNnJXKRERE6jH9xSBSm3JPgesnwPLJxHz2MD3n/oGeaWMg/37oejl4vPgrAuzYU8623WVs213O1l1lbN9dxtZd5cGts3/b7jIWri9me/C5PcLooxivcXojVSoqlWwr5aPN3+LxGDzGKWJ5zL6H8zqlbAPDCm8nqWwT4zo+RdGaDnjXLcEYEzyfw95T1TGv55CHMXi9ztbnMXg8h2ybX0nGyXvJ/PKP7Hr1ajYPHok3Jvag9/s8Hjwe9m+9wftUqWQjfPpnZ26KrpfV2pc2ELCsK97Lik27DioCrdy8i1Vbd+OvVAFqGOejVeNEejRP45Ie2bTKSCS3USKtGieSmhBcsa2iH7zno/93bzNgyUdc5411/ljJ64/NPYXiRj3ZsNfDxuJSNhTvZcPOvWwsLmVjcDtn1TY2FpdSWs0iUtPkeC7umU16Yh2sGCciEiq9roWCx5xhw+c8VrNr+UthyafQZYizkqSIiEg9puKQSG0zBtqcBq1PhcUTYfJD8O7PYMoT0OMKfJ0uplGjNjRqGFftSwYCluK95QcKR7vKgsWlQ17vKmfpxhK2FFeweOdGKgLOfEkV1hIIWKyFCmtpYdcwxvMwPvYwrPw+Zs9OBxbX3r/JYdpzvfdqHlz+MlP+dRl3lN+Bvxo/nnwGGkyeSFyMh1ivh7gYL78r/QeDyndx986fsuW5mcT6PMQFH85z78HP97/XeV3V+eUVAaf4U6kI9MOW3QcVYuJjPOQ2SiSvaRJnd2lKq0aJ+4tAjRvGHr03l9cHlz7LtJRLGNDCBz9Mg5XTYOqTmCmPk+KJISWnN+1z+0Nuf+ja11n1rBJrLcV7/GzcuZcNwcLRhmAxadNOZ/vNqu1sKN5LqT/A6KnLGXn1CXTLccnqeiLifg2bQOeLYe5rcNoDNZvIf8VUKNsZuiFqIiIiUUzFIZG6YgzkDYZ2Z0HhB/Dlv2DSn51HZlfofBF0ugQatz3qpTwep3fQ/l4nR1FQUEB+fn7VB9d9C6/cDtbA1RN5J6sb1loCFgLWUlGpiBQIFpUCluD+4PP9+53zKwJOAWr/80Mf1uIPOO85sO3Jd0tzOOe7RyjIfolpPR7Hj++w9+177q8IsHTFD2Q2y6bMH6DUH6B58WzOWD2ZcclXsjG2JWXlFWzfU0ZpeYCyigCl5QFK/RWU+gOU+QMH9e6pjhivoUV6Aq0aJzKofQa5jRP3F4Eyk+LxHKk30zGo8CVA+3xof5azY28xrJ7hTJi6chpMfQqm/A08MZB9glMoyu0PzftiYhNJSYghJSGGdplJR7yHtZZvi3Zw66tzuGzkVzx0cReG9m5e49hFROpE31vgu//Cd29B7xuO/zqFH0JMIrQaFLrYREREolS1ikPGmMHACMALjLbWPnrI8Q7A80Av4HfW2icOOe4FvgbWWGs145/Ubx4PdLrIeWxfDQvHwfz3nDkUPnsImnR2PhXtdHHtL6u7aga8ejnEJcE17+2fv8EYg9eAF0NMXa4A3/0+yE4h5+P7GPbDg85KXj8yB1FBwTry8zs7L/xlMPJmSG3Jhbc9xYUxDY56O3+FUzTaV1xyikgV7C0P7C8glfor8HoMLdMTaZYaj89bx0MP4pOh3ZnOA6B0p/N121csmvZ3mPpEsFjUq1KxqB/EJlZ5SWMM3Zun8sEd/bnj9Tnc+/a3fFu0nT+c3znyV9ITEcnpA027ORNTn3C98+HLsQoEYNFH0O4MiIkPfYwiIiJR5qjFoWBh52ngTKAImGWMGWetXVDptK3AL4CLj3CZXwILgeSahSviMqnNneVzT7oNdqw5UCia/LDzyOh4oFDUpENo771sMrxxJSRlwTXvO7FEgsqTVL99/VELRPt99W9nIu0r34JqFIYAfF4PPq+HanbAigxxSc4fM+3OcF6X7gz2LAoOQ5v2D5j6JHh8Ts+ilqccKBYdMvwiPTGWF6/vy98mLuI/U5azcN1O/u+qXmQm6w8lEYlgxkDfm2HcHfDDl878fsdq7RwoWQ95GlImIiIC1es51BdYaq1dDmCMeQO4CNhfHLLWbgQ2GmMO+w1rjMkBzgMeBu4KRdAirpSS7RRGTvw5FK+FhR84haKCR6HgEWicV6lQ1PH4PindZ+EH8PYN0Lg9XP2uM4dDJDnx52AtTLy/egWi7avg88edpYjbn113cUaCuCRoe4bzACgtObhY9OU/YdpTTrGo2b6eRadA8xMhriE+r4f7z+1I15wU7n37W87/1zSeuaoXvXPTw5uXiMiP6XIZ/O8BmPXs8RWHCseD8R4YwisiIlLPVac4lA2srvS6COh3DPf4B3AvcOQJMETkYMnNoN/PnMfO9QcKRZ8/Dp8/5hR1Ol3kFIoyOx9boWju6/D+bc4QpKv+Cw3Sai+PmjjpVmdbnQLRR79x/g0GP1r18fokriG0Pd15wIFi0Q9fHF4saj8Y+twErQZxfrdmtGuSxM9e/ppho6bz4AWd+OmJLY8+kbaISDjEJkDPn8KMkVC8DpKzju39heOdYnmk/g4UERGpY9UpDlX1l0G1ZnE1xpwPbLTWzjbG5B/l3FuAWwAyMjIoKCiozi2iRklJiXKKEpGZVztodQ+xzW6i8ebpZGz6ktQpT2Km/I3dDZqxKeNkNmWcQknDVlUWivbl1GzNeNovGcW21G58n/trKmbMC0Mux6ITOW1upO3C59j0zAUs6HQ31nPgx1ZJSQnfvf0YXRdNYFnra1k9dxmwLHzhhkDtfP95wTsQ2gzEk7uXlB2FpG/9hsxlnxFb+CG7GzRjbbPBbGp6Ovf0SGTUtx4eeH8+E79exDWdYon11qxAFJn/p2rOjXm5MSdxsT43wldPw+wX4NT7q/++zUudYch9bqy10ERERKJNdYpDRUDlyUhygLXVvP4pwIXGmHOBeCDZGPOKtfanh55orR0FjALIy8uzR1xZKUr96GpRUcqNOUE05HWJsynZBIUfkLDgfVqueJeWq96G9NYHehRldd9fKCqYPJl8z9ewZBTknUfaZWMYEDUTcObDV23JmHg/gza9CJeN2d+DaMqkj+m65BXI6Eibq56kTXXmJopwdfP9N9jZ+EthwfskzBpN22VjaPvDa9DlMs4ZegMjCpMYMWkJO0jkmZ/2Iict4bjvFvn/p46PG/NyY07iYumtncn6Zz8PA34NvmpOILdovLPNO7f2YhMREYky1VmWZhbQzhjTyhgTCwwDxlXn4tba+621Odba3OD7PquqMCQix6FhhrOE7zXvw92L4YIRkJYLX/wTRg2Cf/aAT/4Aa+bQevmL8NlfoNswGPpS9K3MctKtcPYjzoTdb98AFeUAtPzhLdixCs5/qnqTVsvBfHHQbSjc+D8YPg26XwHz38Xz3GncueIWJgxYyfrNW7nw31/w5dLN4Y5WRORwfW6Gkg1Q+EH131M43lntLFIWYhAREYkAR+05ZK31G2NuBybiLGU/xlo73xgzPHh8pDGmKc5S9clAwBjzK6CTtba4FmMXkX0SG8MJ1zmP3Vuh8ENY8L7T3f6LEbQApwF9zuPgidKlyg+ag+gGGPQbmq9+D3pcBS1PDm9sbtC0K1zwDzjzz/DtmzDrOTrN+i3T41N4zw7igTE/8JPBp3LzgNaah0hEIkfbM5wPRmaOhi6XHv38ko2weibkH8MwNBERkXqgOsPKsNZOACYcsm9kpefrcYab/dg1CoCCY45QRI5NQjr0usZ57N4KiyawcNFSOp77YM1WOIsElQtESz6hwtsAz5l/Dm9MbhOf7CwR3ecm+OFLvLNGM2ThB1waO46pn3Rh9MLLuera4STER1nvMxFxJ4/H+Xn1v9/D+u+haZcfP3/RR4CFDlrCXkREpLIo7UIgItWSkA49f8qGpvnRXxjaZ98QM/8elrW5zuk1JaFnjLM89OXPY+6cjz319/RI2MzN6x5k12Od2Dbhz84KQSIi4dbjKvDFO8vaH03heEht4az0KSIiIvupOCQi0eekW+HeFazPOjPckdQPSZmYQfeQ9JsFzB80kkW2OWkznyTw987w5tWw/HOw1VrEUkQk9BLSoetl8O1bsGf7kc8rLYHlBdDhfPd8YCIiIhIiKg6JSHRKSA93BPWPx0vnU6+g5S8+4saUZ3m2/Bz2LPkcXroQ/t0Hvvo/2LMt3FGKSH3U52Yo3w3zXj/yOcsmQUWpVikTERGpgopDIiJyTJqnJ/D07ZeyqOs99CgZwejG9+GPS3HmgnqyI7x/G6z9Jtxhikh90qwH5PSFmc9CIFD1OYXjoUEatDipbmMTERGJAtWakFpERKSy+BgvTw7tTrecFB4aH8dr6Sfx/OXxtFz+ujO045tXIPsE6H0jngrNCyUidaDvzTD2Zlg+GdqefvCxinJYPNHpNeRV81dERORQ+u0oIiLHxRjDdae0olOzFG59dQ7nvLmdJy6/j3PP/DPMewNmPQfv30p/EwPzMiEhzfnUvkEaNEh3tgnph+/b9/DFhjvF6rEWKsqchz+4rSh1/hj1lx44dtg5+14Hzz3sfeUHXtt9PSEs2OB2372xleZ8sgf2HcfxDttKgOmQ3gYatXa2DVJr819PJHQ6XQQf3w+zRh9eHPrhS9i7HTpoSJmIiEhVVBwSEZEa6dsqnQ/v6M/PX53Nra/OYfigNtxz9i14+94CK6exZtJomjdKcOYj2rMVNi4MPt8GAf+RLxzbMFgwSj2kkFSpiHToPl8slO8Ff/BRvscptPiD2xC+HlC6B6ZUQKA89P+o3ljwxoE3xnkYDxCcQNcY5/n+CXVN8NCPHTfVOp66YyMUfM7+4hFAQqNgsajNwUWjRm0gLin0uYscL18cnHAdTH0Stv0AaS0PHCsc76xo1ua0sIUnIiISyVQcEhGRGmuaEs8bt5zInz9YwMjPlzF/7Q7+Oawnaa0GsKxtBc3z8w9/k7VQutMpGO0rFu3e93z7gf379q3//sA+e4Q5RY6XJ8b5wzEm3tnue+x7ndDY+cMzpoGz9cWzdt0mmue2PlDE8cUFizrBh29fcaeqfcFzfbEHH/fGBotB4VlJaXpBAfmnnAjbVsCWZbB1WXC73FmV7tDJfhObVF00Sm8NsYlhyUHqud7Xw7Sn4OsxcOafnH3WwqIJTmFI35ciIiJVUnFIRERCIs7n5eFLutI9J5Xfv/c9F/x7GiN/esKR32AMxCc7j7Tc6t8oEIDS4gMFpT1bnWLS7q3O0KyYePA1OKSY0+DHiz8e7zHnu6ygoOqiV7SLiYcmHZ3Hocp2wdYVlYpGy2DLclj6CczdcPC5SVmHF40atYW0Vs49RGpDSo4zr9CclyD/foiJp2HJCtixGgb9JtzRiYiIRCwVh0REJKSG9mlOXtMkhr8ym0uf+ZKh7X0krtxKnM9DnM/rbGMqPfd58HmPYfFMjyc41CwVaFVreUgVYhOhaRfncajSnU4Po8pFo63LoHAC7N5c6UTj/AEvUlv63gKFH8L8sdDjShpvnuEMzcw7J9yRiYiIRCwVh0REJOS6N0/lgzv6c/trc3h5wVZeXvDVj57v9Zj9haI4nzdYPDpyMelI58R4nUJTrNfg83jweY2zzxPcBvfH+o58PMbrISZ4zOc1xHg8eDzhGeYVVeKSIKu78zjUnu1O4ahy8Yj5dR6i1BOtBkLjPGdZ+33Foeb9IFErJ4qIiByJikMiIlIrGjeM45Ub+/HCuMnkdelGaXmAUn+AUn+Fsy0PbvftO+y487yswnlevMd/2LFSf4C95RUE7NHjqQmPAZ/XQ4zHEOPz4PN4qCgvI2H6Z/i8Bq8xeD3Oo/Jrn8ezf7/z2lTx2oPXA16P56DjlZ8HrCVgcbaBA8+thYqA3X/cWht87TwPWEvF/nMtgQBU7Hse3F8RcK4TsJaSHXsZv2keTZLjaJIUT5OkOJokx5HRMJ4myXHExxz78DvA6eWV3ct57PdsSL42bmCMGQyMALzAaGvto4ccTwPGAG2AvcAN1trvg8dSgdFAF5yZxG+w1n5ljEkH3gRygZXAUGvttjpJKNyMgT43wUf3wPdjabhrBZxyY7ijEhERiWgqDomISK3xeT20TfMyoF1Grd7HX+EUlsr8AcoDAcorLP6K4DYQwF9hKa8I4A842+oe3/faX2Epr3ReeYVlzdq1ZDRpREXAOS9gLf4Kp9hy6OtSfwUVFufc4L6KYGHm8NeBg15XBCweY/AYgzHgCRae9j337Nt6Kj0Pnuv1HPK+Ss89ngPn7nvfjjLLlCWb2FxSRkUVFbekeJ9TMEqKDxaQDjzPCD7PSIojOd6HCdOk2tHGGOMFngbOBIqAWcaYcdbaBZVO+y0w11p7iTGmQ/D8fWu1jwA+ttZeZoyJBRKC++8DJllrHzXG3Bd8XX8m3ek+DCb9CT74lfM6T0vYi4iI/BgVh0REJOr5gsPJEuPq7p4FBVvIz69iCFUUKygoID8/n4qAZeuuMjbu3MvGnaVsKi7d/3xjcSmbSkqZs2obG4tLKfUfvnJcfIxnf7HIKSDF0SQ5PrjP2S/79QWWWmuXAxhj3gAuAioXhzoBjwBYawuNMbnGmExgDzAQuC54rAwoC77nIiA/+PxFoID6VByKT3YKRLNGsyuhBYmN2oQ7IhERkYim4pCIiIgcxOsxZCQ5vYE6/8h51lqK9/rZtHMvG4tLneLRzr1s2lm6v5C0eMNOpi3dzM69/jqLP8pkA6srvS4C+h1yzjxgCDDNGNMXaAnkABXAJuB5Y0x3YDbwS2vtLiDTWrsOwFq7zhjTpHbTiEB9boZZo9mUcSJawF5EROTHqTgkIiIix8UYQ0qDGFIaxNC2SdKPnrunrCJYNHJ6IJ33WB0FGfmqGn936Ji+R4ERxpi5wHfAN4AfiAF6AXdYa2cYY0bgDB97oNo3N+YW4BaAjIwMCgoKjjmBSJbc83E20IiVLsurpKTEdV8rN+YE7szLjTmBO/NSTtEjEvJScUhERERqXYNYLy0aJdCiUcLRT65fioDmlV7nAGsrn2CtLQauBzDOZE4rgo8EoMhaOyN46ts4xSGADcaYrGCvoSxgY1U3t9aOAkYB5OXl2fz8/FDkFEHy9w+XdBPlFD3cmJcbcwJ35qWcokck5OUJ691FRERE6rdZQDtjTKvghNLDgHGVTzDGpAaPAdwETLHWFltr1wOrjTF5wWOnc2CuonHAtcHn1wLv12YSIiIiEt3Uc0hEREQkTKy1fmPM7cBEnKXsx1hr5xtjhgePjwQ6Ai8ZYypwij+V12W/A3g1WDxaTrCHEc5QtLeMMTcCq4DL6yQhERERiUoqDomIiIiEkbV2AjDhkH0jKz3/Cmh3hPfOBXpXsX8LB5a7FxEREflRGlYmIiIiIiIiIlKPqTgkIiIiIiIiIlKPqTgkIiIiIiIiIlKPqTgkIiIiIiIiIlKPGWttuGM4jDFmJ7Ao3HGEWGNgc7iDCDE35gTuzMuNOYE781JO0cONedVlTi2ttRl1dC+pBpe2v0D/V6OFG3MCd+blxpzAnXkpp+gR9jZYpK5Wtshae9jKG9HMGPO1cooObszLjTmBO/NSTtHDjXm5MSc5Jq5rf4E7v6+VU/RwY15uzAncmZdyih6RkJeGlYmIiIiIiIiI1GMqDomIiIiIiIiI1GORWhwaFe4AaoFyih5uzMuNOYE781JO0cONebkxJ6k+t3793ZiXcooebszLjTmBO/NSTtEj7HlF5ITUIiIiIiIiIiJSNyK155CIiIiIiIiIiNSBiCoOGWMGG2MWGWOWGmPuC3c8oWCMaW6MmWyMWWiMmW+M+WW4YwoVY4zXGPONMebDcMcSCsaYVGPM28aYwuDX66RwxxQKxpg7g9973xtjXjfGxIc7pmNljBljjNlojPm+0r50Y8wnxpglwW1aOGM8HkfI62/B78FvjTHvGmNSwxnjsaoqp0rH7jbGWGNM43DEVhNHyssYc0fw99Z8Y8zj4YrveBzh+6+HMWa6MWauMeZrY0zfcMYodcdtbTC1v6KLG9tgbmh/gTvbYG5sf4E722BubH9B5LbBIqY4ZIzxAk8D5wCdgCuMMZ3CG1VI+IFfW2s7AicCt7kkL4BfAgvDHUQIjQA+ttZ2ALrjgtyMMdnAL4De1tougBcYFt6ojssLwOBD9t0HTLLWtgMmBV9Hmxc4PK9PgC7W2m7AYuD+ug6qhl7g8JwwxjQHzgRW1XVAIfICh+RljDkVuAjoZq3tDDwRhrhq4gUO/1o9DvzJWtsD+EPwtbicS9tgan9FF1e1wVzU/gJ3tsFewH3tL3BnG+wF3Nf+gghtg0VMcQjoCyy11i631pYBb+B80aOatXadtXZO8PlOnF922eGNquaMMTnAecDocMcSCsaYZGAg8ByAtbbMWrs9vFGFjA9oYIzxAQnA2jDHc8ystVOArYfsvgh4Mfj8ReDiOg0qBKrKy1r7P2utP/hyOpBT54HVwBG+VgB/B+4FonKiuyPk9XPgUWttafCcjXUeWA0cIScLJAefpxCFPy/kuLiuDab2V/RwcRss6ttf4M42mBvbX+DONpgb218QuW2wSCoOZQOrK70uwgW/xCszxuQCPYEZ4Y0kJP6B80MmEO5AQqQ1sAl4PthVe7QxJjHcQdWUtXYNTjV9FbAO2GGt/V94owqZTGvtOnD+CACahDme2nAD8FG4g6gpY8yFwBpr7bxwxxJi7YEBxpgZxpjPjTF9wh1QCPwK+JsxZjXOz45o/ORUjp2r22Bqf0U817XBXN7+Ave3wVzR/gLXtsHc2P6CCGiDRVJxyFSxL+qqm0dijGkIvAP8ylpbHO54asIYcz6w0Vo7O9yxhJAP6AU8Y63tCewi+rrIHiY4BvwioBXQDEg0xvw0vFFJdRhjfoczLOLVcMdSE8aYBOB3ON1j3cYHpOEMWbkHeMsYU9Xvsmjyc+BOa21z4E6Cn+SL67m2Dab2V1RwXRtM7a/o5Zb2F7i6DebG9hdEQBsskopDRUDzSq9ziNLul4cyxsTgNExetdaODXc8IXAKcKExZiVO1/PTjDGvhDekGisCiqy1+z5VfBunoRLtzgBWWGs3WWvLgbHAyWGOKVQ2GGOyAILbqOtSeiTGmGuB84GrrLXR/gdaG5zG8bzgz4wcYI4xpmlYowqNImCsdczE+SQ/qiZ6rMK1OD8nAP6LM9xI3M+VbTC1v6KGG9tgbm5/gUvbYC5rf4F722BubH9BBLTBIqk4NAtoZ4xpZYyJxZm0bVyYY6qxYBXzOWChtfapcMcTCtba+621OdbaXJyv02fW2qj+NMRaux5YbYzJC+46HVgQxpBCZRVwojEmIfi9eDpRPsljJeNwfogS3L4fxlhCxhgzGPgNcKG1dne446kpa+131tom1trc4M+MIqBX8P9ctHsPOA3AGNMeiAU2hzWimlsLDAo+Pw1YEsZYpO64rg2m9lf0cGkbzM3tL3BhG8xt7S9wdRvMje0viIA2mK+ub3gk1lq/MeZ2YCLOjP5jrLXzwxxWKJwCXA18Z4yZG9z3W2vthDDGJFW7A3g12DBeDlwf5nhqzFo7wxjzNjAHp4vsN8Co8EZ17IwxrwP5QGNjTBHwIPAoTjfSG3EaYZeHL8Ljc4S87gfigE+CPWSnW2uHhy3IY1RVTtbaqB+adISv1RhgTHAZ0jLg2mj6pPEIOd0MjAhOoLoXuCV8EUpdcWkbTO2v6OKqNphb2l/gzjaYG9tf4M42mBvbXxC5bTATZf+OIiIiIiIiIiISQpE0rExEREREREREROqYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYikMiIiIiIiIiIvWYL9wBVCU1NdW2bds23GGE1K5du0hMTAx3GCHlxpzAnXm5MSdwZ17KKXq4Ma+6zGn27NmbrbUZdXIzqRY3tr9A/1ejhRtzAnfm5cacwJ15KafoEQltsIgsDmVmZvL111+HO4yQKigoID8/P9xhhJQbcwJ35uXGnMCdeSmn6OHGvOoyJ2PMD3VyI6k2N7a/QP9Xo4UbcwJ35uXGnMCdeSmn6BEJbTANKxMRERERERERqcdUHBIRERERERERqcdUHBIRERERERERqccics4hEREREQm/8vJyioqK2Lt3b7hDOW4pKSksXLgw3GEQHx9PTk4OMTEx4Q5FRETkMCoOiYiIiEiVioqKSEpKIjc3F2NMuMM5Ljt37iQpKSmsMVhr2bJlC0VFRbRq1SqssYiIiFRFw8pEREREpEp79+6lUaNGUVsYihTGGBo1ahTVPbBERMTd1HNIREREasWuUj+bdpayqaTU2VZ6SPRQYSg09O8oIiKhUF4RYE95BXvKKthd5mz3lPvZUxZgd5n/4GPl+447r/eWVxzxuioOiYiIRKBAwFJWEfh/9u48Pqrqbvz458xMJvu+kQVIIBAgIWFfZRNEURBBK7jV3VrUqm0ftf7qo7X20Vrr0mrrVlHrBiiooCKKBhCQPSEh7EmALED2fZuZ8/vjTpIJW7ZJJjOc9+s1r3vn3nPvPScDyZ3vPed7MFkkRr0ON73oFV8uG0wWiqvPDva0CjQA/q0AACAASURBVABZ12sazr4B0esEwd5GB9RcUVrk5OQwd+5cMjIySElJ4YUXXmDNmjWOrpaiKIrdrNt3kmWZ9fxQntGh46Ts+LUkEilBNh8vsVhab7dYV7QyEovNevNSYi2vrVus57I9R3FxHUuzttNUTXlGhZveNp3jzG2t39Nq5cz9ADoh0OmsSyEQQlvX6wQ6oQX+daKpnLCWo7ms3nqcTqeV1Qvb47R1vU6QdayetcV7zxnQqbUGfJqCO43mjn1IOgFeRgMebnq8jPrzllPBIUVRFMVpSakFUOoaLdQ3mmkwW6w3E9pNh0XK5hsQi9RuVCzWmw+zlK33W6w3Krblm16WlnM27U/LN3F6xwnqzRYaTDYvs9lm3UKDSVqXZptt2qv+jPe26yZL6z/8OgEebnrtZdDh4abHaF16uFmXhpZ1d+s+96b9Bn2r7c3HuOlwt+7LrbTw0+EiCqvqzhv4Ka1pPOdn4e/pRqivO6E+7iRHB2jr1vfN677uBHoZ0esE4o898S9EcTXS+n9Wp1OZERRFUc6lpsHEU1/uY/nOXDz04FmU3+FzdOZhlAC0w5qCHyBoCaZg3d/0XlivI5q326xbj7Pd1hRsqWqU6Gobm69J83Vt34sz6qSd07aQsN0vmvaLlvKCVveUZovF5n7S9l5R+9tktsjme8yWY864D7W9v7S0vh8V0oJvyWm8jPrmII6X0UCglxEvox5PNz2eRn2r9dbvDa33Wc/hadRj1Otafabi0XN/hio4pCiKonSrk9UWUk+UUddotr4s1Jta1puWtdb92j5Lq/J11m311m21Nts784TLbvbuPWuTToDRoMOo12E0aMGYlvct655eBox63bn327w36AQNppafgfYzspz1syypbjjjZ2qmzhpo6pDN25pX3Q06wvy0AE9siDfjYoMI9fFoFewJ9XUnxMeIu+H8T6IUpStycnKYM2cOM2bMYOvWrTz00EO8/vrr1NfXM3DgQJYuXYqPjw87duzgwQcfpLq6Gnd3d9avX09xcTE33nhjc66fV199lUmTJjm4RYqiKN0jI6+c33y8h+ziau6fEccIt3xmXTrD0dWyq5SUFKZPn+zoatid1q7pDq2DCg4piqIo3ebZb/bzxqZa2LS5zbJueoGHoaWni2dTLxk3HT7uBoK9W/d28TBoT0Nse8O46YVNV9+Wp1U6ca6uv7b7ztiva93lV297Lp32dGnPrh1cMmkCRoMOd72+Oaij1zl+6JetpuFptoGjluCSFkBq2rY/M5Np40c2B3183Q29Yiibohw8eJClS5fy9NNPs3DhQr7//nu8vb3561//yosvvshjjz3GokWLWLZsGWPHjqWiogJPT0/CwsL44osvCA0N5fDhw9xwww3s3LnT0c1RFEWxK4tF8p+fsnn+2wMEe7vz4V3jmTQwhJSUAkdXTXEiKjikKIqidIvP9+TxxoYsJkcauOOyEa2GMDUFdTyahzjpe11QpS0F3jqiA70cXY026XQCD532M26LX+khJgwI7oFaKc7oT6v3kZlfYddzDov048l5CW2W69+/PxMmTGDNmjVkZmYyebL21LihoYGJEydy8OBBIiIiGDt2LAB+fn4AVFdX88ADD7Bv3z70ej2HDh2ya/0VRVEc7XRlHb9bnsamw0XMHhbOX69NIlDl9lM6oV3BISHEFcArgB54W0r53Bn7A4F3gIFAHXCHlDJDCBEPLLMpOgD4Xynly/aovKIoitI7ZeSV8+hnexkXG8Ttg+qYOTTc0VVSFMWJeXt7A1peh8suu4yPP/641f69e/ees5fbSy+9RFhYGB999BEWiwUPD48eqa+iKEpP+PHgaf5nRRqVdSaeuSaRm8b3Uz1+lU5rMzgkhNADrwGXAbnADiHEl1LKTJtijwOpUsoFQogh1vIzpZQHgRE258kDVtm5DYqiKEovUlRVzz3v7yTY28i/bhpFxs6tjq6Soih20J4ePt1twoQJ3HfffRw5coS4uDhqamrIzc1lyJAh5Ofns2PHDsaOHUtlZSWenp6Ul5fTp08fdDod7733Hmbz+afwVRRFcRb1JjPPfXOApZtzGNLHl4/unsDgcF9HV0txcu2Z6mEccERKmSWlbAA+AeafUWYYsB5ASnkAiBFCnPmYeCZwVEp5rIt1VhRFUXqpRrOFJR/upri6gTd/OYYQH3dHV0lRFBcSGhrKu+++yw033EBSUhITJkzgwIEDGI1Gli1bxgMPPEBycjKXXXYZdXV1LFmyhI8++ogJEyZw6NCh5h5IiqIozurI6UqueW0LSzfncNukGD6/b7IKDCl20Z5hZVHACZv3ucD4M8qkAQuBn4QQ44D+QDRwyqbMYuBjFEVRFJf15zWZbM8u4ZXFI0iM8nd0dRRFcQExMTFkZGQ0v7/00kvZsWPHWeXGjh3Lzz//3GrboEGD2Lp1K76+2henZ5999qxzTp8+3eEzxCiKorRFSsknO07wp9X78DIa+M+tY9SwfcWu2hMcOtegxTMnDn4OeEUIkQqkA3sAU/MJhDACVwN/OO9FhLgHuAe0p0IpKSntqJrzqKqqUm1yEq7YLldsE7hmu5y5TRtyG3k/o4ErYtzwLztMSsphwLnbdCGu2C5XbFNv0tkcjtZ9AcDbQCLafdgdUsqtQoggtPyOMUAOcL2UstR6zB+AOwEz8Bsp5bfd3UZFURTF/spqGvjDynS+yTjJJXEhvHh9MmF+KoeaYl/tCQ7lAn1t3kcD+bYFpJQVwO0AQsuAlW19NZkD7JZS2vYkakVK+SbwJkB8fLx0tSc4KSkpLvdUyhXbBK7ZLldsE7hmu5y1TbuOlfLBd1uZMiiE124f12rmMWdtU1tcsV2u2Kbeois5HK37XgHWSimvsz50a5oq7zFgvZTyOSHEY9b3jwohhqH12k4AIoHvhRCDpZQq6Y6iKIoT2ZZVzEPLUimsrOcPc4Zw95QB6JxshlfFObQn59AOYJAQItZ6M7IY+NK2gBAiwLoP4C5gozVg1OQG1JAyRVEUl3Sqoo57P9hFhL8n/7xhpNNNSa8oPaTTORyFEH7AVOA/1n0NUsoy6zHzgfes6+8B19hs/0RKWS+lzAaOWOugKIqiOAGT2cLf1x3khrd+xt2gY+WSSfxq2kAVGFK6TZs9h6SUJiHE/cC3aN2g35FS7hNC3Gvd/zowFHhfCGEGMtG6MAMghPBCe0r2q26ov6IoiuJAdY1mfvXfXVTXm/jgzvEEeBnbPkhRLk5dyeFoBgqBpUKIZGAX8KCUshoIl1IWAEgpC4QQYTbXs03Ak2vdpiiKovRyJ0pqePCTPew+XsZ1o6N56uoEfNzbM+hHUTqvXf/CpJRfA1+fse11m/WtwKDzHFsDBHehjoqiKEovJKXkic8zSD1Rxus3jyK+j5opQ1EuoCs5HN2AUcADUsptQohX0IaPPdHF67WZ89Hf35/KysoLXKb3M5vNvaYNdXV1dsnr5Yr5wVyxTeCa7XLFNkHvadfPBSbe21cPwL3J7kwILWXn1p86da7e0iZ7csU2Qe9olwo/KoqiKJ3y/tZjrNiVy28ujeOKxAhHV0dReruu5HD0AnKllNusRT9FCw4BnBJCRFh7DUUAp9t7Pes1L5jzcf/+/c0zfTmrysrKXtMGDw8PRo4c2eXzuGJ+MFdsE7hmu1yxTeD4dlXVm3jqy318mpbLqH4BvLJ4JH2DvNo+8AIc3abu4Iptgt7RrvbkHFIURVGUVrYeLebpNZnMGhrOQ7MGn7/gng+ZtPkW+PhG2PkOlJ04f1lFcW2dzuEopTwJnBBCxFv3zUQbxo/1HLda128FvrDZvlgI4S6EiEXr4b29OxqmKIqidM3e3DLm/mMTK3drD92W/2pilwNDitJRqueQoiiK0iG5pTXc99FuYoK9eGlR8vkTI+bugjUP0ejeB+OpdDj4lbY9dAjEzYJBl0G/iWBw77nKK4qDdDWHI/AA8KE1eJSFtYcR2lC05UKIO4HjwC+s59snhFhuPY8JuO9imanMx8eHqqqqHr1mSkoKL7zwAmvWrOHdd99l586dvPrqqz1aB0VRnI/FInlrUxZ/+/Ygob7ufHz3BMYPUBlZFMdQwSFFURSl3WobzNzz/i4azRbe+uUYfD3czl2wqhCW3wK+fdiT8BcumTUPig7B4e/gyHew/U3Y+iq4ecOAaS3BooB+PdsgRelBXczhmAqMOcf2Ylqmuz9z31+Av3Shyi7NbDaj1+sdXQ2lh52urOPI6SrMFonJIjGbrUuLxGSxtGxv3m9p/b55aTnH8Tbbre+LC+v4qjANdzcd7gY97gbr0k2Hu0GH0WC7XYe7m836BcppI08VZ3a6oo7fLk/jpyNFXJHQh+euHa4m9lAcSgWHFEVRlHaRUvLIZ3vZf7KCd24dy4BQn3MXNJvg09uhphjuXIfpYCkIAaHx2mvS/VBfBTmbWoJFB63fl0PitSCR6lWkKIrVo48+Sv/+/VmyZAkATz31FEIINm7cSGlpKY2NjTzzzDPMnz+/zXOlpKTwpz/9iYiICFJTU8nMzOSDDz7gH//4Bw0NDYwfP55//etf6PV61q5dy+OPP47ZbCYkJIT169ezfft2HnroIWpra/H09GTp0qXEx8e3eV3FceoazezMKWXj4UI2HirkwEn7JCc36AR6nWhZ6nWt3+sEOp2gutrC8Zoi6k0W68tMo/ms3PAdZrQNIFnXDXqBTggMeoFep9PqIprqZ91nrZ++Vf116HW0HHNG2/RntOnk8UbCCyqID/dV06p30vr9p/ifT/dS02Di2YXDWTy2rwr4KQ6ngkOKoihKu7yxMYvVafk8ckU8M4aEnb/g+qe0wM+CNyAiGQ6mnF3G3Qfi52gvKaHosBYkOqx6FSlKr/XNY3Ay3b7n7DMc5jx3wSKLFy/moYceag4OLV++nLVr1/Lwww/j5+dHUVEREyZM4Oqrr27Xl6vt27eTkZFBbGws+/fvZ9myZWzevBk3NzeWLFnChx9+yJw5c7j77rvZuHEjsbGxlJSUADBkyBA2btyIwWDg+++/5/HHH+ezzz7r+s9BsRspJYdPV7HxUCEbDxexLauYepMFN71gTP8gHrkinuToANwNTcEcXXPwpHVwxybYo2+9XSdo9xf5cyWZNVskDdZAUb3JQn2jhQazmbrGlgBS0/bmdZOF+kZtvcF07nK2vZvMZ/SGOt9+s5SYzC09nSxSYjJbmveZLfKcwaz3Mzfh52FgXGyQ9RVMQqQfbnqV0vZCcktreHtTNu9uyWFohB//vGEEcWG9I2G+oqjgkKIoitKmlIOn+evaA1yVFMGvpw08f8GMlbDlnzD2bkhe3L6TCwGhg7XXxPugoRqyN56/V1HcLOg/qWd6FZkatB5QNUVQXWRdL4baUkIKgcYJ4ObR/fVwJdXFUJDq6FooTmTkyJGcPn2a/Px8CgsLCQwMJCIigocffpiNGzei0+nIy8vj1KlT9OnTp83zjRs3jtjYWADWr1/Prl27GDt2LAC1tbWEhYXx888/M3Xq1OZyQUFBAJSXl3Prrbdy+PBhhBA0NjZ2U6uVjiipbuCnI0VsOlTIpsNFnKyoA2BgqDc3jOvH1MEhjI8Nxtu9d3z10esEnkY9nkbnGdZosQkWrf5uA/o+g9meXcL27BK+369Nkujppmd0/8DmgNGIvgF4uDlPG+1NSsnRwmrrz6mYHTml5JXVAnD75BgevWLIRf3zUXqf3vEbUlEURem1souq+c3HexjSx4+/XZd0/qelpzLhi/uh73i4/P86f0Gjd/t6FcVOhUGzIO4yCOzf9nmlhIaq1kGe6iIt8FNTrAUtzgwC1Vec93SJAEdegyFXQeK1MGA66M+Tg+liVV0E+alQsMe6TINyNWOd02qjh093uu666/j00085efIkixcv5sMPP6SwsJBdu3bh5uZGTEwMdXV17TqXt7d387qUkltvvZVnn322VZkvv/zynL/rnnjiCWbMmMGqVavIyclx+LTDF6sGk4Xdx0vZdFgLBqXnlSMl+Hu6cUlcCFMGhXDJoBCiA9VsT/ai0wl0CNz0EOqlY/qoaBaOiga0PE47c0rZnl3CtuwSXvr+EFKCUa8jua9/c8+i0f0D8eklAbruYLZI9hdUsC27hB3ZJezIKaG4ugGAEB93xsUGcveUWCbFhTA4XPUWUnof1/3fqSiKonRZVb2Je97fiV4nePOW0XgZz/Nno64clt2sDRf7xXtgsFNCxXP2KtrUEiw69I1WLmSwFiQKHgg1JWcEeYqsgZ9iMNef+zp6I3iFgHewtgyMAe8Qm23W7d4h2rq7L2lfvkay7ijsXw1pH4NnIAy9GhIXQswU0F1kTwOrCrUeQfmpLcuK3Jb9QQMgeiyMuxsiRsCfpjusqorzWbx4MXfffTdFRUVs2LCB5cuXExYWhpubGz/++CPHjh3r1HlnzpzJ/PnzefjhhwkLC6OkpITKykomTpzIfffdR3Z2dvOwsqCgIMrLy4mKigLg3XfftWMLlQuRUpJTXMPGQ4VsOlzI1qPFVDeY0esEI/sG8PCswUwZFEJSdAB6lQOnx4X5enDl8AiuHB4BQHlNIzuPlTQHi17fkMVrPx5FJyAxyp9xMVrPorExQQR6O28C5nqTmfTccrZZe1DtPlZKZb0JgOhAT6bFhzLe2s7YEG+VU0jp9VRwSFEURTkni0Xy8LJUsoqq+e8d4+gbdJ4nsBYLrLoXyo7BrWvAL6L7KmX0hvgrtJeUUHwEDq/TAkU73gKz9oQOd3/wCtKCOX7R0Ce5JfDjFdwS+GkqY/TRAlEdUBo0Cqb/Fua+CEd/0IbUZXwGu98D71AYdo0WKOo7AXQuloOhzUDQQOg3HiJ+BZEjtNxTHv6Oq6/i9BISEqisrCQqKoqIiAhuuukm5s2bx5gxYxgxYgRDhgzp1HmHDRvGM888w+zZs7FYLLi5ufHaa68xYcIE3nzzTRYuXIjFYiEsLIzvvvuORx55hFtvvZUXX3yRSy+91M6tbJuUkn+lHOWH1DrK/POYNSzcZXtilNc2suVIERsPF7HpcCG5pdpwnH5BXiwYFcWUQaFMHBiM3/lmzVQcxt/LjZlDw5k5NByA6noTe46XsT27mG3ZJbz/8zHe/ikbgMHhPs09i8bHBhHu13uHalfXm9h9vLR5OF3qiTLqTRYABoX5cPWIyOagV2SAp4Nrqygd55p/TRRFUZQu+8cPh/ku8xT/O3cYk+JCzl9w09+1vEBznof+E3uugkJAyCDtNfE+aKjRejB5Bduv51J7GNxbhsE11mrBqozPYM9/tYCVbyQkLNCGnkWN6nAQyuGqTrcOAhWkQkVey/7gOOg3wRoEGgERSSoQpHSL9PSWZNghISFs3br1nOWqqqrOe47p06efNRRs0aJFLFq06Kyyc+bMYc6cOa22TZw4kUOHDjW///Of/3zWeW+77TZuu+22CzWlU6SUPPPVfv7zUzZeBnhoWSruBh0zh4YxLymSGUPCnDp/idki2XWstLl3UOqJMiwSfNwNTBwYzK+mDWTqoBD6B3u3fTKlV/F2N3CJdagfaD1u9uaWN/csWrU7jw9+Pg5A/2Cv5p5F42OD6Rvk6bAeN2U1DezIKWV7djHbs0vIyK/AbJHNPaBuntC/ORgU5MQ9oBSliQoOKYqiKGf5dt9JXv7+MNeOiub2yTHnL3j4O/jxL5C0CMbd02P1Oyejl/ZyJDdPGDZfe9VXwsG1sG+llivp59cgoL/WmyhhoTZLUy8LFBnrS+HQt62DQZX51r1CCwT1n6QFgSJHQJ8k8PBzaJ0V5WJgsUj++EUGH207zm2TYpjqexrf2GTWpOXzVXoBX6efxNuoZ3ZCH+YlR3BJXChGQ+/vsXiipIaNhwvZdKiIDQdrqF23BSEgKTqA+2bEMXVwKCP6BqgZsFyMu0HP2BgtqHLfDDCZLWQWVDQHi77bf4oVu7TeqG56gaebHi+jQUvi7abHy5rMu/W6odV2T6N1X6t1Q/O6h/VY239bpXUWvkzL15JHZ5dy8FQlAEaDjhHRAfx62kDGxgYxql8AvqrHmuKCVHBIURTn01AD3z+FUT/B0TVxSYdOVfLbZakkR/vzlwWJ539iV5IFn90J4Ykw9+VeF+hwOHdfSPqF9qotgwNfaT2KNv8DfnoJgge1BIrCOjckpsMsZqjIh9JsKM2BEuuyNBtKsplUVwZbAay9smIuad0jyF0l0FScQ3p6OrfccgsAFosFnU6Hu7s727Ztc3DNOs5ktvDIp3tZuSePJdMH8j+Xx7NhQ2Hzl+sn5g5jW3YJq9Py+SbjJKv25OHv6cacxD7MS45kfGwQhl4SXKmsa2Tr0WI2WYeK5RTXABDp78GYcAOLpg1n8sAQp85Do3ScQa8jKTqApOgA7poyAItFcvh0FdtzSsgrraWu0UxNg4maBrN13UxVvYnCynpqGszUNpqpbdDKWGTHru2mF3i46THqddbk0XvwNuoZHRPEvOQIxsUGkxTt79S98hSlvVRwSFEU53N0PWx/g6h+RcDCbruMlJLc0loq60wMjfC9KBIJltc0cs/7O/E0Gnj9ltHnvxlqqIFlvwQELPqv43vs9HaeATDyJu1VXQz7v9ByFG14Hjb8FcISIHGBFigKHti1azVUQ+mxMwJA1vWy4y15mQB0BgjopyXgThzFkVKIm3qd1qtJBYIUJzZ8+HBSU1MBqKysxNfXOf89N5gsPLRsD1+nn+T3swdz/6WDzipj0OuYHBfC5LgQnp6fyE9HClmdVsDqtHw+2XGCEB8jVw6PYF5yJKP7BaLrwYTNJrOFvXnlbDqkBYP2nCjDbJF4GfVMHBDMrZNimDIolIGh3mzYsIHpSZE9Vjel99LpBPF9fInv07H/t1JKGswWa6CoJWhUaw0o1TaYbNatASVrmXqTGSpOceOscQyN8O01AVVF6UkqONQDjpyu5GCJmYkmM+4GFXVWlC47tgWAPid/1HpC2GFWKLNFkl1Uzb78cvblV5CRV05GXjkVddqsEzHBXiwYGc2CkVH0C3bNQIjZIvnNJ3vIK6vl47snEOF/nmSKUsLqB+FUBtz0KQTF9mxFnZ13MIy5Q3tVnoRMa6Doh2e0V8QILT9RwgII6Hv28VJqeYCaevycGQCqOtW6vLs/BMVAeAIMmasFgoJitaVfNOhbbgVyU1KI6z+p25quOCcp5UURHO9uUnasS0Ndo5klH+7mhwOn+eNVQ7lryoA2jzEadFw6JJxLh4RT12gm5eBpVqcVsGzHCd7feowIfw/mJmmBouFR/t3yuZ4oqWnuGbT5SBEVdSaEgOFR/tw7bQBTBoUyql+gUwx7U5yLEAJ3gx53g56ATtyqpaSkMDxa5cxTLl4qONSN6k1mXvn+MK9vOIpFwkt71jE2JoiJA4OZNDCE4VH+arpNRemMY1vAzRv3hhJtlqhBl3Xo8EazhcOnqloFgjILKqhpMAPazfXQPr7MTY4kMdIfvQ6+SM3n5fWHeOn7Q4zpH8jCUdFcNTwCfy/XGXP+/LcH2HCokP9bMJwxMUHnL7j9TUhfDjP+CINm9VwFXZFvHxj/K+1VdgIyP9eGnn33hPaKHgeDZkNdWesAUGONzUkE+EVpAZ9Bs1sHfwJjwTNQDflTOs3Dw4Pi4mKCg4NVgKgLpJQUFxfj4dG+mZhqGkzc/f5Othwt5i8LErlpfP8OX9PDTc8ViRFckRhBVb2J9ftPsTotn3e35PDWpmz6B3sxLymSecmRHe6hYauyrpGfs0rYdLiQTYeLyC6qBiDC34M5iRFcMkjr1aQS9iqKovRuKjjUTdJzy/ndilQOnapi0Zi+hJlPU+UVwZYjxTy/9iBwEF8PA+Njg5kcpwWLBof7qBsvRWlLXQWc3AuTfkPD9ncw7vnvBYNDdY1mDp6sJCO/nIy8Cvbll3PgZCUN1qlHvYx6EiL9uH5MXxKj/EmI9CMuzOes5JeLxvYjv6yWz1PzWLk7j8dXpfPUl/uYNSyMBSOjmTbYOZJ/ns+Xafm8sSGLm8b348bx/c5f8NgW+PZxiL8Spvyu5yp4MQjoC5Me0F4lWVpvon2r4MdnwODZEvAZMKN1ACignzZjmqJ0g+joaHJzcyksLHR0VTqtrq6u3UGZ7uTh4UF0dHSb5SrqGrlj6Q52Hy/l779IZuGoto9pi4+7gfkjopg/Iorymka+zTzJ6rR8/r3hKK/+eIRBYT7MS45kblIEA0J9Lngus0WyN7esuXfQnuNlmCwSTzc9EwcG88uJ/ZuHiqn7WkVRFOehgkN21mCy8OoPh3kt5SghPkaW3j6WGfFhpKSkMH16AgCFlfX8nFXMlqNFbDlazPf7tSEAIT5GJg4MYdLAYCYPDHHo1I2K0mud2A7SAgOmczrnKNEHv4GaEvAKoqrexP6CpiFhWiDo8OkqzNbshP6ebiRG+XH7pBiGRfqRGOVPbLB3u/MvRAZ4smR6HL+eNpCMvApW7snly9R8vk4/SaCXG1cnR7JgVDTJ0d3TVb+7ZOSV88inaYyNCeTJeQnnL1hRAMtv1WbcWvA66Jw3GNbrBQ2Aqb/XXnXl4O6nev8oDuHm5kZsrHMPHU1JSWHkyJGOrka7lNU08Mt3tpOZX8E/bxjFVUkRdr+Gv5cb14/py/Vj+lJUVc83GVqg6KXvD/Hid4dIjPJjXlIkVyVFEB2ojc3JLbUdKlZMeW0jQkBipD/3TLUOFesfoNInKIqiODEVHLKjzPwKfrcijf0FFSwcFcWTcxPOOeQk1NedeclaN17Q/uBuOVrM1qPFbD5SxOo0bdrgqABPLVAUF8LEgcGE+zn+qZfifExmC1UNktLqBoQAgQCBdV0bn60ttX1N3z9t359VrpNfUpvyLUgJ0vpeNr9v2YfNtqayTccbs37CqDNQFJjMZs/pLDKvZsW7L/HvmplkF1c3Hx/i487wKD8uGxZOQqQfCZH+RAfaJ+AqhGB4tD/DiQkd6gAAIABJREFUo/15/MqhbDpcyMrdeXy84wTvbT3GgBBvFo6K4pqRUc031r1VcVU9v/rvLgK9jPzrptHn7/1kaoAVt2rJjm/9EjzUmPweo37WinJRKKys55b/bCOrqJo3bhnNzKHh3X7NEB93bpnQn1sm9KegvJav9hawem8Bz35zgGe/OcCIvgFU1DaSZTNU7PKEcKYMClVDxRRFUVxMu4JDQogrgFcAPfC2lPK5M/YHAu8AA4E64A4pZYZ1XwDwNpCI9h3vDinlVru1oBdoNFv4d8pR/rH+MIHeRt765RguG9b+P+jRgV5cP8aL68f0RUrJ0cJqth4tYvORYtZlnmLFrlwABoZ6MzlO61k0YUAwAV7qD7KisVgkBRV15BRVk11U3bzMLq7mZEkFRnMNZT98Z/frnitwBOcO/tjLcuPXGOnPNc9vBSIYZowhqXANgwbMZ8HIKBKi/EiM9Cesh4KpbvqW5J8VdY18k17Ayt15vLDuEC+sO8S42CCuHRXFnOER+Hn0rvxEjWYL9320m6KqelbcO5FQ3wsMTfr2cTixDX7xLoQN7bE6KoqiXAwKymu56e1tFJTVsfS2sUyOC+nxOkT4e3LXlAHcNWUAx4trWJOez7f7TtE/2IubJ/Rn6uAQBoaqFAiKoiiuqs3gkBBCD7wGXAbkAjuEEF9KKTNtij0OpEopFwghhljLz7TuewVYK6W8TghhBHr3Y/QOOniykt+tSCUjr4L5IyJ5al4CgV14iiKEIC7Mh7gwH26ZGIPFIsksqGgegvbprlze33oMISAh0o9J1mFoY2OC8HZXHcFcmZSSwsp6LfhTXE2WNQiUU1RDTnE19dYcOgAebjpigr2JD/Xmbd1zBFUdYtWM75DomoM22jlb986xfd90zXPtk9aDz7W96ZbxzJ5HnBFAat0rqXVvpLN7LWnv9eZ6Rm/MYl/fG3gqfhjVBVkMDP8VXuv/wBuzjBBx9hS/PcnPw41FY/uxaGw/TpTU8EVqHiv35PHoZ+n87xf7mDUsnGtHRTFlUOhZOY0c4S9f7efnrBJeWpRMUnTA+QumfgQ73tJy4SQs6LkKKoqiXAROlNRw49s/U1rdyPt3jmPshSYE6CH9gr1YMj2OJdPjHF0VRVEUpYe0J5owDjgipcwCEEJ8AswHbINDw4BnAaSUB4QQMUKIcKAWmArcZt3XADTYrfYOZDJbeGNjFi9/fwg/Dzdev3kUVyTaf1y4TidIjPInMcqfe6YOpMFkYW9uGVusQ9De3ZzDmxuzMOgEI/oGMGlgMP2CvQn2MRLi7U6Qj5FgbyMebmoMuDOQUlJa03hW758c6/tq62xaAG56Qb8gL2JDfJg6OISYEG9ig72JDfUm3NdDy6Oz6UXI0jrq3R5XC30SHdU0+8jZDBsaSZo0h6QhsaSkHMNr9GJIeRJSP4SIJEfXsFnfIC/uv3QQ982IIy23nFW7c/kyLZ+v9hYQ7G1kXnIk146KJjHKr8efwtY1mlm1J493t+Rw1yWxLBh5gWSn+amw5mGImQIzn+qxOiqKK+piT+wcoBIwAyYp5Rjr9mVAvPUUAUCZlHKEECIG2A8ctO77WUp5b7c1TumUo4VV3PTWNmobzXx413iS+14gUK8oiqIo3ag9waEo4ITN+1xg/Bll0oCFwE9CiHFAfyAa7QamEFgqhEgGdgEPSimru1pxRzpyupLfLU8jLbecq4ZH8PT8BIJ9emamGKNBx5iYIMbEBPGbmYOobTCz81gJW44Ws+VoMa/+eATLOYbweBv1BPu4E+RtJMTHSJC3kWAfd4K9jQT7GAn2btqnLZ151iVnYLZI9hdUcLSwqiUQVFxDdmEVFXWm5nJ6naBvoCcxId6MjQkiNsS7+RUZ4In+QomUj22FH56B2GmQvQGyNzp/cOjYFm3Zb0LLNq8gbeasvcvhsj+DoXcNtxRCC9yO6BvA/7tqGBsOFbJqTy4fbTvOu1tyiAvz0fITjYgiMsCzzfPVNZqprDNRWddoXbasV9Q1UtFq39llKutMNJi1XmaXxIXw2Jwh579YTQksuwW8QrThZHrVO1FROssOPbEBZkgpi2zPK6VcZHONvwPlNruPSilH2Lkpip0cOFnBzW9vA+CTeyYwNMLPwTVSFEVRLmbtudM/17fPM8MPzwGvCCFSgXRgD2AC3IBRwANSym1CiFeAx4AnzrqIEPcA9wCEhoaSkpLS3jb0GIuUrM1pZOXhRjz0sCTZnXERFaTvbDuFUlVVVbe2abwHjE+A+ngvyhskFfWSykbrskFS0SCpbKinoqaOg2VQ2aBtN58nF4ynAfyMAl/ry8/68rVZesha5I8/utzY8+76rKSUHC2zsO2kiR0nzZTVaz98AQR5CPp4C8aE6gj3NhLuJejjrSPEU2DQCaBGezUUYsmHo/lw9ALXMjRWMGbnQ0j3UHZG3cuo/APU7lxJRv0wu7erJyWlrcHo3Z+d2/cCLZ9VkD6ZpNrPyVj1AkWhkxxcywtzA66PgqvCPNhx0sSW/BqeX3uQv609yJAgHeEeZt5O/5Zak6SmUVJrQls3SWobwdSO/E0eevByE3gawNMg8DIIwt0gxkvgZdDjadDjaxSMj6jhp00bz30SaSZp79MEVOSzZ+RzVO7I6HSbu/v3n6O4YrtcsU29SKd7YkspT7V1cqH9Mb4euNTuNVfsbm9uGb98ZzseBj0f3DWeuLALTx+vKIqiKN2tPcGhXKCvzftoIN+2gJSyArgdmm9Osq0vLyBXSrnNWvRTtODQWaSUbwJvAsTHx8vp06e3uxE9Iauwit+vSGP38RouTwjnmWuGXzh56xm0qeynd18FO0FKSUWdieKqekqqGyiqaqC4up6SqgaKq60v674TZQ2UVNef0StJMCBUMDcpknlJEQwK93VUU+zKnp+VlJKMvArW7M1nzd4C8srqMBp0TB8cxpXDIxgW6Ue/IC/7DvuTEj5eDKZKuPM7pkSOIP/ou0SWbGX6lEuct/eH2QRbjkDy4ubPp/mzskyBnLdIbEiF6Y87tJodcZV1eby4hlV78vgiNY9tp6sJ9Dbg6+GGr6+BPh7WdZul3zm2Na37uBsu3KOsvdY/DaWpMO8fjB59a5dO1Rt//9mDK7bLFdvUi3SlJ/YptAdz64QQEnjDet9kawpwSkp52GZbrBBiD1AB/FFKuclurVE6bWdOCbcv3YG/lxsf3z2BvkEulY5TURRFcVLt+Za4AxgkhIgF8oDFwI22BawzktVYcwrdBWy0BowqhBAnhBDxUsqDaF2jM3EiFotk6ZYcnl97AA83Pa8sHsHVyZEu0VtGCIG/pxv+nm4MCG27vMUiKa9tpLi6nuKqBr76aTeH6zz45w+H+cf6wwzp48vcpAjmJkUSE+Ld/Q3oxQ6erGR1Wj5r9uaTU1yDQSeYMiiE380ezGXDwvHtzlmrtr4Gh9bCnL9BpDaaoDRwOJEF30JBGkSP7r5rd6eTe6GhCvqfo2eQTg/Ji2Hzy1BRAH72z//VnfoFe/HgrEE8OGtQ7/hyvn81bPo7jLoVuhgYUhSlWVd6YgNMllLmCyHCgO+EEAeklLZd/24APrZ5XwD0k1IWCyFGA58LIRKs92ctlXKCnttd1Zt6xGUWm3l5dx1B7oLfJrtxdO/2C/YEPp/e1CZ7ccU2gWu2yxXbBK7ZLtUm59Eb2tVmcEhKaRJC3A98i5ZA8R0p5T4hxL3W/a8DQ4H3hRBmtODPnTaneAD40DpTWRbWHkbOIKeomkc+3cv2nBJmDgnj2YXDe2x67N5IpxMEehsJ9DYSFwa1x914evoETlfU8XV6AWv2FjRP350Y5ce8pEiuSoogOvDieCKWVVjF6rQC1uzN5/DpKnQCJg0M4d5pA7kisQ8BXj2QCyd3J3z/JAydB+Pubt5cFjBcW8ne4LzBoeZ8Q+cZNjbiJvjpRdj7CVzycM/Vy9UUHoJVv4ao0XDl3xxdG0VxJV3piY2UMt+6PC2EWIU2TG2jtawBrcfRaJtz1QP11vVdQoijwGBg5xnX7NU9t+2hVwTdgR8OnOLl73cTG+LLB3eN71AP9DP1ljbZkyu2CVyzXa7YJnDNdqk2OY/e0K52jS+RUn4NfH3Gttdt1rcC55xDWkqZCozpQh17nMUi+e/Px3jumwMY9IK//yKZhaOiXKK3UHcI8/Pgtsmx3DY5lvyyWr5OL2B1Wj7PfnOAZ785wMh+AcxNiuSq4RH08Xet4NqJkhrW7NXam1lQgRAwNiaIP89P4IrEiC7d+HVYbSmsuB38IuHqV1vmjAcajQEQNkxLSj3ltz1XJ3s6tgUCY8/fKygkDvpOgD0fwuSHWrVfaaf6Slh2Exjc4fr3taWiKPbS6Z7YQghvQCelrLSuzwaetjl0FnBASplrc65QoERKaRZCDEC7T8vqxvYpF/BNegG/+WQPQ/r48f4d4wj07l2TJyiKoiiKkyYf6T4nSmr4n0/T+DmrhGmDQ3nu2uFE+Lc9g5CiiQzw5K4pA7hrygCOF9ewJj2fNWkF/HlNJs98lcnYmCDmJUUwZ3gEIT00w5u9nSyva84hlHqiDIARfQN4Yu4wxwXApIQv7ofKfLjjW/A8x1S4sVNh13tgqne+L/0WCxzfqs1KdiEjb4Yv74fcHdB3XM/UzVVICZ8vgeKj8MvPwf8C09sritJhXeyJHQ6ssj6kMgAfSSnX2px+Ma2HlAFMBZ4WQpjQZo+9V0pZ0j2tUy5k5e5cfr8ijVH9Annn9rH4defQckVRFEXpJBUcspJS8tH24/zfV/sRQvDXa4dz/Zi+qrdQF/QL9mLJ9DiWTI/jaGEVa9IKWL03nye+2MeTX+5j0sAQ5iZF9NyQqy4orKznm4wC1qQVsONYCVJCQqQfj14xhLlJEY5PJrn9LTiwBmY/A9Hn6agXOxW2va4NPYuZ3LP166qig1BbAv0nXrhcwjXwzSOw5wMVHOqoza/A/i+1f0OxUx1dG0VxSZ3tiW2d4Sz5Aue97RzbPgM+60J1FTv4aNtx/t/n6UwcEMzbt47By6huvRVFUZTeSf2FAvLKann00738dKSIS+JC+Ot1SUQFqN5CbdGZG9pddmCoDw/OGsRvZsZx8FQla6y5eR5bmc4fP8/gkkEhzE2KZHZCeK95olZW08DajJOs3pvP1qPFWCQMCvPh4VmDmZsUwYDQXjLtbH4qrPt/MPgKmHj/+cv1nwxCpw0tc7bgUFO+oXMlo7bl7gvDroGMlXDFc2C8OPJdddnRH2H9nyBhwYX/DSmKoijt9p+fsvnzmkxmxIfy75tH23dmUkVRFEWxs4s6OCSlZPnOE/x5zX4sUvKXBYncOK6f6i3UHhkrmbz5XhixFYIHtvswIQRD+vgxpI8fv5s9mH35FdZZvQr4/Yo0jCt1TIsPZW5SBLOGhuPt3jP/RKWUNJgtVNaZ2JzXyLtLt/PT4SJMFklMsBf3zYhjblIk8X18e6Q+7VZXAStuA+9QuObfF86z4xkAEclacGjGH3qsinZxbAv49NFyDrVl5E2Q9pHWCyZ5cffXzdmVHYdP74CQ+LNyVSmKoiid8+oPh3lh3SHmJPbhlcUjMRp0jq6SoiiKolzQRRscKqlu4LfLU0k5WMiEAUH87bpkxw8Ncia730NvqYct/4R5L3fqFEIIEqP8SYzy57E5Q9hzoow1aQV8lZ7Pd5mn8HDTMXNIOHOTIhjRL4D6Rgu1jWbqGs3UNppbva9rtd5Spml7vc37cx1f22hG2kwoHBVQxZ2XxDIvOZKESL/eGTCUElY/qH25v+0r8Apq+5jYqbD1X9BQDUbv7q+jPUipBYf6T2pf4KL/ZC2ItOcDFRxqS2MdLLsFLCZY/CG495LecIqiKE5KSsnfvj3Iv1KOsnBkFM9fl4RBrwJDiqIoSu930QaHXv3hCJuPFPGnqxO4ZUJ/dLpe+OW/t6o8CdkbMek9MaR+BDMeB5+wLp1SCMGofoGM6hfIH68ayo6cEtbsLeCbjAK+Si/o4LnAw6DH06jHw6DDw6hvee+mI8DTzWabzmaf9pKFWdx5zYzeGRCytetd2LcSZv5v27l4msRO1XLLHN8KcbO6tXp2U3ZMS7Td1pCyJkJo09r/+AyU5kBgTHfWznlJCV/9DgpS4YZPOtQDUFEURTmblJI/rc7k3S053DCuH3+5JlHdXyqKoihO46IMDkkp+XbfSaYNDuXWSTGOro7z2bcKpIX9Qx9meMazsO0NmPmE3U6v0wnGDwhm/IBgnpw3jG3ZJRwrrsHDTYenW0sQx8NNZw0A6ZuX7m463A26LgV2UlKO9f7A0MkMWPsYDLwUJj/c/uP6TQSdmza0zFmCQ+3NN2RrxA3w418g9WPnG0LXU3YthdQPYOojED/H0bVRFEVxamaL5I+fp/Px9hPcMTmWJ+YO7f33EoqiKIpi46IMDu3LryCvrJYHZ541IYjSHukroM9wikPGw9C5sOMtuOQhLRmwnRn0OibHhTA5zu6ndl71VVqeIY8AWPAm6DrQXd3oDdFjteCQszi2RWtr6ND2H+MfDQOmQ+pHMO3Rjv2MLgbZG+HrRyDuMpj+mKNroyiK4tSOnK7k0c/S2XWslPtnxPG72YNVYEhRFEVxOhflN6Z1mafQCZg5tGtDoS5KxUchbxcM/4X2fvJDUFcOu993bL0uFk1DgUqOwrVvg09ox88ROxUK0qC21P716w7Htlh7PHXw19XIm6H8OOQ4USCsJ5zMgE9uguA4uPYt0KnZcxRFUTqj0Wzh1R8Oc+UrP3G0sIoXr0/m95fHq8CQoiiK4pQuzuDQvpOMiQki2Mfd0VVxPhkrtWXitdoyegz0vwS2vgam9k9tr3RS6kew9xOtN0zslM6dI3YqSEvLcK3erPKUFgjryJCyJkOuAg9/2POh/evlrMpOwIfXgdEHbv4UPAMdXSNFURSnlJFXztWvbuaFdYe4LCGc7x6exsJR0Y6ulqIoiqJ02kUXHDpeXMOBk5XMHhbu6Ko4Hykhfbk2G5S/zQ3QJQ9BRR5kfOa4ul0MTh+Ar38PMVNg6v90/jzRY8Dg6RxDy4435Rua3PFj3Twh8TptSvu6cvvWyxnVlmqBoYZqLTDkr77EKIqidFRdo5nnvjnA/Nc2U1RVzxu3jOa1G0cR6qseOCqKoijO7aILDq3LPAnA7GF9HFwTJ3QyHYoOwfDrWm+PmwVhCdosWBaLY+rm6hpqtDxDRm9tOFlXhgIZ3KHfBOcIDh3bAm5eEJHUueNH3gSmupYebxerxjr4+EYoydKmrA9PcHSNFEVRnM727BKufGUTr284yrWjovj+4WlcnqDuJxVFURTXcPEFh/adYkgfX/oFezm6Ks4nfQXoDDDsmtbbhYDJD0LhfjjynWPq5uq+eQQKD8DCN8HXDjeisVPhdCZUne76ubrTsa3Qdxzo3Tp3fOQoLZH1ng/sWy9nYjHDqnu0XlgLXtc+e0VRFKXdqupN/O8XGVz/xlYazBY+uHM8z1+XjL9XJ/82KYqiKEovdFEFh4qq6tl5rEQ95ekMi0UbNhY3C7yCzt6fuBD8+8JPL/d83Vzd3uWw578w5bfa1PX2EDtNW+Zsss/5ukNtKZzKgH6dyDfURAgtMXXeTig8aL+6OQsp4dvHIfMLmP2XllxhiqIoSrukHDzN5S9t5L8/H+P2yTF8+9BULhkU4uhqKYqiKIrdXVTBoR/2n8YiYXaCyjfUYce3anmFmmYpO5PeDSbep/VOOLG9Z+vmyoqOwJqHtdm6pj9uv/NGJIO7X+8eWnZ8GyA7l4zaVtIircfbxdh7aMs/YdvrMOE+mHS/o2ujKIriNEqrG/jt8lRuW7oDT6OeT++dxJPzEvB2Nzi6aoqiKIrSLS6q4NC3+04SFeDJsAg/R1fF+aSv0HK/xM85f5mRt4BHgJZ7SOm6xjotz5DeCNf+B/R2vCHVG7Qkz706OLQFdG5aAu2u8AmFQZfD3mVgNtmnbs5g7wr47glIWACzn3F0bRRFUZyClJKv0wu47KUNfJmazwOXxvHVby5hdH81u6OiKIri2i6a4FB1vYlNR4qYnRCOEMLR1XEupgbI/Bzir9QSIp+Puw+MuwcOfAWFh3qufq7q28fhVLqWJ8Y/yv7nj52qJSguO2H/c9vDsS0QNUqbdayrRt4EVafgyPddP5czyEqBz3+tzWy34A3QXTS/6hVFUTrtdEUd936wiyUf7qaPvwdf3D+Z382Ox93QhUkgFEVRFMVJXDTfGDYeKqTBZFH5hjrj6A9a/pfzDSmzNe4ebTasLf/o/nq5sn2rYOd/YNIDMPjy7rlGU2Li3ph3qKEG8vd0fUhZk0GzwTtUy93k6k6mwyc3Q8ggWPSB9v9RURRFOS8pJct3nmDWixv48WAhj14xhM+XTCYh0t/RVVMURVGUHnPRBIfWZZ4i0MuNMapbcMelrwDPwPYlQ/YJ1RIA710GFQXdXzdXVJIFX/4GosbAzCe77zphw8AruHcOLcvdARaTNvTNHvRuWu6hQ2uhusg+5+yNyo7DB9eBhx/c9Cl4Bji6RoqiKL3aiZIafvnOdh75dC9D+vix9sEp/Hr6QAz6i+YWWVEURVGAdgaHhBBXCCEOCiGOCCEeO8f+QCHEKiHEXiHEdiFEos2+HCFEuhAiVQix056Vb69Gs4X1+08xc2i4+mPfUQ3VcPBrbfp6g7F9x0y8T/tiv+3f3Vs3V2SqhxW3a7NsXfdO56dwbw+dTht2lL1Rm9WqNzm2BRDaNPb2MvJm7d/l3uX2O2dvUlOiBYYaa7XAUHcMRVQUpUu6435KCPGUECLPuj1VCHGlzb4/WK91UAjRTd1QnZPZIlm6OZvLX97I7mOl/Hl+Ap/cM4EBoT6OrpqiKIqiOESbkRIhhB54DZgDDANuEEIMO6PY40CqlDIJ+CVwZkbiGVLKEVLKLmaW7ZxtWSVU1JmYPUzNUtZhB7+Bxpr2DSlrEjQAhs2HnUuhrrz76uaKvnsSClJh/r8gsH/3Xy92qjYLXUlW91+rI45vgT7DwcOOXfrDhkLkKEj9sPcFw7qqsRY+vgFKs+GGjyD8zF/RiqI4WjffT71k3T5CSvm19XrDgMVAAnAF8C9rHS56R05Xcv0bW/nT6kzGxgSx7rfTuGViDDqdykmpKIqiXLza041mHHBESpklpWwAPgHmn1FmGLAeQEp5AIgRQvSaSMy6zJN4uumZOjjU0VVxPukrwC9Km0q9IyY/CPUVWoBIaZ/9a7TeVuPvhaFze+aasdO0ZfaGnrlee5ga4MQO++UbsjXyJjiVAQVp9j+3o1jMsPJuOPGzlnw65hJH10hRlHPr6fup+cAnUsp6KWU2cMRah4tWo9nCqz8c5spXfuJoYRUvXp/Mu7ePJSrADhMfKIqiKIqTa09wKAqwnc4o17rNVhqwEEAIMQ7oD0Rb90lgnRBilxDinq5Vt+OklKzbd4qpg0PwcFMPzDqkpkSb3Snx2o7PdhQ5Ugs8/PxvbaiUcmFlx+GLJRAxAi57uueuGzxQC/71prxDBWlgqu2e4FDidWDwgD0f2P/cjiAlfPMo7F8Nlz8LiQsdXSNFUc6vO++n7rcORXtHCNGUXLE917toZOSVc/Wrm3lh3SEuGxbOdw9PY+GoaDWDraIoiqJYCdnG8AohxC+Ay6WUd1nf3wKMk1I+YFPGD63r80ggHRgC3CWlTBNCREop84UQYcB3wANSyrO+iVpvdO4BCA0NHb18uX3ygmSVm3l6ax13DzcyOaob87e0oaqqCh8f5xrHHpG/lvhD/2bn6Jeo8h1w1v622hRYkkry3ic5EP8AJyNmdWdV7aqnPythMTEi9XG8q4+zc8xL1HlG2P0aF2rTkP0vE1Syiy2T3gPh+JxcfY+vZGDWe2ye9B6NxgsnVO7MZzU08+8Elexm68SlWPTtzKPVgzrSpr7HP2Ng1vuciL6Go3G3d3PNOs8Zf/+1hyu2qyfbNGPGjF2OGm7uCN11P2XtWVSEFjz6MxAhpbxDCPEasFVK+YH13P8BvpZSfnZGvbrl/qu3aDBLVuyvZn2ewNco+OUwI6PDDY6uVpep3z/OwxXb5YptAtdsl2qT8+gN92Dt+euYC/S1eR8N5NsWkFJWALcDCO0RTLb1hZQy37o8LYRYhdal+azgkJTyTeBNgPj4eDl9+vR2VK1tO749gF6XxX0LphHg5bgvgikpKdirTT1m6fMQEs+YudYEyWdos01yGpz+jCFFaxmy6OmO9z5ykB7/rNY9ARUH4bqlTOimnh8XbFNAPnz+I9OHhkGfxHOX6Ukf/guCBzF59jVtFu3UZ9XXAv9dwNTwql7Z06bdbUpbBinvQ+K19F34Nn178f8vp/z91w6u2C5XbFMv0i33U1LKU03HCyHeAta093rW83XL/VdPk1JSWFlPVlE12dZXVmE16XllnKoQXD8mmv935TD8vRz3oNCeXPH/qiu2CVyzXa7YJnDNdqk2OY/e0K72BId2AIOEELFAHlpywxttCwghAoAa6xj6u9BuViqEEN6ATkpZaV2fDfTgmBlYt+8U42ODHBoYckrluXBsM8z44zkDQ+0ihJZ76LM74dA3MOQq+9bRFRxaB1v+AWPucFygImaKtsze6PjgkMUMx3+GhLYDQ50WOw38orXE1L0wONQuR3/QhiHGTIFr/u00gVdFuch1y/2UECJCSllgPcUCIMO6/iXwkRDiRSASGARs79YW9oDKusZWwZ9sm2BQVb2puZzRoCM22JuRfQNJ9Czj/uuSHVhrRVEURen92gwOSSlNQoj7gW8BPfCOlHKfEOJe6/7XgaHA+0IIM5AJ3Gk9PBxYZR3PbQA+klKutX8zzi2rsIrDp6u4aXy/nrqk68iw9joffm3XzjPsGlj/NPz0MsRf2flAkysqz4NVv4LwRLj8/xxXj4C+2gxz2Rth4hLH1QPgdCbUl3dPvqEmOj2MuAE2vqB9Bs425XtBGiy7BULiYfGT/Is1AAAgAElEQVSHYHB3dI0URWmHbryfel4IMQJtWFkO8Cvr+fYJIZZbz2MC7pNSmru/pV3XYLJwvKSGrMKqlkCQdVlY2ZLHUAiIDvQkNsSH0f0DiQ3xbn5FBniit84+lpKS4qCWKEoPqDyp5fjc/yUMnQdTHwF31xtyoyhK92vXoGvrtKhfn7HtdZv1rWhPpM48Lgtw2KOadZlaT+vLEvo4qgrOK30FRI3WggZdoTfApAfg699rPUL6d3DWM1dlaoDP7tKSdf/iXXBz8EwpsVMhYyWYTdpn5ijHtmrL7gwOAYz4/+zdd3hVVdbA4d9OQgIkJKGFXkIvAULvIRQBsQ1WrKBY0HHUsZdxHLt+M+PoONgRcQRURGz0AUKXHkogSIdQQieEEEjZ3x/rRgIE0m7Pep/nPknuPXefddLuuevsvdZtsODvsHYixD3p2n0507FdMP4mKB8Jd3wH5SM8HZFSqhhccT5lrb3zMvt7HXi9pPG6Um6u5UBa5u+Jn/yJoD1HM8jNVxKzamgwjaqH0rd5daKrhRFdLZRG1UOpX6WiNhtRxZeyCjZPhbbDoHozT0dTcoe3yuzztRMhN1vO2xe/B+snw+A3JVGkF2WVUsXg+xX5LmNW0gFi6oRri9LiOpgMB9bD4LecM17s7ZDwJix+V5NDIB3gpj8LR7bA0E+g2kXvA9wvOg5WfSGzUup29FwcuxZDRD2IdPFsvyqNoEEvWVrW+wnfOHnKOApf3QDZmXDPjxBe29MRKaVUsWVm5fDc9+uZvmE/mVm5v99foVwg0dVCaVMnguva1Sa6eqgkgqqG+k2dIOUlZr8o5xsL/ynnP53vldntgT7ye5aySs6pN/0MgcFynt3jT9KBdvevMPUJ+PZOaDIArvw/uV8ppYrAb5NDB9MyWbPnOI8P8OErAp6y4TvpWtV6qHPGC64IXR6AhDfg4CaIaumccX3N0e0w8wXYPE2SE7d9C80GeToq8XvdofmeSw5ZC7uWQKN49+yv/e3ww4O+MaMt6zRMHAbHd8NdP5TdvyGllE87cTqL+8atZMWuowzrXJ+YOuEyC6haGDXCQ7StvHK9o9slMdTjT1ChMqwcC9/eBZVqQce7oeNwqOSFKw6sha1zJCm0c6HMHO79OHQdBWFR57ar3w3unw/LP4F5b8AH3aHXY9Drz56fpa6U8np+mxz636aDWAsDdUlZ8VgrS8qi45z74tjlPnlBW/xvGPqh88b1BWfSYdE7sOR9ucIz4GXo9qB31YoJi4KoVlJ3qPfjnonh6HY4ddD1S8rytLoOpj0FiV95d3IoN0eWIO5ZLksQ3fX9UUopJ0pNy2T458vZdiid94a159p2OvtReUDiRMBA1wel5mDPx+C3mbDiM7mIueD/oMXVMpuoYS/PzyzOyYakKbJcLHU9VKoNA1+DjiMgpFLBzwkMkhqSrYfCrL/A/Ldh3Tdw5d+h2UC3hq+U8i1+mxyamXSABlUr0qyGFmQrlr2r4dhOiHvKueNWrALt74SVY6DfX3yvCHBJWCuFvWe9CCf3ydr2AX+D8Fqejqxg0XGwapzUQfJE4mrXYvnoruRHcKicOG34Hga/7Z3FG62F6U9D8i8Soyu7uCmllItsP5TOnWOWczzjLGNHdKFX02qeDkmVRbm5Up+ncd9z56EBgdBiiNyObIOVn8Oar2DjD1C9hSSJ2t4C5cPdG+vZDIlj6fsya7haM7huNLS5GYKK2IE5vBbcOAY63CW1PyfcJImvwW+6fvm+Ur4u/SAs+pesaggOheAwea8QHArBlfJ9HiaJ2vO2CTv/8wDfqY3nl8mhk5lZLNl2mBE9GuoU5eJaPwkCQ6SInbN1/6Ncmfn1AxjklfUxnWf/OnlTv3sp1GonMz7qd/V0VJcXHQfLPoKUldCwp/v3v2spVKwqJ0Du0v4OWPNf2PijLDPzNovekb+ZHo9At1GejkYppYpt7Z7j3P3FCgww8f5utK0b6emQVFm1cyGc2CMX6gpStbGcn/b7i1w4WvGpJFVmvwTtbpFEUY3Wro0x4ygs/xSWfwwZR6BuF6kB2uxKCAgo2ZiN+sCoxfDraJj/f/CfLtDnKej+p6InmvxdZhp8eR3tMrIgdzDU6wb1Omvjj7Io46gUel/2sVwwb9hTEsvpB+BIOpxNh7On5GNRBVW4TGLpXDKpwZ79sHA1BATluwUW8nVRtingOZcK1QnfQq+TsPkQWTmWQbqkrHhyc2SmS7OBrvlnWLkBxFwvhY/jnpS13v7m1BGY95ocY4XKcM2/JQHhCxnjBj2l1tSOBR5KDi2WWUPuTOjW6wpVm0hham9LDiVOhDmvQJubZCmiUkr5mAW/HWLUV6uoEhrMf0d2JbpaqKdDUmVZ4gQICYcWV11+u3IV5Jyg/e2wdxWsGCPPXfk51O8BnUdCy2udm1g5vhuWjobVX0JWBjQbLEvenLXsPShY6g7F3AgznpXzi8SJcNU/3Ffr0Zut/Bz2raZcaEMpVG5zASPJwHpdpZZTva4y40onHvinzDSZwLB0NJw5CW1uhD7PQrUmBW+fmwtZpyRRdCYdzp7M97njVuDnp2T8jCPyd59vm2ibCzvdetQX8cvk0KyNqVQLC6Z9fT9MPrjSjgVS86XNTa7bR89HZXbSijG+1UK8MDnZsGoszH1N/uC7PADxz/hWAqxCpMxy2rEA+j7n3n2f2AvHd0lhRXcyRtraz3lFah5VaeTe/V/K1jnw08Mym+u6D0p+tVAppTzkx8S9PPHtWprWqMS4uzsTFV7e0yGpsiwzTWYJt7uleIWZ63SU28DX5ELSijEweSSEVocOw6X2T2S9kseVmuRoP/+dnJO0uUlmC9doVfIxLyeyHgwbD1tmS93FL6+D1tfDoDe8t+yBq2VlSlKgUTwr6/+Z+O4dZRb9nmXStGTdt1IWA6Rwef5kUc22UuNJyYybkEq+0/Uvz9lTMltv8btw+pgsvez7fOGzBAMC5HhDKsElyn8Vi7XMnzeXPr17Qm6245aT7/NL3VfY15fY5uUHCgzD736bz2TnMC/5IFe3rUVggGZ2i2X9d3JFpakLi9XVbAON+8vype4PQzk/OFncsRCmPwMHk+TN/JX/57vdpKLjYOkH8o8y2I1XeHcvlY+eKLbc7lZJ6iVOkKnkHhZ2cht8+1epdXDLVzrlWynlc8Ys2sGrv2yka3QVPh3eifDyPvZmQfmfjT9C9mlp+14SFatIh7Nuf4Rtc2XJ98J/yvLvZldCl3shOr5oF3PyurMufhe2zIJyodD1Aej2UOkSTcXR9Ap46FeJYeE7Ekf8cxKHr725L63E8ZCeCtd/CrutvNlv3FduIG+kU5POJYv2LJOaVADlKkrysH63srcUzVrpQr15KiRPg32rJWna9hb5O3NVgtNZss/ISo8F/5DJEU2ukKRQnQ6eiccYbECgG98bl5Hk0NJtR0g/k83A1jU8HYpvycqETT9JrSFXt7rs9RiMuwbWToBO97h2X650fA/MflG6SETUh5v/K98/X55uGh0nV7B2/wpN+rtvv7sWyxrcmm3ct8884bUlYZk4QU6MPLkE8NhO2q57BSpWhtu/KzsnGEopv2Ct5e0Zm/lo/jYGt67Ju8NiKV/OB5ZVK/+XOAGqNoW6nUs3TkAANB0gt2O75M3l6i/lDXKVxrLkLPa2gmeO5+bC5mmSkElZIXUW+74gtYwqVildXCVRrjzEPwttb4ZpT8OsF+T7dNU/vbuLqzPlZMt5b51Ocg68e/7F2wQEQq22cutyn9x3Yi/s+RV2L5OP+ZeiRbWSOqP1u/vfUrScbLmgu3ma3I7tlPvrdJJz6NQNMgFg6X+gdgdZmhlzg3etpMjJkoTg/L9DWgo06AU3f1l2fucL4XfJoVkbUwkNDqRHY+2EUSxbZsGZNFlf6WoNe0Pt9tLavcNw36jHk1/WaYl94TuAhfjnoecjrk+quUP97hBQTpaWuTU5tFReSD31u9D+dpg0ArYnuPe48/ttJvz8KMZmwx2Ty+70bqWUT8rOyeXZ79fz3aoUbutan1evi9EZ3Mo7HNkGu5dA/5ec+ya9cgMY8JIkWDb+KLOJZj4Pc16FNjdA5/ugdiwmN0sSSIv/DUe2QGQDGPIPmV0RXNF58ZRUlUZw+yTpjDr9WRg7GNrdBle8AmHVPR2dayV9L2UNBr9ZvN+NiDoQcYMkPkDqxuxdeS5ZtG6S1DEC31+KdiYdts2R2UFbZsrSq8AQKXTe8zFofiVUylfn99RhWYqXOB6mPgEznoeWV8vve6N4z53r5+bIKpmEN+HYDklo/WE0RPfxn+SdE/jQb2bhcnMtszemEt88Sq9UFdf6STIVsGGc6/dljPwzmTRcXohaXef6fTqDtRLvzOelgFirP8DAV/2rHWhwqFxV27HAffs8dQQObYK2Lqx1VZjmQ+SqRuJ49yeHTh+TF861EyCqFWubPU2n6s3dG4NSSpXC6bM5PDxhNXOSD/Jo/6Y8NqCpdotV3mPt19Jwo90w14wfFCKzb9reLN1qV3wm59VrvoLaHeh2eCecPSqzo28YI+eP3pYcMEZmvzfuBwv+Dkv+I7Oh+r0os/x97UJuUeTmyoXe6i1kaWBphIRJ4qNRvGPsIixFq9lG6trUiJEYvKnUxsnUc7ODts+HnDNQPlIKpbcYIjPuQ8IKfm5oNej+EHR7EPavlXPrdd9K06PwOlLOIfY26Q7oDrm5kPwzzHsDDiVDjTZw6zfQbJAmhQrgZf+ZSmfNnuMcOnlGl5QVV+YJmbXQcYT7XqxaXiNXKha9Kx0fvP2P82AyzHhGZpZEtYLhP8v0U38UHQcL/g9OH5ci1a6WV2+ovgfqDeUJCpEikKvGSbLGXdNfN0+Hnx+DU4cg7mmIe5L0RUvds2+llHKC4xlnGTluJat3H+O1P8RwR7cGng5JqXNyc2HtRGjUV5aRu1qttnDtv2XWzdqvYc1XnAqtR8jNn0nixdvPd4NDYcDfZObQtCdg2pOS5Lr6HUlo+JPfZsjFyaGfOL/xR2FL0fauhJVjpQ4WgAmEak0dyaLWksCo0Vp+Z93xO2MtHNp8rn7Q3pVyf2QDWSrZfIisLijO+0RjoHas3Aa+JommNV9Jna6F/5AuybG3yySBSyWaSntMW2ZJXdED66BaM7jpC2h5nTZ6uQy/Sg7N2niAoABDfPMoT4fiWzb9IhlhV3Ypu1BAoBT2++XPsHMRRPd2376L4/RxmP82LPtY/nFd+Xe5guJtV3ycKToO5r8lxRJbDHH9/nYvlempnioAl6f9HbD8E5lymvdC7ioZR6WV7Lpv5IrRbd/Ii6dSSvmQ/SdOc9eY5ew6ksEHt3Xgyja6HFZ5mZ0L4cQeSXi4U4VI6DYKuo1iXUIC8U3i3bv/0qreDO76SWZ7zHwBPu0vF5H7/9Uz9ZGczVqpExRZ/9zSMFe7cClabg4c3QGp62WWUWoS7Fkh3/M8FSrLeWLeDKMaraXpjTNKWeTmyIym5KmSuDm6Xe6v3R76/kXeA0S1ck5yKigEWg+VW9o+SdiuGQ8/PiRd81oPlRIP9bs7Z3/b50tSKGU5VG4IQz+W97n+OAPOyfzmHa61lllJqXRvXJWICmWsyn5prZ8kmeG6ndy733a3yhS/xe96X3IoN0ey23NegYwj8oLY70UIrerpyFyvbicIqgA75rsnObRrsWOfIa7f1+XUaidXahLHuzY5lDxVkqIZR6DPs9D7Ce1IppTyOVsPnuSuMcs5mZnNuHu60L1xGXh9VL4ncQKERECLqzwdie8xRmqRNh0ICW9JoeFNP8GAl8HW8XR0pbNzocyOGfIPz13wDQiEak3k1nrouftPH4eDGx0Jow1wYIPUrMrKkMdNAFRtki9h5EgaRdQtPLFyNkO67W2eJjOnMo5IrdHoOOj+R1leF+Hin214bTn37fW4JKfWfCXNfRK/klUlsbfLe8SSxLF7Gcx9VX6+4XXg6nfl4m9Z68BXCn6THNp2KJ0dh09xT69oT4fiW9IPShKg1+Pun+pargJ0HSV/xAc2QM0Y9+7/EsJPJMOnL8H+RMlgX/m9JA7KiqAQKZrnjrpDZ07K+vzej7t+X0XR/naZ0ZO60fktODOOwvSnJRlbo410I6vV1rn7UEopN1i16xgjx60gKCCArx/oRuva2llReaHMNCkU3W6YfzQN8ZTy4TD4DakTM/UJ+OlhYiNioHdPz1/YK6mF70BolCQOvE2FSGjQQ255cnOliHJesig1CfaulqRKnvIRF8wyioGolpQ7e1ySS8nTYPs8yM6UhGmzgbJcrMkA+Rm7mzHyfqN+N7jybdj4k1ygnfsqzHtdloK2vx2aX1V4PaZ9ifKcLbPk5zr4bbmw7011nHyE3ySHZialAnBFS603VCxJU6T1ojuXlOXXeaT8g178HtzwqWdiyHPqCMx6gQ5rJ0Kl2lI0MOYG718f7grRcTDnZUkehrlwmeae5WBzzn8B9KQ2N8OsF+XFadDrzht3088yW+j0Melu1/txvYqhlPJJc5NTeWj8amqGl+fLe7pSv6oXdFtSqiAbf5CaLrG3ezoS/1AzBu6eDivHEDntSZlJ1PNRT0dVfHtXS5JkwN98J2kYECAFnKs2Pr+RT+YJOLjp/KTRmvGQdcqxgUHOsC1E1JMu0S2GSL0fbzoPDQ6F2FvldnSHzPhbOxG+u0cKYbe5Uf6Oa7c//33ZwU2yCmXTT7LdgL9Bl/tlPFUifpMcmpV0gHb1IqkZoRnCYsmbxRDVwjP7r1BZMrvLPoL+L3qm85e1sr53+tOQmcau+jfS4Pb3XFMczVdE95GPOxe6di32riVShK9uF9ftozhCq0LzwVJEcsDfSv/CeeoITH9Kfr9qtoU7p0h3CqWU8kGTV6Xw9OR1tKoVzti7O1MtzEdnDaiyIXECVG3q/rIJ/iwgALrcx+Fl31Bt/t+h7TCo5GMX5he9IzNnOo30dCSlVz7i3OybPLm5cHynJIoObGDnrt1ED3pQzj994YJ3lWjo9wLEPyerWxLHy9KzFZ9JDaTY26mUFgKTx8v72OAw2bbbg/L9UKXiF6W69584zdqUEwzSLmXFc3QHpKyQbKwndX9I/lktHe3+fZ9IgYnDYPJIKVj2wAJ2NLqzbCeGQJbRhYS7fmnZ7qWOfXnR97v9nZBxWDr4lcbGH2F0F5km2/cvcN9cTQwpVcYZYwYbYzYbY7YaY54t4PHKxpgpxph1xpjlxpiYfI/tNMasN8YkGmNW5rv/78aYZMdzphhjIh33NzTGnHZsn2iM+ag0sX88fxtPTFpLt0ZVmHh/N00MKe92ZJucY8Te5htviH3MtsZ3y/Kkua94OpTiOfSbNOLpcp9nllK5Q0CA1O5peQ30fY5dDW+RMga+9ncQEACN+8INn8ETm+Hqf8lMr1kv0HH1kzIrv+ej8Ng6iH9WE0NO4hfJodkbZUnZwFY1PRyJj9nwnXx0V5X+S4moK8vaVn8pdVncITdXMtCjHbV1Br0BI2c7v86MrwoMkimnrkwOZZ+BlJXes6QsT+P+EFZTrlSUxKnDMGkEfHuXFNN7YD70ecq7pu8qpdzOGBMIjAauBFoBtxpjLnzReR5ItNa2Be4C3rvg8b7W2lhrbf6pELOBGMdzfgOey/fYNsf2sdbaUSWJOzfX8vrUjbw5PZmr29bi8xGdCQvxm4nnyl+tnSiFe9sN83Qkful0xTrSjW3NeFmm5SsWvwtB5WWWifIdFSKlW/R9c+GhZWxu9iA8uhaueNk/uud5kSIlh0pzpcvxeKAxZo0x5hdnBZ7frKRUGlUPpUmUF80+8HbWwrpJUL8HRNbzdDTQ4xGpwr/cDXWHDm+FL66Sonp1O8JDS6VCv7Y3PF90nLS1PL7HNePvXQ05Z7wvORQYBO1ukZlD6QeL99ykKTJbKHmqdLe7d44UBlRKKegCbLXWbrfWngW+Bq67YJtWwBwAa20y0NAYc9lp0dbaWdbabMeXvwJ1nRVwVk4uT0xay6cLdzCiR0P+Paw9IUH6Wqm8XG4uJE6UgrbhtT0djf+KewpCq0kjD2s9HU3hju+Bdd9Ax+ESt/JNUS3YX3uw7y1n9BGFJoecdKXrUWBT6cO92ImMLH7dfkRnDRVX6gY4vBnaeHjWUJ4araDpIFj+sbRZdIWcLCl+/WEPOJgE142GO3+Q5WTqYtFx8nHnQteMv2uxfKzf3TXjl0bsHVIoe903Rds+/ZDMFJo0Qupm3T8f4p7U2UJKqfzqAPmz7SmO+/JbC1wPYIzpAjTgXLLHArOMMauMMfdfYh/3ANPzfR3tuDg33xjTuzjBnjqTzchxK5myZi9PDWrOS9e0IiDAx5YlqLJp5wJIS5ElZcp1ykdA/79KO/INkz0dTeGWvC8fuz/s2TiU8mJFmRf8+5UuAGNM3pWujfm2aQW8CXKly7HOvYa1NtUYUxe4CngdcHq/6nmbD5Kda7XeUHGtnwQBQdBqqKcjOafXYzD2SlnO0+U+5469LxF+ehgOrIeW18KQf2jGuTBRraBiVVla5ooTrF1LoHpL75wOWr0Z1O0sBfC6P3zpddrWQtL3MPVJOJsO/V+SWXCBuuRCKXWRgv6RXHi5/S3gPWNMIrAeWAPkzQrqaa3dZ4yJAmYbY5Kttb+v/TXGvODYNm9N7H6gvrX2iDGmI/CDMaa1tTbtvKAk0XQ/QPXq1UlISODkWcu/VmWy40Qud8cE09qkMH9+SumO3oPS09NJSEjwdBhOpcd0aS02/YtqgaEsOViJXC/4Hvn1z8rWoWNYY8r98jTLD4aRG+idjYHKnT1Ot5VjORgVx+bEbcC2Arfz65+VH/HHYwLvOK6ivIMp6EpX1wu2ybvSteiCK12pwLvA00ClUkdbgJlJB4iqFEK7upGuGN4/5ebC+snQuJ90Z/IW9bvLG/Il70PHu53zBjvrNCS8JWOGVoOb/wutri39uGVBQAA07C3JIWudW8guJ1va2Le92XljOlv7O+DnR2X5W92OFz+efhCmPi4F8ep0hOs+8FzXP6WUL0gB8q/jrgvsy7+BI3FzN4AxxgA7HDestfscHw8aY6YgF+8WOLYdDlwN9LdW1ndYa88AZxyfrzLGbAOaASsv2OcnwCcAzZs3t03adeGuz5ez9xR8clcnrmjl+xdSEhISiI+P93QYTqXHdAmZabBoGbS/lbj+g5wSV2n5/c+q0WgYO5i4gNUQ/7xH47qkOa9Abha1bnibWtWbXXIzv/9Z+Ql/PCbwjuMqyrvvEl/pMsZcDRx0nJTEX3YnBVy5KszZHMvcTRn0qB3EggXzC93ek7whE5gn4ngS7dNS2FjnJg6WIiZXHFO1iAHEpLzJxu/e4GCNuFKNFXF8A803j6bi6X3srzmAbY3vJvtgGBxMuOzzvOln5SwlPabaWTVplraXZdMncrqi89bth53cSqezJ9l4KtLrfgfzBGZH0SMgmAPT/o8tzfIVLrSWqIMLabrlEwJzMtnRaDgpda/DbjwAGw+Uer/6++c7/PG4/PGYvMgKoKkxJhrYCwwDzpuW6eg0luGoSXQvsMBam2aMCQUCrLUnHZ8PBF5xPGcw8AzQx1qbkW+s6sBRa22OMaYR0BTYfrkAs3Lhhg+XcPpsDv8d2ZUu0V44s1Opy9n4A2SfhtjbPR1J2dGguzS3WfyeXFiLrO/piM6XmQbLP5PuXZdJDCmlipYcKs2VrmHAtcaYIUB5INwY85W19o4Ld3LhlauiZM3mbErlTM5KRlzRgT7NqhfhUDzHGzKBv/vlRwiqQKs/PEmrUrQQd8kx5cbBge9odWw2rW5+sWSzVTJPwOyXIHGs1BO66UdqNYqnVhGf7lU/Kycp8TEdrgtbPqJrVCZ0KsHzL2WprEptNXgkrSIuLLlRdC7/WaUNpc7mGdTp+YW0zzx5AH55HDZPlVlu131A4+rNaOzEXervn+/wx+Pyx2PyFtbabGPMw8BMIBD43FqbZIwZ5Xj8I6Al8KUxJgdZvj/S8fQawBQ5xSIImGCtneF47D9ACLLUDOBXR2eyOOAVY0w2kAOMstZetiXo/vRcqgPfjupOi5p+2uZZ+bfECVCtmczoVe4z4GVIngaz/wo3feHpaM63cgycOQG9nV7dRCm/U5TkUImvdCHtVJ9zbBMPPFlQYqikZiWlUikkiO6NvGhplLfLyYKkH6DFEChFYshlAgKg5yPw059gewI07lu852+eLm/e0w9IrZi+z0NwqEtCLROqNobwOrK0rNM9zht312JJ3JUiMeQWsbdLUerkqZCbA9OfhuxMGPgadHtIO9wppYrFWjsNmHbBfR/l+3wpMsPnwudtB9pdYswml7h/MlCsKrGBATD5wR7UrVyxOE9Tyjsc2Qa7l8KAvzl3KbwqXGQ9qR2a8CZ0vg8a9vR0RCLrNCwdLaU0arf3dDRKeb1Cu5U52qPmXenaBHybd6Ur72oXcqUryRiTjHQ1e9RVAefJybX8b1MqfVtEERxU6GGoPNvmwemj0OYmT0dyaW1vgbAasPjdoj8n/RBMuhsmDoMKkTDyfzDodU0MlZYx0rVsx0KpVeUM1srJW30va2FfkIa9ZXr0T4/AlPuhenMYtQh6/EkTQ0opv1M7LEATQ8p3rZ0IJkDOI5X79XgEwuvCjGfkgpo3WPMVnDoEvXTWkFJFUaSsirV2mrW2mbW2sbX2dcd9H+Vd7bLWLrXWNrXWtrDWXm+tPVbAGAnW2qudFfiqXcc4cuosA7VLWfGsnwTlI6Fxf09HcmlBIdDtQZk5tC/x8ttaC2u/htGdpTBw3xekjXhBBYRVyUTHQcZhOLTJOeMd/g0yjkADH0gOBQRAl/ulrf2gN+Du6VDtoov6SinlF3SuhfJZuTmQOFFmiIQ7r0aiKobgijDwFekMvOa/no5GVkss+beUAWjYy9PRKOUTfHbKzaykAwQHBnh9rSGvcvaULI9p/QcICvZ0NJfX6R4IriTF7S7l+PqdRssAACAASURBVG4YfyNMeQCqNpUZHX2e9v5j8zUNe8vHHQsuv11R7VosH30hOQSyPPHZ3dD9jzpbSCmllPJGOxZAWgrE3lb4tsp1Wl8v3YfnvAqnj3s2lg2T5b1C7yd0maFSReSTySFrLbM2ptKzSVUqlS/n6XB8x+bpkHXKu5eU5SkfAZ3ulq4TR3ec/1huLiz7GEZ3g11LYfDbcM8MbSPuKpH1oEojJyaHlsqywSqNnDOeqxkjs9mUUkop5Z0SJ0BIBDS/ytORlG3GwOC3ZIb4gr97Lo7cXFj0L4hqBU0HeS4OpXyMTyaHkg+cZPfRDAa2runpUHzL+u+gUm3fqPUCUvDXBMLS/5y779BmGDtYCgPX7wZ//BW6jdIZHa4WHQc7F0FOdunGsVZmDjXooVdxlFJKKVV6mSektECbG6BceU9Ho2rHQoc7YdlHcHiLZ2L4bTocSpZaQwE++XZXKY/wyb+WWUmpGAP9W0Z5OhTfkXEUts6GmOt9559keC1od4sUk0vbD/P/Dh/1kpo1Qz+GOyZLsWDletFxcCYN9q8t3TjHd0PaXt9JUCqllFLKuyX9ANmnpcOo8g79XoRyFWHmC+7ft7Ww8J/SFbf1UPfvXykf5iNZgvPN2niADvUrE1VJrw4U2cYfITfbN5aU5dfjUWkd/p/OMO81aHE1/HE5tBumM0/c6fe6Q/NLN87upfLRV+oNKaWUUsq7JU6Aas2gjjYj8RphUVIHdMtM2DLbvfvesQD2rpLuaYFB7t23Uj7O55JDKccySNqXxiDtUlY867+Tos212nk6kuKp3kwSWiFhMGwC3DRWXnCUe4VFybrt0tYd2rVY6klFtXJOXEoppZQqu45sgz2/SiFqvWjoXbo8AFUaw4znpHOYuyx6R2pb6kwypYrN55JDs5JSAbiildYbKrITKfKmvM1NvvnCOfRj+PNGaKFFBj0qOg52/wrZZ0o+xq6l0sXCV5Y2KqWUUsp7JU4AEwBth3k6EnWhoGAY/CYc2QLLP3XPPveugu0J0mFW608pVWw+9w5t1sYDNKsRRnS1UE+H4js2fA9YaHOjpyMpmYBATSZ4g+g4WdOfsrJkz08/KCcIuqRMKaWUUqWVmwNrJ0Lj/lKnUnmfpgOhyQBIeAtOHXb9/ha+4+h4fI/r96WUH/Kpd9zHTp1l+Y6jDNRZQ8Wz4Tuo3QGqNvZ0JMqXNegpV+dKurRs1xL5qMWolVJKKVVaOxZIk4vY2zwdiboUY2DQG5B1Cua+5tp9HUyG5F9kOVtIJdfuSyk/5VPJoTnJB8m1MEhb2Bfdod+kw5SvFaJW3qdCpNSsKmlyaPdS6Vzha3WvlFJKKeV9EifILJHmQzwdibqc6s2h832w6gvYv851+1n8rpxndh3lun0o5ed8Kjk0M+kAtSLKE1Mn3NOh+I4N3wFGWtgrVVrRcZCyAs6eKv5zdy2Gup1kDbpSSimlVEllnoBNP0PMjVpbxhfEPwMVKktxamudP/7x3bB+EnQYDqFVnT++UmWEzySHTp/NYeGWQwxsVQPji0WVPcFa+UcZHQeVdLaVcoLoOMjNksLUxZF5Ag5skKVpSimllFKlkfSD1EHUjlS+oUJl6PcX2LUINv7o/PGXvA8Y6PGw88dWqgzxyuRQuawTkJt73n0LthwiMyuXgbqkrOj2rYaj23VJmXKe+t0hoFzxl5btXgZYLUatlFJKqdJLHA/VmkOdDp6ORBVVxxFQIwZmvQhZp503bvpBWP0ltLsFIuo6b1ylyiCvTA6VzzwEX14riQ2HWUmpRFQoR5foKh6MzMes/w4Cg6HlNZ6ORPmL4FCo27n4yaFdiyWpVKeTa+JSSimlVNlweCvsWSaFqHU1ge8ICJTW9id2w5L/OG/cXz+E7DPQ8zHnjalUGeWVyaHM8lFSRPmDHrB0NNlZWcxJTqV/iyjKBXplyN4nNwc2TJYWkhUiPR2N8ifRcbA/EU4fL/pzdi+F2u0huKLr4lJKKaU85WyGXJTLyfJ0JP5v7QTpntr2Fk9HooorOg5aXguL3oETe0s/XuYJWPEZtLoOqjUt/XhKlXFemWnJKhcOD/0KjfrAzOc5/fEVVD29k4Gta3g6NN+xcyGkp0KbGz0difI30XFgc8+1pi9M1mnYu1qXlCmlyjxjzGBjzGZjzFZjzLMFPF7ZGDPFGLPOGLPcGBOT77Gdxpj1xphEY8zKfPdXMcbMNsZscXysnO+x5xz72myMGeT6IyzDlvwbJo+Er24o3sUTVTy5ObD2a2jcH8JreToaVRIDX5Wf4//+VvqxVnwGZ9Kg9+OlH0sp5Z3JIQAi6sCtX8P1nxJ4bBvTg5+j36H/6hWZolo/CYLDoNlgT0ei/E3dThBUoehLy1JWShFrTQ4ppcowY0wgMBq4EmgF3GqMaXXBZs8DidbatsBdwHsXPN7XWhtrrc2/RvdZYI61tikwx/E1jrGHAa2BwcAHjhiUs+VkS82TytFy4eTzQdI9STnfjvmQtleWlCnfVLkh9PgTrP/WUZOyhLJOy5KyJgOgVjunhadUWea9ySEAY7BtbuLmoPdYG9aT4Pmvw6d9Yf86T0fm3bIyYePPUmuoXAVPR6P8TVAI1O9W9OTQriWAgXpdXRqWUkp5uS7AVmvtdmvtWeBr4LoLtmmFJHiw1iYDDY0xhU2bvg4Y5/h8HPCHfPd/ba09Y63dAWx1xKCcbetsSVgMfBXu/B5O7odP+8usWeVciROgfAQ0H+LpSFRp9PozVKoFM565qAlRka35Ck4dgl46a0gpZ/Hu5BCQtC+NDSdC2NF3NNzyFZxMlQTRnFel+Ji62NbZcOaELilTrhMdBweTIP1Q4dvuWizdKbT2lVKqbKsD7Mn3dYrjvvzWAtcDGGO6AA2AvPY7FphljFlljLk/33NqWGv3Azg+RhVjf8oZVo6FsJoyWzs6DkbOhnLlYewQSJ7q6ej8R+YJ2PQzxNwo31/lu0LCYMDLsG8NrJ1Y/OfnZMHi9+TCo85MV8ppgoqykTFmMDK1ORD4zFr71gWPVwY+BxoDmcA91toNxpjywAIgxLGv76y1LxUnwFlJBwgw0L9FFIRdAw16wswXYOE/IPkXuG60LHNR56z/DipWg+h4T0ei/FV0H/m4cyHEXH/p7XKyIGUFtL/TPXEppZT3Kqitkr3g67eA94wxicB6YA2Q7Xisp7V2nzEmCphtjEm21l5uCmdR9ocj0XQ/QPXq1UlISLj8Ufig9PR0lx1XSOYhum2Zze76N7Bj4eLf7y/X6lXarH+NSl/fztYmI9lb17mdY115TJ5S2DHV2jeL5tmZrMptwUkfOvay+LMqElud9uHNqTDteZYdqUxOUNGbltQ4MJeWJ/awrv4Ijs6fX7o48tGflW/wx2MC7ziuQpND+dbIX4FcdVphjPnJWrsx32Z5a+SHGmNaOLbvD5wB+llr040x5YBFxpjp1tpfixrgrI2pdGpYhaphIXJHxSow9EN5Q/rzYzDmCuj2EPR9QTshAWSmwW8zoMNdEFik3J9SxVerHYSEy9r/yyWH9q+FrAy9qqOUUnIOVS/f13WBffk3sNamAXcDGGMMsMNxw1q7z/HxoDFmCrJEbAGQaoypZa3db4ypBRws6v4c430CfALQvHlzGx8fX7qj9EIJCQm47LjmvQFYGlz/VxpE1j//sfiBMOV+mm76jKZVHG28A5xT9smlx+QhhR7TmDegWnM6XnOfT7WwL5M/q6Jq+iF81o/edhnEv1y05+TmwgdPQ40Y2l7/hFN/F/Rn5Rv88ZjAO46rKMvKSrxG3op0xzblHLeLrlpdyq4jp0g+cJJBrWte/GDTK+ChpdBxBCz9D3zYA3YuKurQ/it5KmRnQpubPB2J8meBQTKLr7C6Q3kdzTQ5pJRSK4CmxphoY0wwUiz6p/wbGGMiHY8B3AsssNamGWNCjTGVHNuEAgOBDY7tfgKGOz4fDvyY7/5hxpgQY0w00BRY7qJjK5vyClE3GQAXJoZALlre9CV0fxiWfwxf3wZn0i/eThXu8FbYswza3+5TiSFViLodod1t8OsHcGRb0Z6zeSoc3ix1i/R3QSmnKkpyqFRr5I0xgY7p0QeB2dbaIpeln5WUCsDAVpeoxVg+HK7+Fwz/BbDwxVXwy+Nw5mRRd+F/1k+SE5S6nT0difJ30XFwdDsc33PpbXYtgapNICzq0tsopVQZYK3NBh4GZgKbgG+ttUnGmFHGmFGOzVoCScaYZKSr2aOO+2sgs6/XIgmeqdbaGY7H3gKuMMZsQWZ5v+XYXxLwLbARmAH80Vqb4+rjLFO2zJTi053uvvQ2AQEw6HW46p+wZRZ8MQROHnBfjP5i7QQwAdD2Fk9HopxtwEsQGAyzXix8W2th4TvSGbDVHwrfXilVLEVZd1SqNfKOE5FYY0wkMMUYE2Ot3XDB8wtc8z5p2WnqVQpg27rlFJZLDoh5m+gd46m78nPOrP+R35r9kaNVOxTh8NzDHWsIQzIP023bPHbXv54dTlx/eynesC7SFfzxuFxxTKHpFekMbJrxKak1+128gc2l5/aFHK7Wjc0u+n7qz8o3+OMxgX8elz8ekzex1k4Dpl1w30f5Pl+KzPC58HnbgQJ7NVtrjyBL+Qt67HXg9VKErC5n1RfScanpoMK37XwvRNSHSSOkk9nt30KN1q6O0D/k5sDar2WGVqUCVhMo31apJvR+Aua8DNvmQuMCzinz7JgP+1bD1e9q+QylXKAof1WlWiOfb5vjxpgEYDDnpkLnf/y8Ne8xnbqzZeb/eKRfU+LjmxXxcAbDnuWU//Fh2q5/WaYpDnpd6hR5mFvWEM76CxhDg6F/pUHlBq7dF96xLtIV/PG4XHJMubmw8RVahhykZUFjpybB/HRqdbuRWrFO3reD/qx8gz8eE/jncfnjMSnlEsd3w5bZEPdU0d+kNhsI90yHCbfAmEFw8zhoUmBeT+W3Yz6k7ZVzeuWfuj0Eq8fBjOdg1OJL/00t/Kd0Boy9zb3xKVVGFGVZWWnWyFd3zBjCGFMBGAAkFyWwOZtSsZaC6w1dTr0uMGoh9H4S1n0DH3STtpf+LvMErPwCWv8B3JAYUoqAAGjYW+oO2QJKiWm9IaWUUv5q9ZdS76TDXcV7Xq12cO8cOVcbfxOsGuea+PzJmvFQPhKaXenpSJSrlCsPA1+HQ8mw8vOCt0lZJeecPR6GoBD3xqdUGVFocqiUa+RrAfOMMeuQJNNsa+0vRQlsZlIqdStXoGWtSsU7IpB/GP1fhPvnSa2Tb+6Qabzph4o/lq9YORbOnoQej3g6ElWWRMfJ1byj2y9+bNcSCK9TcJFOpZRSylflZMHq/0KTKyCyXuHbXyiiDtw9HRr3hZ8fgf/9TWbjqoudPg7Jv0CbGyWBoPxXi6sgug/Mex0yjl78+KJ3JEnYcYTbQ1OqrCjKzCGstdOstc2stY0d69ex1n6Ut07eWrvUWtvUWtvCWnu9tfaY4/511tr21tq21toYa+0rRdlfroVFWw8zsFVNTGmq0NdqB/fNg35/kS5eo7vAukkFz3LwZdln4NcP5R9q7VhPR6PKkug+8nHHBTWurJXkUIMe2klCKaWUf/ltJqQfKN2b1PLhcOs30PFuWPQvmDwSsjKdFqLfSJoiXXh1GZH/MwYGvwVn0mDeG+c/dnCTJAm7PgAhJZg4oJQqkiIlh9ztdLblbHYuA1tfoktZcQSWk/XgDyyEKo3g+3th4q2Qtq/w5/qK9ZPkJKXno4Vvq5QzVW0ss4MubGl/dLv8Ttbv7pm4lFJKKVdZNRYq1YamA0s3TmCQdN294hVI+h6+vBZOHXFOjP4icQJUbwG1vafJjHKhGq2g00hYOQZSN567f9G7UC4Uuo669HOVUqXmlcmhjGyoEhpMpwaVnTdoVAsYOUvWs25PgNHdIHGi88b3lNxcWPxvqNHm8tX9lXIFY2Rp2Y6F50+J371UPjbo6Zm4lFJKKVc4tgu2zpFaQ87olmSMXNy7aRzsXwuf9YfDW0s/rj84vAVSlsusIZ2FXHb0fR5CwmHGszIT/dhOuRDecYRXNBlSyp95ZXLodJalf4soggKdHF5AoBQxe3CxtA/9YRTsS3TuPtxtyyw4vBl6PqIvnMozouMg4zAc2nTuvl1LoGJVqN7cc3EppZRSzvZ7Ieo7nTtu6z/A8J/hzEkYM+BcU4eyLHECmABoe4unI1HuVLEK9H1BShZsngZL3pffgx4PezoypfyeVyaHcoGBxe1SVhxVG8NtX0tRs3k+3hZz8XsQUQ9aD/V0JKqsathbPuZfWrZriSwp04SlUkopf5GTBWv+K8vJIuo6f/x6XeDe/0HFavDldVIns6zKzYG1X0OTAVDJhe8JlHfqdA9UbwnTn4U1X0HsrRBe29NRKeX3vDI5ZIDeTau5diflI6DXYzLzZvcy1+7LVfasgN1LoNtDUltJKU+IrCf1vPKSQ2n74NgObWGvlFLKv2yeDumpUkTaVapESxmEul2kTuaCv/tfI5Wi2J4AJ/dpIeqyKjAIBr8JJ3ZDzlno+ZinI1KqTPDK5FCFIEP5coGu31GX+yE0Cua+6psvvEvek9lPHe7ydCSqrIuOg52LICf73FR4LUatlFLKn6waK00Yml7h2v1UrAJ3fi/Lqea+Bj8+LLOWypLECXKO2+xKT0eiPKVxX6kz1HWUrPpQSrmcVyaHKrprEkxwKMQ9CTsXyhUKX3JkG2z6BTqPhJAwT0ejyrroOGk9emCtFKMODoOabT0dlVJKKeUcx3bCtrlyQS7ADRcwg0Jg6MfQ5xlI/Aq+ugFOH3f9fr3B6ePStrzNTVCuvKejUZ50zXsyg0gp5RZOaLPgfBWD3FinpOMI6fY19zVoFO87NVKWvA+BwdDlAU9HotT5dYd2LYF6XZ3TxUUppZTyBqvGSVHc9k4uRH05xkjnpsoN4adH4PNBcPskiKzvnPFzsuDUIVkql+74eOogpOe7ZRyW/TfuB437ywwOV58rJ02B7ExdUqaUUm7mle/eAtyZnwkKgT5Pw8+PwG8zoLkPTF9NPyjTbdsNg0o1PB2NUhAWBVGtYOOPcHAjxFzv6YiUUkop58jJkqK4zQZDRB337z/2NimA/fUd8Gl/aapyKbk5cOpwwYmeUwfPTwSdPlrwGMGV5HU9LAqqNoHUDXKODJKYatxfkkWN+kgNT2dLnCDFiGu3d/7YSimlLskrk0NuF3sbLH5XZg81HQQBXrna7pzln0hxth5/8nQkSp0THQfLPpLPG/T0bCxKKaW81+ljEBAEIZU8HUnRJE+VxIorC1EXJjoO7p0N42+EsVfRsM41cPZ/5xI9ecmfU4eBAupolqvoSPjUkNk/DXqcSwCFOu4Pqy6fB1e8+PlHt8PWObK0bv0kqb9kAqFuZ0kUNekvyZzSLrk7vAVSlsMVr/rObH6llPITmhwC6fQV/7x0hdg4BWJu8HREl3YmHZZ/Ci2ugmpNPR2NUufkJYcCg6F2B09Ho5RSypsc3QGbp0miZfdSmW16f4JvdFtd9QVE1JMEiCdVbw73zoGvb6Phrm9hb3lHYicKKjeAep3PfZ2XCMpL/pS2PmWVRtClEXS5T2ZS7VkO2xzJooQ3IeENKSDdKF6+T437yWyn4kqcIEmntjeXLl6llFLFpsmhPDE3wKJ3YN4b0PI6762XsuYryDwOPR/1dCRKna9BT6nHUKeTFpBUSqmyzlrYt8aREJoGB5Pk/qjW0O5WSBwPv37g/eczR7fD9nnQ9wX3FKIuTFgUjJzNwjnT6d3/Ss/MrgksBw17yq3/X+HUEfkebZsrs4s2/iDbVWvuSBT1l5lKBc1Iys/mwNqvockAqFTT9cehlFLqPF6aAfGAgAB54f/mdlj3NbS/w9MRXSwnG5aOhnrdoF4XT0ej1PkqREKvx6FWO09HopRSyhOyz8KuRTI7aPN0SNsrFw3q94BBb0DzIVAlWrY9fQwS3oLWQ51XYNkVVo2TmSzuLERdGGPICaroPcuuQqtCmxvlZq3UHsxLFK0YI0nAwBBo0P1cvaIarS+Kv/KxtXByn3anUkopD9HkUH4trpL10glvS/vMoBBPR3S+jT/Aid1w5duejkSpgvV/0dMRKKWU1zLGDAbeAwKBz6y1b13weGXgc6AxkAncY63dkO/xQGAlsNdae7Xjvm+A5o5NIoHj1tpYY0xDYBOw2fHYr9baUU4/qMw02DpbEkJbZsOZNAiqIDNG+v1FajmGVr34eVe+DaO7wvRn4NaJTg/LKbLPygyn5ldCeC1PR+MbjJHET43WUhsz6zTsWgxb50rCaPaLcgurea5WUaN4CK1GzQNzZWmaLzSHUUopP6TJofyMkROZr26A1V/KumpvYa0Uza7WTLplKKWUUspnOBI7o4ErgBRghTHmJ2vtxnybPQ8kWmuHGmNaOLbPX+jmUSThE553h7X2lnz7+CdwIt/226y1sU4/mLR955aL7VgAuVlQsRq0uhZaXC1v9stVuPwYkfUh/lmY/VdJLLW4yulhltrmqdLqveMIT0fiu8pVkGViTQbI1yf2SpJo21z4bTqsnQAYqNWO6oeSoNMI77s4q5RSZYQmhy7UuL9Mf17wD4i9vfD10e6yPQEOrIdr3/f+bmpKKaWUulAXYKu1djuAMeZr4Dogf3KoFfAmgLU22RjT0BhTw1qbaoypC1wFvA48fuHgxhgD3Az0c3rk1sKhZEj+RRJC+1bL/VUaQbdR0PwqWe5e3Jo83R6SGjPTnoboPqUvmuxsK8dCRH2Z4aKcI6IOdLhTbrk5sC/RkSyaQ05gCAGdPNgRTimlyjjNMlzIGFkak34AVnzm6WjOWfyedJ1oe0vh2yqllFLK29QB9uT7OsVxX35rgesBjDFdgAZAXsund4GngdxLjN8bSLXWbsl3X7QxZo0xZr4xpnexos3NgV1LYOYL8O/28EE3mPua4zzpr/DQMvjTahj4mtSSKUmx5sBycPW/IC0F5r9V+PbudGQb7JgPHe/yjkLU/iggEOp2hD5PwT0zWNxrvCxHU0op5RE6c6ggDXrIDKJF/5KpxOXDC32KS+1fJ10g+r+kU22VUkop31RQ9WB7wddvAe8ZYxKB9cAaINsYczVw0Fq7yhgTf4nxbwXyF+/ZD9S31h4xxnQEfjDGtLbWpp0XlDH3A/cDRFWvzvrv3qba4WVUPbKC4Kw0ck0Qxyq35XCzwRyp2pmzIVUhB9h4ADhQvO/AJTSrdQW1loxm5dkmnApr6JQx80tPTychIaFYz2m0bRz1CGDp6cacLeZz3aEkx+Tt/PGYwD+Pyx+PCfzzuPSYfIc3HJcmhy6l3wvwaT/49UOIf8azsSz5NwSHQad7PBuHUkoppUoqBaiX7+u6wL78GzgSN3fD78vEdjhuw4BrjTFDgPJAuDHmK2vtHY5tg5AZRx3zjXUGOOP4fJUxZhvQDClonX+fnwCfAHSqU8622fAGhERAi0HQ4ioCGvenavlwCigp7Txd2sJ/OtH5wHi4Z6bTl88nJCQQHx9f9Cdkn4XlI6HFEHoMut6psThLsY/JB/jjMYF/Hpc/HhP453HpMfkObziuIr36GmMGG2M2G2O2GmOeLeDxysaYKcaYdcaY5caYGMf99Ywx84wxm4wxScaYR519AC5Tp6MUVVz6H8g46rk4ju+GDd/LDKYKkZ6LQymllFKlsQJoaoyJNsYEIwmfn/JvYIyJdDwGcC+wwFqbZq19zlpb11rb0PG8uXmJIYcBQLK1NiXfWNUdRbAxxjQCmgLbLxfg2XLhcOcP8PQ2uOEzaTPvjtnTFavI8rSU5bDmS9fvrzDJP0PGYdD6N0oppcqQQpND+bprXIkUSrzVGNPqgs3yumu0Be5C2rQCZANPWGtbAt2APxbwXO/V9wU4c1Jm7njK0g9kfX+3Bz0Xg1JKKaVKxVqbDTwMzEQ6jn1rrU0yxowyxuS1mG8JJBljkpHzrqJeVBvG+UvKAOKAdcaYtcB3wChr7WWvdp0JqQaN+0otIHdrdys06AWzX4L0Q+7ff34rx0o3tUZaiFoppVTZUZRlZaXprrEfWfOOtfakMWYTUnwx/3O9V41W0OZGWPYxdH0QKtVw7/4zjsLqLyHmRoioW/j2SimllPJa1tppwLQL7vso3+dLkRk+lxsjAUi44L4RBWw3GZhc4mDdzRi4+h34sCfMfhGGflT4c1zh8FbYuRD6vajdYZVSSpUpRXnVK213DRz3NwTaA8tKFqqHxD8H2Wdg0Tvu3/fKMZB1Cno+4v59K6WUUkq5U/Xmcs6zdiLsWOCZGFZ/AQFB0P5Oz+xfKaWU8pCizBwqcXeN3wcwJgy5evXYhV0y8m3ze7eM6tWre7xSd37Navaj5vLPWGY6caZ89RKNUdzq4wE5Z+n26/ucrNKB9ZsOwaaiP9ddvKGiuiv443H54zGBfx6XHpPv8Mfj8sdjUj4m7inYMBl+eRweXOzeLq3ZZyBxAjQf4v7Z4koppZSHFSU5VJruGhhjyiGJofHW2u8vtZP83TKaN29uPV2p+zyxjeH9DnQ/uwAGv1+iIYpdfXzlWMg6QdVrXiY+Oq5E+3Q1b6io7gr+eFz+eEzgn8elx1R6WVlZpKSkkJmZ6dL9REREUL58eZfuw91ccUzly5enbt26lCvngTo2yveUqwBD/gnjb5Caj3FPuW/fm36GjCNaiFoppVSZVJTk0O/dNYC9SNHD2/JvYIyJBDKstWfJ113DkSgaA2yy1npgXZaTRNaDjnfDis+g52NQtbFr95ebA0veh1qx0LC3a/ellFJ+JiUlhUqVKtGwYUPkZcg1Tp48SaVKlVw2vic4+5istRw5coSUlBSio6OdNq7yc00HQKs/wIJ/QMwNUKWRe/a76guo3BCi492zP6WUUsqLFFpzqJTdNXoCdwL9jDGJjtsQpx+FO/R+AgKDIeEt1+9r8zQ4ug16PioFGpVSShVZZmYmVatWdWliP27HWQAAGpxJREFUSBWNMYaqVau6fBaX8kOD34SAcjD1CbAXVjNwgcNbpBB1h+FaiFoppVSZVJSZQyXurmGtXUTBNYt8T6Ua0PUBWPwe9PqzdDJzBWtlH5ENoOW1rtmHUkr5OU0MeQ/9WagSCa8N/f4CM56BpCkQc71r97fqC0ch6jtcux+llFLKS+mlkeLo+SiEVIJ5r7tuH7t/hZQV0ONPEFik3J1SSqkyoEePHpd9fMiQIRw/ftxN0SjlBp3vhVrtYMZzkHnCdfvJyoTE8dDiagiLct1+lFJKKS+myaHiqFgFuj8Myb/A3tWu2cfi96BCFYi93TXjK6WU8ricnJxiP2fJkiWXfXzatGlERkaWNCSlvE9gEFz9L0hPhbkuvDC36Wc4fQw6jnDdPpRSSikvp8mh4ur2oCRv5r7m/LEPbYbfpkOX+yG4ovPHV0op5XI7d+6kRYsWDB8+nLZt23LjjTeSkZFBw4YNeeWVV+jVqxeTJk1i27ZtDB48mI4dO9K7d2+Sk5MBSE1NZejQobRr14527dr9nhQKCwsDYP/+/cTFxREbG0tMTAwLFy4EoGHDhhw+fBiAd955h5iYGGJiYnj33Xd/j6tly5bcd999tG7dmoEDB3L69Gl3f3uUKp46HWUG0YpPXXdhbtVYqBwN0X1cM75SSinlA3TdUnGVD4dej8Hsv8KuJdDg8tP8i2XJvyGoAnS5z3ljKqVUGfbyz0ls3Jfm1DFb1Q7npWtaX3abzZs3M2bMGHr27Mk999zDBx98AEhb90WLFgHQv39/PvroI5o2bcqyZct46KGHmDt3Lo888gh9+vRhypQp5OTkkJ6eft7YEyZMYNCgQbzwwgvk5OSQkZFx3uOrVq1i7NixLFu2DGstXbt2pU+fPlSuXJktW7YwceJEPv30U26++WYmT57MHXdojRXl5fq/CJt+gl/+DPfNhYBA5419aDPsWgwDXtZC1Eoppco0fRUsic73QVgNmPOq8zpopO2Hdd9C+9shtJpzxlRKKeUR9erVo2fPngDccccdvyeEbrnlFgDS09NZsmQJN910E7GxsTzwwAPs378fgLlz5/Lggw8CEBgYSERExHljd+7cmbFjx/K3v/2N9evXX9R6ftGiRQwdOpTQ0FDCwsK4/vrrf59dFB0dTWxsLAAdO3Zk586drvkGKOVM5SNg0BuwPxFWjHHu2KvGSVc0Xc6vlFKqjNOZQyURXBHinoJpT8K2udCkf+nHXPYR5GZD9z+WfiyllFIAhc7wcZULO3TlfR0aGgpAbm4ukZGRJCYmFnvsuLg4FixYwNSpU7nzzjt56qmnuOuuu35/3F7mokVISMjvnwcGBuqyMuU7Ym6ANV/BnFeg5TUQXqv0Y2ZlwtoJ0PJqCKte+vGUUkopH6Yzh0qqw3CIqA9znTB7KDMNVo6V1vVVGjknPqWUUh6ze/duli5dCsDEiRPp1avXeY+Hh4cTHR3NpEmTAEnorF27FpDlZh9++CEghavT0s5fFrdr1y6ioqK47777GDlyJKtXn1+HJS4ujh9++IGMjAxOnTrFlClT6N27t0uOUym3MQau+ifknIWZzztnzI0/OgpR3+2c8ZRSSikfpsmhkgoKhvhnYN8aSJ5aurFWj4MzJ6DnI86JTSmllEe1bNmScePG0bZtW44ePfr7MrH8xo8fz5gxY2jXrh2tW7fmxx9/BOC9995j3rx5tGnTho4dO5KUlHTe8xISEoiNjaV9+/ZMnjyZRx999LzHO3TowIgRI+jSpQtdu3bl3nvvpX379q47WKXcpWpj6P0EJH0PW/9X+vFWjYUqjSE6rvRjKaWUUj5Ol5WVRtthsOhfMO91aH5lyQokZp+FpR9Aw97SkUMppZTPCwgI4KOPPjrvvgvr+0RHRzNjxoyLnlujRo3fE0X55RWmHj58OMOHD7/o8fzjP/744zz++OPnPd6wYUM2bNjw+9dPPvlkocehlNfp9Ris/xamPgkPLYVyFUo2zsFk2L0UrnhVZiUppZRSZZzOHCqNwCCIfw4OboQN35dsjA2T4eQ+6Plo4dsqpZRSSpVlQSFw1TtwbAcsfKfk46z6AgKDIfY2p4WmlFJK+TJNDpVW6+uhRgz/3969R0ddn3kcfz8JSKThphEMBgE1chFtUAmirKIWi22Uiu4KWLeNditVQdH1grqVtlCt1FV71Hpsi7QLXlnocqpUihooigSRYAKIpUhrgIWIVBMwhIRn/5gfbMhFgpnkN/PL53VODjO/6/Mww+ThO98LBT+Fmn1Hdq57bPn67gPhlK+1THwiItKq6vbQEZE4O+kCOOPqWO/tsg+O/Px9nwcTUV+mFWJFREQCahxqrpQUuPBe+GQTrHnuyM7duDjW6+jcSerSLCIiEnFmNsrMNpjZRjO7u4H93cxsvpm9Z2aFZjaozv5UM1ttZn+otW2qmW0xs6Lg5xu19k0J7rXBzL7estm1skumxVaPffm2I18YZO3vofJTTUQtIiJSixqH4qHfpbH5gpY8BNV7m37em49Bp56x5VlFREQksswsFXgCuBQYCIwzs4F1DrsHKHL3M4B/BR6rs/8WYH0Dl3/E3XOCn1eC+w0ExgKnAaOAJ4MYoiG9O3xtKmz+M7z34pGdu2oWHHsK9Bl+2ENFRETaCjUOxYMZXHQffPpRrOBoii2rYgXNsBtjK5+JiIhIlOUCG919k7tXAc8Do+scMxB4DcDd3wf6mFkPADPLAr4J/LqJ9xsNPO/ue939Q2BjEEN0nPldyBoSW9r+811NO2fHevjobTjru+q1LSIiUosah+LlpAuh93BY+nOo2n3449/8BXToDGfWX3FGREREIucE4KNaz0uDbbWtAcYAmFku0BvICvY9CtwJ7G/g2jcHQ9Fmmlm3I7hfcktJgbxHYg1Di3/UtHPeeSY2EfVXNRG1iIhIbVrKPl7M4OL/gJlfh8KnYfjkxo/9ZBOsXxCbayitc+vFKCIiImFpqJtK3clyHgQeM7MioBhYDVSbWR6ww91XmdmIOuf8EvhJcK2fAA8D1zXxfpjZ94HvAxx33HEUFBQ0NZ+EcfIJefRa9Qzv7u/PZ13619tfUVFBQUEBKTV7OXfVbHYeew7rVxaHEGn8HMgpSqKYE0QzryjmBNHMSzklj0TIS41D8XTiOXDKSFj2KJx9HaR1afi45U9ASjsYOqF14xMRkYSTnp5ORUVFq95z69atTJo0iblz5zZ6zLnnnstbb73VilFFXinQq9bzLGBr7QPc/TMgH8DMDPgw+BkLXB5MNp0GdDaz2e7+bXfffuB8M/sVcGCy6sPeL7jn08DTAP369fMRI0Y0I8WQDDsLnniHM7f+F+QtgdRDy9uCggJGjBgBRc9CzW56fOMueiT5fEMHc4qQKOYE0cwrijlBNPNSTskjEfLSsLJ4u+g+qPwHLH+y4f27P4bVc+CMf4HOma0bm4iIRFJNTc0RHd+zZ88vbBgC1DAUfyuBbDPra2ZHEWvwWVD7ADPrGuwD+B6w1N0/c/cp7p7l7n2C8153928H59QuJq4ASoLHC4CxZtbBzPoC2UBhSyUXqg6d4NKfwfYSWPHLxo975xnIOBV6n9d6sYmIiCQJ9RyKt545MODyWO+goTdAx2MO3V/4K6j+PDakTEREWtbCu+F/4zx85PjT4dIHG91911130bt3b2688UYApk6dipmxdOlSdu3axb59+5g2bRqjR9edi7i+goICfvjDH3LssceyYcMGzj//fJ588klSUlJIT0/ntttu49VXX+Xhhx/m6KOP5rbbbqOiooKMjAxmzZpFZmYmGzduZMKECZSVlZGamspLL71EamoqeXl5lJSUsHbtWvLz86mqqqK6upr58+eTnZ19sEeTu3PnnXeycOFCzIz77ruPq6++moKCAqZOnUpGRgYlJSWcddZZzJ49G9Mkvw1y92ozuxl4FUgFZrr7WjObEOx/ChgA/M7MaoB1wPVNuPRDZpZDbMjYZuCG4HprzezF4DrVwE3ufmStiMmkfx6cOgreeAAGfgu69jp0//a1UFoIX/+pJqIWERFpQJN6DpnZKDPbYGYbzezuBvZ3M7P5wWSIhWY2qNa+mWa2w8xK6p4XWRfeA1UVsOyRQ7dX7YnNR3TqpXBcv3BiExGRFjV27FheeOGFg89ffPFF8vPzmT9/Pu+++y5vvPEGt99+O+71pn9pUGFhIQ8//DDFxcX89a9/Zd68eQDs3r2bQYMGsWLFCoYOHcrEiROZO3cuq1at4rrrruPee+8F4JprruGmm25izZo1vPXWW2RmHtpr9amnnuKWW26hqKiIJUuWkJWVdcj+efPmUVRUxJo1a1i8eDF33HEH27ZtA2D16tU8+uijrFu3jk2bNvHmm29+6b+3tsDdX3H3U939ZHefHmx7KmgYwt2Xu3u2u/d39zHuXm8JLncvcPe8Ws+vdffT3f0Md7/c3bfV2jc9uFc/d1/YGjmGxgwufQh8P/yxXqkaTETdAb46rvVjExERSQKH7TlkZqnAE8BIYuPXV5rZAndfV+uwe4Aid7/CzPoHx18c7JsFPA78Lp6BJ7TuA2LDxgp/BcNugk7Hx7YXzYHPP4Hzbgk3PhGRtuILevi0lMGDB7Njxw62bt1KWVkZ3bp1IzMzk8mTJ7N06VJSUlLYsmUL27dv5/jjjz/s9XJzcznppJMAGDduHMuWLeOqq64iNTWVK6+8EoANGzZQUlLCyJEjgdgws8zMTMrLy9myZQtXXHEFAGlpafWuP2zYMKZPn05paSmXXHIJgwcPPmT/smXLGDduHKmpqfTo0YMLLriAlStX0rlzZ3Jzcw82JuXk5LB582aGD0/uuVwkiXXrDSPugsVT4f1XoP83AEip2QvvvQADR9fv0S0iIiJA03oO5QIb3X2Tu1cBzwN1+8IPBF4DcPf3gT5m1iN4vhT4JH4hJ4kRd8P+fbGl7QHbXwPLH4esIbGJq0VEJLKuuuoq5s6dywsvvMDYsWOZM2cOZWVlrFq1iqKiInr06EFlZWWTrlV3mNaB52lpaaSmpgLg7px22mkUFRVRVFREcXExixYtalLvpPHjx7NgwQKOPvpoxowZw+uvv37I/i+6RocOHQ4+Tk1Npbq6ukk5ibSYYTfDcQNg4Z1QtRuA7jv+DHs/g7PzQw5OREQkcTVlzqETgI9qPS8FhtY5Zg0wBlhmZrlAb2KrYmyniaKwlGpdp/a4mOPfeYbClCGklxXDrs2U9BzLx0uWhB1aXCTCcnstIYp5RTEniGZeyqn5unTpQnl5eYvfp6amptH7XHbZZUycOJGdO3eycOFC5s2bR9euXamsrGTRokX87W9/o6Ki4uD5jV1nz549FBYWUlxczIknnsicOXPIz8+vd17Pnj3Zvn07ixcvZujQoezbt4+NGzcyYMAAMjMzee6558jLy2Pv3r3U1NRQUVHB/v37KS8v58MPP6RPnz7k5+ezfv16CgsLGTJkyMHrDxkyhJkzZzJmzBh27drFkiVLuP/++/nggw+orq4+GENVVRWVlZUN5lJZWRm597UkqNT2kPcIPDMKlvwMRv6YnltfhYx+cOKwsKMTERFJWE1pHGpo1r66XyM+CDxmZkVAMbCa2OSHTRaJpVTrGpwNvxjMOXuXUr5jORxzMoOuvBNSUsOOLC4SYbm9lhDFvKKYE0QzL+XUfOvXr6dTp04tfp/y8vJG75Obm8uePXvo1asX2dnZXH/99Vx22WVceOGF5OTk0L9/f9LT0w+e39h1OnbsyLBhw5g2bRrFxcWcf/75jB8/npSUlHrnzZs3j0mTJvHpp59SXV3NrbfeSm5uLs8++yw33HADDzzwAO3bt+ell14iPT2dlJQUOnXqxMsvv8zs2bNp3749GRkZTJ8+/ZC4xo8fT1FREcOHD8fMmDFjBqeccgqlpaW0a9fu4LFHHXUUaWlpDeaSlpZWb7iaSIvpPQwGXxtbHKTHIDqXfwDnPaiJqEVERL5AUxqHSoHaSz5kAVtrH+DunwH5ABbr7/5h8NO2dTkBhlwPbz9JJ4C8RyPTMCQiIl+suPj/V0nLyMhg+fLlDR5XUVHxhdfp2LHjIRNcN3ZeTk4OS5curXdcdnZ2vaFiACUlsXUipkyZwpQpU4BDG7wOXP9Ag9CMGTMOOX/EiBGHNPo9/vjjX5iHSKsa+WN4/2WYfwP7rT0pZ1wddkQiIiIJrSlzDq0Ess2sr5kdBYwFFtQ+wMy6BvsAvgcsDRqMZPhkaN+RqvZdtEKGiIiISGvoeAxcMg18Pzu6n6eJqEVERA7jsD2H3L3azG4GXgVSgZnuvtbMJgT7nwIGAL8zsxpgHXD9gfPN7DlgBJBhZqXA/e7+m7hnkqjSu8NVz7Bu3QZy2tdfJUZERKS4uJhrr732kG0dOnRgxYoVkRtmKNJqcsbD3nI27+rG4dcFFBERaduaMqwMd38FeKXOtqdqPV4OZDdyrrrL9BvFP7apYUhERBp2+umnU1RUFHYYItFiBudMoFKToYuIiBxWU4aViYiIJJWmLOEurUOvhYiIiEjiU+OQiIhESlpaGjt37lSjRAJwd3bu3ElamnrPioiIiCSyJg0rExERSRZZWVmUlpZSVlbWoveprKyMXKNHS+SUlpZGVlZWXK8pIiIiIvGlxiEREYmU9u3b07dv3xa/T0FBAYMHD27x+7SmKOYkIiIiIoenYWUiIiIiIiIiIm2YGodERERERERERNowNQ6JiIiIiIiIiLRhloiruZhZObAh7DjiLAP4OOwg4iyKOUE084piThDNvJRT8ohiXq2ZU293P66V7iVNENH6C/RvNVlEMSeIZl5RzAmimZdySh6h12CJOiH1Bnc/O+wg4snM3lFOySGKeUUxJ4hmXsopeUQxryjmJEckcvUXRPN9rZySRxTzimJOEM28lFPySIS8NKxMRERERERERKQNU+OQiIiIiIiIiEgblqiNQ0+HHUALUE7JI4p5RTEniGZeyil5RDGvKOYkTRfV1z+KeSmn5BHFvKKYE0QzL+WUPELPKyEnpBYRERERERERkdaRqD2HRERERERERESkFSRU45CZjTKzDWa20czuDjueeDCzXmb2hpmtN7O1ZnZL2DHFi5mlmtlqM/tD2LHEg5l1NbO5ZvZ+8HoNCzumeDCzycF7r8TMnjOztLBjOlJmNtPMdphZSa1tx5jZn8zsL8Gf3cKM8ctoJK8ZwXvwPTObb2Zdw4zxSDWUU619/25mbmYZYcTWHI3lZWYTg99ba83sobDi+zIaef/lmNnbZlZkZu+YWW6YMUrriVoNpvoruUSxBotC/QXRrMGiWH9BNGuwKNZfkLg1WMI0DplZKvAEcCkwEBhnZgPDjSouqoHb3X0AcA5wU0TyArgFWB92EHH0GPBHd+8PfJUI5GZmJwCTgLPdfRCQCowNN6ovZRYwqs62u4HX3D0beC14nmxmUT+vPwGD3P0M4ANgSmsH1UyzqJ8TZtYLGAn8vbUDipNZ1MnLzC4ERgNnuPtpwM9DiKs5ZlH/tXoI+JG75wA/DJ5LxEW0BlP9lVwiVYNFqP6CaNZgs4he/QXRrMFmEb36CxK0BkuYxiEgF9jo7pvcvQp4ntiLntTcfZu7vxs8Lif2y+6EcKNqPjPLAr4J/DrsWOLBzDoD5wO/AXD3Knf/R7hRxU074Ggzawd0BLaGHM8Rc/elwCd1No8Gfhs8/i3wrVYNKg4aysvdF7l7dfD0bSCr1QNrhkZeK4BHgDuBpJzorpG8fgA86O57g2N2tHpgzdBITg50Dh53IQk/L+RLiVwNpvoreUS4Bkv6+guiWYNFsf6CaNZgUay/IHFrsERqHDoB+KjW81Ii8Eu8NjPrAwwGVoQbSVw8SuxDZn/YgcTJSUAZ8EzQVfvXZvaVsINqLnffQqw1/e/ANuBTd18UblRx08Pdt0HsPwFA95DjaQnXAQvDDqK5zOxyYIu7rwk7ljg7FfgnM1thZkvMbEjYAcXBrcAMM/uI2GdHMn5zKkcu0jWY6q+EF7kaLOL1F0S/BotE/QWRrcGiWH9BAtRgidQ4ZA1sS7rWzcaYWTrw38Ct7v5Z2PE0h5nlATvcfVXYscRRO+BM4JfuPhjYTfJ1ka0nGAM+GugL9AS+YmbfDjcqaQozu5fYsIg5YcfSHGbWEbiXWPfYqGkHdCM2ZOUO4EUza+h3WTL5ATDZ3XsBkwm+yZfIi2wNpvorKUSuBlP9lbyiUn9BpGuwKNZfkAA1WCI1DpUCvWo9zyJJu1/WZWbtiRUmc9x9XtjxxMF5wOVmtplY1/OLzGx2uCE1WylQ6u4HvlWcS6xQSXZfAz509zJ33wfMA84NOaZ42W5mmQDBn0nXpbQxZvYdIA+4xt2T/T9oJxMrjtcEnxlZwLtmdnyoUcVHKTDPYwqJfZOfVBM9NuA7xD4nAF4iNtxIoi+SNZjqr6QRxRosyvUXRLQGi1j9BdGtwaJYf0EC1GCJ1Di0Esg2s75mdhSxSdsWhBxTswWtmL8B1rv7f4YdTzy4+xR3z3L3PsRep9fdPam/DXH3/wU+MrN+waaLgXUhhhQvfwfOMbOOwXvxYpJ8ksdaFhD7ECX4839CjCVuzGwUcBdwubvvCTue5nL3Ynfv7u59gs+MUuDM4N9csvs9cBGAmZ0KHAV8HGpEzbcVuCB4fBHwlxBjkdYTuRpM9VfyiGgNFuX6CyJYg0Wt/oJI12BRrL8gAWqwdq19w8a4e7WZ3Qy8SmxG/5nuvjbksOLhPOBaoNjMioJt97j7KyHGJA2bCMwJCuNNQH7I8TSbu68ws7nAu8S6yK4Gng43qiNnZs8BI4AMMysF7gceJNaN9HpiRdg/hxfhl9NIXlOADsCfgh6yb7v7hNCCPEIN5eTuST80qZHXaiYwM1iGtAr4TjJ909hITv8GPBZMoFoJfD+8CKW1RLQGU/2VXCJVg0Wl/oJo1mBRrL8gmjVYFOsvSNwazJLs71FEREREREREROIokYaViYiIiIiIiIhIK1PjkIiIiIiIiIhIG6bGIRERERERERGRNkyNQyIiIiIiIiIibZgah0RERERERERE2jA1DomIiIiIiIiItGFqHBIRERERERERacPUOCQiIiIiIiIi0ob9HyDhdHyaNuPjAAAAAElFTkSuQmCC\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fix, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(20, 8))\\n\",\n \"\\n\",\n \"plot_hist(hist, ax=ax1)\\n\",\n \"plot_hist(hist, metrics=['auc', 'val_auc'], ax=ax2)\\n\",\n \"plot_hist(hist, metrics=['precision', 'val_precision'], ax=ax3)\\n\",\n \"plot_hist(hist, metrics=['recall', 'val_recall'], ax=ax4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Performance\\n\",\n \"\\n\",\n \"The performance of the model in terms of the PR score is only marginally better than the previous model with a score of `0.97735`. \\n\",\n \"However, the classification report shows a different combination of precision and recall for the positive class, with a precision of `0.965` and recall of `0.954`. \\n\",\n \"The confusion matrix reveals a nearly equal amount of false-positives (`9375` samples) and false-negatives (`12649` samples). \\n\",\n \"Even though the misclassification rate of `Infiltration` was reduced to `77%`, it still is very high.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 190,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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tz2FqxD/eWc3alH4W4iIhIjL55WdTUNbCvLv2nI1WIi4iIxGicjrRyX/o/K64QFxERiZEXjJteXlmT4ko6phAXERGJUZgbbYmHYU5xhbiIiEiMgYU5AOyuTv8e6gpxERGRGEVBS3xPte6Ji4iIhEpjx7aPy/emuJKOKcRFRERiNIZ4blb6R2T6VygiIpJEBWqJi4iIhFN2ZjQaMzPSfyYzhbiIiEgLffOyNGKbiIhIGOVmZbBpZ1Wqy+iQQlxERKSF3KwIdQ2aAEVERCR0inIz2aFhV0VERMJnSFEuZbuqU11GhxTiIiIiLdTUN1Bdq7HTRUREQmfMwALqGtQ7XUREJHSK87Oprm2gPs07tynERUREWijIic4pXpXml9QV4iIiIi3kZ0eHXq3cl94zmSnERUREWmicBKVCIS4iIhIujZOgqCUuIiISMkW50RDfU60QFxERCZXGy+k799amuJL2KcRFRERayM2K9k7fvje9h15ViIuIiLTQ2BKPWHrPKa4QFxERaSE/eE58b43uiYuIiIRKQXYmZrBbHdtERETCJZJhFOVkskv3xEVERMKnX342O6vSu3d6Zrwrmtlw4ODYz7j7G4koSkREJNUKcjJZvnFnqstoV1whbma3AucCq4DG0eAdUIiLiEiPVFNXn/bPicfbEj8LONLd9yWyGBERkXQxsDCnx3Rs+wjISmQhIiIi6aRkZD9295B74nuB5Wb2CtDUGnf3KxNSlYiISIr1zctiX10D1bX1TSO4pZt4Q3xh8CUiItIr1NU7EB0//aC+IQ5xd3/YzLKBI4JFH7h7el9jEBER6YLRA/MBqEzjUdvi7Z0+HXgYWA8YMNLM5uoRMxER6an65EW7gqVzD/V4O7b9DDjF3ae5+5eBU4FfJK4sERGR1OoXhHjpjr0prmT/4g3xLHf/oPGFu3+IequLiEgPlq6d2WLF27FtqZndDzwavL4AWJaYkkRERFKvX360rVq5r76DNVMn3hD/DnAFcCXRe+JvAHclqigREZFU65MbDfHd1el7Tzze3un7gJ8HX3Ezs1nAHUAEuM/db2ljnenA7UQvz29z92md2YeIiEgi5GdHyM7MYEdl+s5k1m6Im9lT7v51M1tBdKz0Ztx9YjufjQC/BmYCpcB7ZrbQ3VfFrNOPaIt+lrtvMLPBB3gcIiIi3crMKM7PSuve6R21xL8b/PerB7Dt44C17v4RgJk9AZxJdBKVRt8AnnX3DQDuvuUA9iMiIpIQ/fKy2Z7Gc4q32zvd3cuCb7cBG939YyAHKAE+6WDbw4GNMa9Lg2WxjgCKzex1M1tmZhe2tSEzu8zMlprZ0q1bt3awWxERke4xsCibrXvSd+6veB8xewPIDeYUfwW4GHiog89YG8taXpLPBCYDXyH67Pm/mtkRrT7kfq+7T3H3KYMGDYqzZBERka4pzs9mVxpPghJviJu77wXOBn7p7nOAcR18phQYGfN6BK1b76XAS+5e6e7biP6xUBJnTSIiIgnVJy8rrWcyizvEzex4os+H/y5Y1tH99PeAw81sTDDu+nm0nkTlBeBLZpZpZvnA54HVcdYkIiKSUEU5mVTsC/nY6cD3gH8BnnP3lWZ2CPBaex9w9zozmwcsJvqI2QPBZy8P3r/H3Veb2UvA+0AD0cfQ/nqgByMiItKdinIz2VfXwL66enIy028Et3ifE/898PuY1x8RHfilo88tAha1WHZPi9c/BX4aTx0iIiLJ1Dc/G4BdVbUMLgpZiJvZ7e7+PTP7DW0/J35GwioTERFJsX4xM5kNLspNcTWtddQSbxwr/bZEFyIiIpJu+gYhnq491NsNcXdvnORkKVDl7g3QNBpbToJrExERSanGSVDSddS2eHunvwLkx7zOA5Z0fzkiIiLpI91b4vGGeK67VzS+CL7Pb2d9ERGR0OsXdGzbmaZDr8Yb4pVm9rnGF2Y2GahKTEkiIiLpoU9uJpEMY0eahnhnnhNfYGaNI64NBc5NTEkiIiLpoXEmsx1pek883ufE3zOzo4AjiY6Jvsbd0/OIREREulG//Oy0nVM8rsvpwZCo84HvuvsKYLSZHcj0pCIiIqESbYmHOMSBB4Ea4PjgdSnwk4RUJCIikkaK87ND/4jZoe7+H0AtgLtX0fZUoyIiIj1KcX4228N8OR2oMbM8gqFXzexQIH1nSRcREekm/Qqy2Lm3FvdWo4+nXLy9038IvASMNLPHgC8CFyWqKBERkXTRPz+bmvoG9tbUU5ATb2wmR4fVmJkBa4CzgalEL6N/1923Jbg2ERGRlOsTjNq2u7o2fCHu7m5mz7v7ZOB3SahJREQkbRQGwV1eUcPQvnkprqa5eO+Jv2Nmxya0EhERkTSUmxWdR7xiX12KK2kt3usCJwKXm9l6oJLoJXV394mJKkxERCQdDO8XbX2n44Av8Yb47IRWISIikqaapiNNw5nM2g1xM8sFLgcOA1YA97t7+l1PEBERSZB0nlO8o3viDwNTiAb4bOBnCa9IREQkjeRlRcjOzEjLoVc7upw+zt2PBjCz+4F3E1+SiIhI+jAz+uVlsTsNL6d31BJvqliX0UVEpLcqzM1kd3X6hXhHLfESM9sdfG9AXvC6sXd6n4RWJyIikgbSdRKUdkPc3SPJKkRERCRdFednU7pjb6rLaCXewV5ERER6rf4F6TmnuEJcRESkA33zsthdlX5dwxTiIiIiHeiTm0VVbT01dQ2pLqUZhbiIiEgHYmcySycKcRERkQ40jtq2Zfe+FFfSnEJcRESkA5EMA6Cqtj7FlTSnEBcREelA40xmu6rSq4e6QlxERKQDxfnZAOyo1D1xERGRUCkuCEI8zZ4VV4iLiIh0oE9uJpEMY3ulQlxERCRUzIzi/Gy1xEVERMJoYGE2W/foETMREZHQOahvLmW7qlNdRjMKcRERkTgMLMzRPXEREZEwGlCYTXlFDe6e6lKaKMRFRETiMKgwh5r6BvbsS5/ZzBTiIiIicRhQGH1WfFsadW5TiIuIiMShadS2NHrMTCEuIiISh3QcelUhLiIiEoemy+kVupwuIiISKoOKcgDYnEZziivERURE4pCTGaE4P4utFekz4ItCXEREJE7FBdm6Jy4iIhJGAwtydE9cREQkjAYWZSvERUREwmhwUW7v6dhmZrPM7AMzW2tm17az3rFmVm9m5ySyHhERka4Y0ieXin11VKbJ0KsJC3EziwC/BmYD44DzzWzcfta7FVicqFpERES6w5A+0cfMPt2dHj3UE9kSPw5Y6+4fuXsN8ARwZhvr/V/gGWBLAmsRERHpsoP65gLwaZrMK57IEB8ObIx5XRosa2Jmw4E5wD0JrENERKRbDO+XB8CmHVUpriQqkSFubSxrOQnr7cB8d69vd0Nml5nZUjNbunXr1m4rUEREpDMaW+Kf7EqPEM9M4LZLgZExr0cAn7RYZwrwhJkBDAROM7M6d38+diV3vxe4F2DKlCnpMxu7iIj0KjmZEQYX5aRNSzyRIf4ecLiZjQE2AecB34hdwd3HNH5vZg8Bv20Z4CIiIulkWL+8tGmJJ+xyurvXAfOI9jpfDTzl7ivN7HIzuzxR+xUREUmkYf1yKduZHh3bEtkSx90XAYtaLGuzE5u7X5TIWkRERLrDqP4FvLxqMw0NTkZGW92/kkcjtomIiHTC0L651NY7ZWnwrLhCXEREpBMGFGYDULYz9ffFFeIiIiKdcNRBRQCUpkEPdYW4iIhIJwztGx3wpSwNRm1TiIuIiHRCQU4mAwqy+bi8MtWlKMRFREQ6a0RxHpt0T1xERCR8RvbPZ8P2vakuQyEuIiLSWQcPyKd0RxV19Q0prUMhLiIi0kkjivOpb/CUzyuuEBcREemkgwfkA7B+W2ovqSvERUREOungAQUAKb8vrhAXERHppIP6ROcV/+O68pTWoRAXERHppEiGkR3J4INP96S0DoW4iIjIARg1IJ+IZjETEREJnxMOG8i6bZW4e8pqUIiLiIgcgNED8tlbU8/Win0pq0EhLiIicgDGDCoEYN3W1I2hrhAXERE5AIcMjD5mtm6bQlxERCRUhvXLIytirC9P3bPiCnEREZEDEMkwhvfLY2MKB3xRiIuIiByg0QML+PvWipTtXyEuIiJygMYO7cPaLRVU19anZP8KcRERkQM0flgf6hqctVtS0xpXiIuIiBygwwZHHzN7ellpSvavEBcRETlAo4PZzFI14ItCXERE5ADlZkU4ZGABDQ2pGXpVIS4iItIFhbmZvPjXT1Oyb4W4iIhIF6z8ZDcAdfUNSd+3QlxERKQLbp5zNAAbd1Qlfd8KcRERkS448qAiANaU7U76vhXiIiIiXdD4mFkqnhVXiIuIiHRBQU4mAA+9tT7p+1aIi4iIdIPyypqk71MhLiIi0k3qk/y8uEJcRESki/496KFeuiO505IqxEVERLqosYf6K6u3JHW/CnEREZEuOmJItIf6mk+T+5iZQlxERKSLinKzOHxwIduT3LlNIS4iItINDhtcyEdbK5O6T4W4iIhIN9hVVctH2yqTOoa6QlxERKQbzBg7BIBPd1cnbZ8KcRERkW5w5JBoD/UN25P3mJlCXEREpBscPCAfgI0KcRERkXA5qG8uZrBppy6ni4iIhEpWJIMBBdls3aMQFxERCZ0+uVn8ecPOpO1PIS4iItJd7LOpSZNBIS4iItJNxg7tw469yRu1TSEuIiLSTYpyMpM6aptCXEREpJvs2FtDblbyolUhLiIi0k1GFOcTMUva/hIa4mY2y8w+MLO1ZnZtG+9fYGbvB19vmVlJIusRERFJpEiG0eDJ21/CQtzMIsCvgdnAOOB8MxvXYrV1wDR3nwjcCNybqHpEREQSzQwaPHkpnsh+8McBa939IwAzewI4E1jVuIK7vxWz/jvAiAPZUW1tLaWlpVRXJ+8Be5GuyM3NZcSIEWRlZaW6FBHpRhlmJDHDExriw4GNMa9Lgc+3s/4lwIttvWFmlwGXAYwaNarV+6WlpRQVFTF69GgsifciRA6Eu1NeXk5paSljxoxJdTki0o0yktwST+Q98bbStM0jM7MTiYb4/Lbed/d73X2Ku08ZNGhQq/erq6sZMGCAAlxCwcwYMGCArhyJ9EAZZtT3kMvppcDImNcjgE9armRmE4H7gNnuXn6gO1OAS5jo91WkZ2q8nO7uSfn/PJEt8feAw81sjJllA+cBC2NXMLNRwLPAN939wwTW0mNs376dmTNncvjhhzNz5kx27NjR5np33HEHEyZMYPz48dx+++1Ny88991wmTZrEpEmTGD16NJMmTQLgsccea1o+adIkMjIyWL58OQCzZs2ipKSE8ePHc/nll1NfXw/AQw89xKBBg5o+c9999wHw2muvNdtWbm4uzz//PAAXXXQRY8aMaXqvcR9r1qzh+OOPJycnh9tuuy2uY7n66qs56qijmDhxInPmzGHnzuh4xS+//DKTJ0/m6KOPZvLkybz66qtNn9nfsWzYsIETTzyRY445hokTJ7Jo0aJmNezevZvhw4czb968pmVf+tKXmo5j2LBhnHXWWQDs2rWL008/vWk/Dz74YMc/WBHpETKC4E5aY9zdE/YFnAZ8CPwduD5YdjlwefD9fcAOYHnwtbSjbU6ePNlbWrVqVatlPdXVV1/tN998s7u733zzzX7NNde0WmfFihU+fvx4r6ys9NraWp8xY4Z/+OGHrda76qqr/Ec/+lGr5e+//76PGTOm6fWuXbvc3b2hocHPPvtsf/zxx93d/cEHH/Qrrrii3XrLy8u9uLjYKysr3d197ty5vmDBglbrbd682d99912/7rrr/Kc//Wlcx7J48WKvra11d/drrrmm6Vz86U9/8k2bNjV9ftiwYR0ey6WXXup33XWXu7uvXLnSDz744Gb1XXnllX7++efv93jPPvtsf/jhh93d/aabbmqqZcuWLV5cXOz79u1r9Zne9Hsr0lvcueRDP3j+b722rr5bt7u/fEzoc+Luvsjdj3D3Q939pmDZPe5+T/D9t9y92N0nBV9TEllPop111llMnjyZ8ePHc++90aflCgsLm95/+umnueiiiwDYvHkzc+bMoaSkhJKSEt566622NtnKCy+8wNy5cwGYO3duUws31urVq5k6dSr5+flkZmYybdLNLiwAAAyKSURBVNo0nnvuuWbruDtPPfUU559/fqvPP/74482W9+nTB4C6ujpqamo6dYno6aefZvbs2eTn57e73uDBgzn22GNb9dZu71hOOeUUMjOjd4SmTp1KaWkpAMcccwzDhg0DYPz48VRXV7Nv3752j8XM2L17NxBtSTd+HmDZsmVs3ryZU045pc3a9+zZw6uvvtrUEjcz9uzZg7tTUVFB//79m+oUkZ4tIyP6b0qynhXvcf+y/Og3K1n1ye5u3ea4YX344enjO1zvgQceoH///lRVVXHsscfyta99bb/rXnnllU2BVF9fT0VFBRC9RLtnz55W6992222cfPLJbN68maFDhwIwdOhQtmzZ0mrdCRMmcP3111NeXk5eXh6LFi1iypTmfx+9+eabDBkyhMMPP7zV55988kleeOGFZstOPfVU3n33XWbPns0555zTtPyZZ57hjTfe4IgjjuAXv/gFI0eObPa5J554gquuuqrZsuuvv54f//jHzJgxg1tuuYWcnJz9nqd4jgWi5/7cc89ttfyZZ57hmGOOabaPto7lhhtu4JRTTuGXv/wllZWVLFmyBICGhga+//3v8+ijj/LKK6+0WeNzzz3HjBkzmv5AmDdvHmeccQbDhg1jz549PPnkk2RkaHBEkd6gsY2TrB7q+pelG915552UlJQwdepUNm7cyN/+9rf9rvvqq6/yne98B4BIJELfvn2BaLguX7681dfJJ58cdx1jx45l/vz5zJw5s+kecMuWYMvWdqM//vGP5OfnM2HChGbLFy9eTFlZGfv27Wu6x3z66aezfv163n//fU4++eSmKwSNysrKWLFiBaeeemrTsptvvpk1a9bw3nvvsX37dm699dYuH8tNN91EZmYmF1xwQbPlK1euZP78+fznf/5nh8fy+OOPc9FFF1FaWsqiRYv45je/SUNDA3fddRennXZaqz9OYrU8l4sXL2bSpEl88sknLF++nHnz5jW18kWkZ2sccjVZId7jWuLxtJgT4fXXX2fJkiW8/fbb5OfnM336dKqrq5tdeo7nkaKOWuJDhgyhrKyMoUOHUlZWxuDBg9vcziWXXMIll1wCwHXXXceIEZ+No1NXV8ezzz7LsmXLWn3uiSeeaDPcITpAyRlnnMELL7zAzJkzGTBgQNN7l156KfPnN39C8KmnnmLOnDnNLpE3XkXIycnh4osvbtWJrbPH8vDDD/Pb3/6WV155pdm5Li0tZc6cOTzyyCMceuihHR7L/fffz0svvQTA8ccfT3V1Ndu2bePtt9/mzTff5K677qKiooKamhoKCwu55ZZbACgvL+fdd99tdrviwQcf5Nprr8XMOOywwxgzZgxr1qzhuOOO6/BYRSTcMiy5l9PVEu8mu3btori4mPz8fNasWcM777wDwJAhQ1i9ejUNDQ3N/qGfMWMGd999NwD19fVNLbWOWuJnnHEGDz/8MBANsDPPPLPNehovs2/YsIFnn322WTAvWbKEo446qlkYQvTS8YIFCzjvvPOallVUVFBWVgZEw3/RokUcddRRAE3LARYuXMjYsWObba+t1n7jZ9yd559/vlWLvzPH8tJLL3HrrbeycOHCZvfcd+7cyVe+8hVuvvlmvvjFL8Z1LKNGjWq6XL569Wqqq6sZNGgQjz32GBs2bGD9+vXcdtttXHjhhU0BDrBgwQK++tWvkpub27QsdlubN2/mgw8+4JBDDunwOEUk/JJ9OT2hvdMT8ZWuvdOrq6t91qxZfvTRR/s555zj06ZN89dee80XLFjghxxyiE+bNs2vuOIKnzt3rru7f/rpp37GGWf4hAkTvKSkxN9666249rNt2zY/6aST/LDDDvOTTjrJy8vL3d1906ZNPnv27Kb1TjjhBB87dqxPnDjRlyxZ0mwbc+fO9bvvvrvVtl977TX//Oc/32zZp59+6lOmTPGjjz7ax40b5/PmzWvqEX7ttdf6uHHjfOLEiT59+nRfvXp10+fWrVvnw4YN8/r65j00TzzxRJ8wYYKPHz/eL7jgAt+zZ4+7u5eVlfnw4cO9qKjI+/bt68OHD2/qSb6/Yzn00EN9xIgRXlJS4iUlJf7tb3/b3d1vvPFGz8/Pb1peUlLimzdvbvdYVq5c6V/4whd84sSJXlJS4osXL251ftrqjT9t2jR/8cUXmy3btGmTz5w5s+k4H3300Vbbck+P31sR6V5bdlf7itKdSeudbp7MQV67wZQpU3zp0qXNlq1evbpVK1Ak3en3VkTiZWbLvI0nuHQ5XUREJKQU4iIiIiGlEBcREQmpHhPiYbu3L72bfl9FpDv0iBDPzc2lvLxc/zBKKHgwn3jsY2kiIgeiRwz2MmLECEpLS9m6dWuqSxGJS25ubqvn9EVEOqtHhHhWVhZjxoxJdRkiIiJJ1SMup4uIiPRGCnEREZGQUoiLiIiEVOiGXTWzrcDH3bjJgcC2btxeb6Xz2HU6h12nc9h1Ooddl4hzeLC7D2q5MHQh3t3MbGlb49FK5+g8dp3OYdfpHHadzmHXJfMc6nK6iIhISCnERUREQkohDvemuoAeQuex63QOu07nsOt0Drsuaeew198TFxERCSu1xEVEREKq14S4mc0ysw/MbK2ZXdvG+2Zmdwbvv29mn0tFneksjnN4QXDu3jezt8ysJBV1prOOzmHMeseaWb2ZnZPM+sIinvNoZtPNbLmZrTSz3ye7xnQXx//Pfc3sN2b2l+AcXpyKOtOVmT1gZlvM7K/7eT85meLuPf4LiAB/Bw4BsoG/AONarHMa8CJgwFTgj6muO52+4jyHXwCKg+9n6xx2/hzGrPcqsAg4J9V1p9tXnL+L/YBVwKjg9eBU151OX3Gew+uAW4PvBwHbgexU154uX8CXgc8Bf93P+0nJlN7SEj8OWOvuH7l7DfAEcGaLdc4EHvGod4B+ZjY02YWmsQ7Pobu/5e47gpfvAJqmq7l4fg8B/i/wDLAlmcWFSDzn8RvAs+6+AcDddS6bi+ccOlBkZgYUEg3xuuSWmb7c/Q2i52R/kpIpvSXEhwMbY16XBss6u05v1tnzcwnRv0LlMx2eQzMbDswB7kliXWETz+/iEUCxmb1uZsvM7MKkVRcO8ZzDXwFjgU+AFcB33b0hOeX1CEnJlB4xFWkcrI1lLbvlx7NObxb3+TGzE4mG+AkJrSh84jmHtwPz3b0+2gCSNsRzHjOBycAMIA9428zecfcPE11cSMRzDk8FlgMnAYcCL5vZm+6+O9HF9RBJyZTeEuKlwMiY1yOI/nXZ2XV6s7jOj5lNBO4DZrt7eZJqC4t4zuEU4IkgwAcCp5lZnbs/n5wSQyHe/5+3uXslUGlmbwAlgEI8Kp5zeDFwi0dv8K41s3XAUcC7ySkx9JKSKb3lcvp7wOFmNsbMsoHzgIUt1lkIXBj0KJwK7HL3smQXmsY6PIdmNgp4FvimWjxt6vAcuvsYdx/t7qOBp4F/UoC3Es//zy8AXzKzTDPLBz4PrE5yneksnnO4geiVDMxsCHAk8FFSqwy3pGRKr2iJu3udmc0DFhPtlfmAu680s8uD9+8h2hP4NGAtsJfoX6ESiPMc/hswALgraEnWuSZSaBLnOZQOxHMe3X21mb0EvA80APe5e5uPAvVGcf4u3gg8ZGYriF4anu/umt0sYGaPA9OBgWZWCvwQyILkZopGbBMREQmp3nI5XUREpMdRiIuIiISUQlxERCSkFOIiIiIhpRAXEREJKYW4SC8TzI623Mz+GsxS1a+bt3+Rmf0q+P4GM/vn7ty+iHxGIS7S+1S5+yR3n0B0AocrUl2QiBwYhbhI7/Y2MZMymNnVZvZeMP/xj2KWXxgs+4uZPRosO93M/mhmfzazJcGoXiKSRL1ixDYRac3MIkSH1bw/eH0KcDjRaSoNWGhmXwbKgeuBL7r7NjPrH2ziD8BUd3cz+xZwDfD9JB+GSK+mEBfpffLMbDkwGlgGvBwsPyX4+nPwupBoqJcATzcOuenujXMojwCeDOZIzgbWJaV6EWmiy+kivU+Vu08CDiYavo33xA24ObhfPsndD3P3+4PlbY3P/EvgV+5+NPBtIDcJtYtIDIW4SC/l7ruAK4F/NrMsopNh/KOZFQKY2XAzGwy8AnzdzAYEyxsvp/cFNgXfz01q8SIC6HK6SK/m7n82s78A57n7o2Y2Fng7mIWuAviHYHarm4Dfm1k90cvtFwE3AAvMbBPwDjAmFccg0ptpFjMREZGQ0uV0ERGRkFKIi4iIhJRCXEREJKQU4iIiIiGlEBcREQkphbiIiEhIKcRFRERCSiEuIiISUv8fyWXSpHKY3fgAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Average PR Score 0.9773575192238478\\n\"\n ]\n }\n ],\n \"source\": [\n \"pred_val_score = predict_proba(model_class_weights, X_val)\\n\",\n \"plot_pr_curve(y_val.label_is_attack.values, pred_val_score)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 290,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.991 0.993 0.992 1348471\\n\",\n \" 1 0.965 0.954 0.960 274823\\n\",\n \"\\n\",\n \" accuracy 0.986 1623294\\n\",\n \" macro avg 0.978 0.974 0.976 1623294\\n\",\n \"weighted avg 0.986 0.986 0.986 1623294\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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misclassifiedtotalpercent_misclassified
Infilteration12538161930.774285
SQL Injection290.222222
Brute Force -Web3610.049180
Brute Force -XSS1230.043478
Benign937513484710.006952
DoS attacks-Slowloris610990.005460
Bot47286190.001642
DDoS attacks-LOIC-HTTP50576190.000868
SSH-Bruteforce2187590.000107
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 12538 16193 0.774285\\n\",\n \"SQL Injection 2 9 0.222222\\n\",\n \"Brute Force -Web 3 61 0.049180\\n\",\n \"Brute Force -XSS 1 23 0.043478\\n\",\n \"Benign 9375 1348471 0.006952\\n\",\n \"DoS attacks-Slowloris 6 1099 0.005460\\n\",\n \"Bot 47 28619 0.001642\\n\",\n \"DDoS attacks-LOIC-HTTP 50 57619 0.000868\\n\",\n \"SSH-Bruteforce 2 18759 0.000107\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pred_val = predict(model_class_weights, X_val)\\n\",\n \"print_binary_performance(y_val, y_val.label_is_attack, pred_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.3 Model Comparison\\n\",\n \"\\n\",\n \"|Model|PR Score|Precision Positive|Recall Positive|False-Positives|False-Negatives|\\n\",\n \"|---|---|---|---|---|---|\\n\",\n \"|No class-weights|0.97718|0.992|**0.939**|1956|**16898**|\\n\",\n \"|Class-weights|0.97735|0.965|**0.954**|9375|**12649**|\\n\",\n \"\\n\",\n \"Using the class weights during training, we obtain a reduction in precision of `~2.7%` but an increase in recall of `~1.6%`. \\n\",\n \"Consequently, the number of false-negatives are reduced, while the number of false-positives are increased by an acceptable amount. \\n\",\n \"As the primary goal of our classifier is to detect as many attacks as possible, we choose to continue using the second approach, applying the class weights during training.\\n\",\n \"\\n\",\n \"Both models suffer from a high misclassification rate in regards to the attack category of `Infiltration`, most likely due to the fact that benign traffic and infiltration traffic is very similar.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Model Training with Hyperparameter Optimization\\n\",\n \"\\n\",\n \"In this section we will use the [Hyperopt](https://github.com/hyperopt/hyperopt) library to train and evaluate various model architectures in combination with different hyperparameter configurations. For this purpose, we define a training method that accepts the model parameters from the Hyperopt library, dynamically creating the model, performing training and returning the lowest validation loss.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def train_dynamic_model(args):\\n\",\n \" print('\\\\nRun') \\n\",\n \" print('==========')\\n\",\n \" print('Parameters:\\\\n{}'.format(args))\\n\",\n \" \\n\",\n \" epochs = int(args['epochs'])\\n\",\n \" batch_size = int(args['batch_size'])\\n\",\n \" nr_layers = int(args['nr_layers'])\\n\",\n \" nr_units = int(args['nr_units'])\\n\",\n \" activation = args['activation']\\n\",\n \" dropout_rate = args['dropout_rate']\\n\",\n \" model_name = 'models/{}.h5'.format(uuid.uuid4())\\n\",\n \" \\n\",\n \" if activation == 'elu':\\n\",\n \" kerner_initializer = 'he_normal'\\n\",\n \" dropout_layer = layers.Dropout(dropout_rate)\\n\",\n \" elif activation == 'selu':\\n\",\n \" kerner_initializer = 'lecun_normal'\\n\",\n \" dropout_layer = layers.AlphaDropout(dropout_rate)\\n\",\n \" else:\\n\",\n \" raise ValueError('Invalid activation \\\"{}\\\" supplied.'.format(opt_args['name']))\\n\",\n \" \\n\",\n \" opt_name = args['optimizer']['name']\\n\",\n \" opt_lr_mult = args['optimizer']['lr_mult']\\n\",\n \" \\n\",\n \" if opt_name == 'sgd':\\n\",\n \" optimizer = optimizers.SGD(lr=(0.01 * opt_lr_mult), momentum=0.9, nesterov=True)\\n\",\n \" elif opt_name == 'adam':\\n\",\n \" optimizer = optimizers.Adam(lr=(0.001 * opt_lr_mult))\\n\",\n \" elif opt_name == 'nadam':\\n\",\n \" optimizer = optimizers.Nadam(lr=(0.002 * opt_lr_mult))\\n\",\n \" else:\\n\",\n \" raise ValueError('Invalid optimizer \\\"{}\\\" supplied.'.format(opt_args['name']))\\n\",\n \" \\n\",\n \" K.clear_session()\\n\",\n \" gc.collect()\\n\",\n \" \\n\",\n \" model = create_model(input_dims=X_train.shape[1], \\n\",\n \" nr_layers=nr_layers, \\n\",\n \" nr_units=nr_units, \\n\",\n \" activation=activation, \\n\",\n \" kerner_initializer=kerner_initializer,\\n\",\n \" optimizer=optimizer,\\n\",\n \" dropout_layer=dropout_layer)\\n\",\n \" \\n\",\n \" mc = callbacks.ModelCheckpoint(filepath=model_name, save_best_only=True, verbose=0)\\n\",\n \" \\n\",\n \" lr_scheduler = callbacks.ReduceLROnPlateau(factor=0.2, patience=5)\\n\",\n \" \\n\",\n \" hist = model.fit(x=X_train, \\n\",\n \" y=y_train_is_attack, \\n\",\n \" validation_data=(X_val, y_val.label_is_attack.values),\\n\",\n \" batch_size=batch_size,\\n\",\n \" epochs=epochs,\\n\",\n \" class_weight=class_weights,\\n\",\n \" callbacks=[\\n\",\n \" lr_scheduler,\\n\",\n \" mc\\n\",\n \" ],\\n\",\n \" verbose=2)\\n\",\n \" \\n\",\n \" best_loss = np.amin(hist.history['val_loss']) \\n\",\n \" print('Best loss: {}'.format(best_loss))\\n\",\n \" print('Model: {}'.format(model_name))\\n\",\n \" \\n\",\n \" return {\\n\",\n \" 'loss': best_loss,\\n\",\n \" 'status': STATUS_OK,\\n\",\n \" 'model_name': model_name\\n\",\n \" }\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.1 Search-Space Definition and Training\\n\",\n \"\\n\",\n \"In order to try a variety of different model architectures and hyperparameters, we have to define the search spaces for each of our parameters. These search spaces will be used to choose values for the corresponding parameters. \\n\",\n \"The following search spaces are utilized:\\n\",\n \"* Number of layers: uniform integer space in the interval of `[2, 6]`.\\n\",\n \"* Number of units: uniform integer space in the interval of `[100, 1000]` with step size `100`.\\n\",\n \"* Activation function: choice of `elu` and `selu` activation functions.\\n\",\n \"* Dropout rate: uniform space in the interval of `[0, 0.5]`.\\n\",\n \"* Optimizer: choice of `SGD`, `Adam` or `Nadam`.\\n\",\n \"* Learning rate multiplier: log-uniform space in the interval of `[-0.5, 1]`. This value is used to multiply the default learning rate of the chosen optimizer.\\n\",\n \"\\n\",\n \"Training is performed on `50` models, with `30` epochs per model and a rather large batch-size of `4096`, which is chosen to keep the training time per model reasonable.\\n\",\n \"\\n\",\n \"We use the [Tree of Parzen Estimators (TPE)](https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf) search algorithm, provided by Hyperopt, to optimize the search process. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"trials = Trials()\\n\",\n \"\\n\",\n \"space = { \\n\",\n \" 'epochs': hp.choice('epochs', [30]), \\n\",\n \" 'batch_size': hp.choice('batch_size', [4096]),\\n\",\n \" 'nr_layers': hp.quniform('nr_layers', 2, 6, 1), \\n\",\n \" 'nr_units': hp.quniform('nr_units', 100, 1000, 100), \\n\",\n \" 'activation': hp.choice('activation', ['elu', 'selu']),\\n\",\n \" 'dropout_rate': hp.uniform('dropout_rate', 0, 0.5),\\n\",\n \" 'optimizer': hp.choice('optimizer', [\\n\",\n \" {\\n\",\n \" 'name': 'sgd',\\n\",\n \" 'lr_mult': hp.loguniform('sgd_lr_rate_mult', -0.5, 1),\\n\",\n \" }, \\n\",\n \" {\\n\",\n \" 'name': 'adam',\\n\",\n \" 'lr_mult': hp.loguniform('adam_lr_rate_mult', -0.5, 1),\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'name': 'nadam',\\n\",\n \" 'lr_mult': hp.loguniform('nadam_lr_rate_mult', -0.5, 1),\\n\",\n \" }\\n\",\n \" ])\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"best_run = fmin(fn=train_dynamic_model,\\n\",\n \" space=space,\\n\",\n \" algo=tpe.suggest,\\n\",\n \" max_evals=50,\\n\",\n \" trials=trials)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[Output omitted for brevity]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Training Results\\n\",\n \"\\n\",\n \"After completion of the hyperparameter search process, we plot the chosen parameter spaces. The selected values of the ten best performing models are colorized. \\n\",\n \"From the graphs we can infer the following optimal parameter spaces:\\n\",\n \"* Number of layers: the optimal number of layers seems to be in the interval of `[4, 6]`.\\n\",\n \"* Number of units: the optimal number of units per layer seems to be in the interval of `[300, 400]`.\\n\",\n \"* Activation function: the activation function `elu` should be preferred, as it was used for all of the best ten models.\\n\",\n \"* Dropout rate: a dropout rate `<0.3` seems to perform the best.\\n\",\n \"* Optimizer: the `Adam` optimizer was used for all of the ten best performing models.\\n\",\n \"* Learning rate multiplier: the learning rate multiplier seems to perform best if kept `<1.1`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 249,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"avg best error: 0.13028952267457333\\n\"\n ]\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"finite loss range 0.13028952267457333 0.7716237019156418 0.13131302273812773\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(20, 5))\\n\",\n \"main_plot_history(trials)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(20, 20))\\n\",\n \"main_plot_vars(trials, columns=3, colorize_best=10, arrange_by_loss=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Best Model Parameters\\n\",\n \"\\n\",\n \"The best model has the following parameter configuration:\\n\",\n \"* `5` layers,\\n\",\n \"* `300` units per layer,\\n\",\n \"* a dropout rate of `0.22`,\\n\",\n \"* `elu` activation function,\\n\",\n \"* `Adam` optimizer,\\n\",\n \"* learning rate multiplier of `0.61` effectively reducing the default learning rate of `0.001` to `0.00061`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 288,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Best validation loss: 0.13028952267457333\\n\",\n \"\\n\",\n \"Best model parameters:\\n\",\n \"======================\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"{'activation': 0,\\n\",\n \" 'adam_lr_rate_mult': 0.61157158868869,\\n\",\n \" 'batch_size': 0,\\n\",\n \" 'dropout_rate': 0.22339774943469998,\\n\",\n \" 'epochs': 0,\\n\",\n \" 'nr_layers': 5.0,\\n\",\n \" 'nr_units': 300.0,\\n\",\n \" 'optimizer': 1}\"\n ]\n },\n \"execution_count\": 288,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"print('Best validation loss: {}\\\\n'.format(np.min(trials.losses())))\\n\",\n \"print('Best model parameters:')\\n\",\n \"print('======================')\\n\",\n \"best_run\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 263,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Best model path: \\\"models/c0cb0656-558f-4311-b138-9b91ab4d1fe6.h5\\\"\\n\"\n ]\n }\n ],\n \"source\": [\n \"best_model_path = trials.results[np.argmin(trials.losses())]['model_name']\\n\",\n \"print('Best model path: \\\"{}\\\"'.format(best_model_path))\\n\",\n \"best_model = models.load_model(best_model_path)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Performance\\n\",\n \"\\n\",\n \"The optimized model has an insignificantly higher PR score of `0.97793` compared to the static model with a score of `0.97735`. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 264,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Average PR Score 0.9779380329781184\\n\"\n ]\n }\n ],\n \"source\": [\n \"pred_val_score = predict_proba(best_model, X_val)\\n\",\n \"plot_pr_curve(y_val.label_is_attack.values, pred_val_score)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.2 Search-Space restriction and Training\\n\",\n \"\\n\",\n \"As a second optimization step, we use the optimal parameter values discovered in the first round of hyperparameter search, to conduct another round of hyperparameter search on a restricted search space. This allows us to further explore the optimal parameter space. \\n\",\n \"For this round of hyperparameter optimization we use a variable batch size and conduct `20` trials.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"trials = Trials()\\n\",\n \"\\n\",\n \"space = { \\n\",\n \" 'epochs': hp.choice('epochs', [30]), \\n\",\n \" 'batch_size': hp.quniform('batch_size', 512, 4096, 10),\\n\",\n \" 'nr_layers': hp.quniform('nr_layers', 4, 6, 1), \\n\",\n \" 'nr_units': hp.quniform('nr_units', 300, 400, 100), \\n\",\n \" 'activation': hp.choice('activation', ['elu']),\\n\",\n \" 'dropout_rate': hp.uniform('dropout_rate', 0, 0.3),\\n\",\n \" 'optimizer': hp.choice('optimizer', [\\n\",\n \" {\\n\",\n \" 'name': 'adam',\\n\",\n \" 'lr_mult': hp.loguniform('adam_lr_rate_mult', -0.5, 1),\\n\",\n \" }\\n\",\n \" ])\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"best_run = fmin(fn=train_dynamic_model,\\n\",\n \" space=space,\\n\",\n \" algo=tpe.suggest,\\n\",\n \" max_evals=20,\\n\",\n \" trials=trials)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[Output omitted for brevity]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Unfortunately, the second round of hyperparameter search did not yield a better result, as the loss of the best model is recorded with `0.1306`, compared to the loss of `0.1302` obtained from the best model of the first round. \\n\",\n \"The difference in the losses is negligible though, suggesting that we have found a good configuration for our model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"avg best error: 0.13063318593310846\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"finite loss range 0.13063318593310846 0.14781879949703883 0.1318917911982782\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(20, 5))\\n\",\n \"main_plot_history(trials)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(20, 20))\\n\",\n \"main_plot_vars(trials, columns=3, colorize_best=10, arrange_by_loss=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.3 Training a Model with Optimal Parameters\\n\",\n \"\\n\",\n \"In the last step of this section, we will use the optimal parameter values and model configuration, determined during the first round of hyperparameter search. We train a model using the optimal parameters for `200` epochs, with the objective of obtaining a better performance. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def train_optimized_model(X_train, y_train, X_val, y_val, model_path, epochs, batch_size, class_weights): \\n\",\n \" input_dims = X_train.shape[1]\\n\",\n \"\\n\",\n \" K.clear_session()\\n\",\n \" gc.collect()\\n\",\n \" \\n\",\n \" nr_layers = 5\\n\",\n \" nr_units = 300\\n\",\n \" dropout_rate = 0.22339774943469998\\n\",\n \" lr = (0.001 * 0.61157158868869)\\n\",\n \"\\n\",\n \" model = create_model(input_dims=input_dims,\\n\",\n \" nr_layers=nr_layers,\\n\",\n \" nr_units=nr_units,\\n\",\n \" activation='elu',\\n\",\n \" kerner_initializer='he_normal',\\n\",\n \" dropout_layer=layers.Dropout(dropout_rate),\\n\",\n \" optimizer=optimizers.Adam(lr=lr))\\n\",\n \"\\n\",\n \" print(model.summary())\\n\",\n \" \\n\",\n \" mc = callbacks.ModelCheckpoint(filepath=model_path, save_best_only=True)\\n\",\n \" \\n\",\n \" early_stopping = callbacks.EarlyStopping(patience=50)\\n\",\n \"\\n\",\n \" lr_scheduler = callbacks.ReduceLROnPlateau(factor=0.2, patience=5)\\n\",\n \" \\n\",\n \" hist = model.fit(x=X_train, \\n\",\n \" y=y_train,\\n\",\n \" validation_data=(X_val, y_val),\\n\",\n \" batch_size=batch_size,\\n\",\n \" epochs=epochs,\\n\",\n \" class_weight=class_weights,\\n\",\n \" callbacks=[mc, lr_scheduler])\\n\",\n \" \\n\",\n \" return model, hist\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"opt_model, hist = train_optimized_model(X_train, \\n\",\n \" y_train_is_attack, \\n\",\n \" X_val, \\n\",\n \" y_val.label_is_attack.values,\\n\",\n \" 'models/opt_model.h5',\\n\",\n \" epochs=200,\\n\",\n \" batch_size=4096,\\n\",\n \" class_weights=class_weights)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[Output omitted for brevity]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Validation\\n\",\n \"\\n\",\n \"Inspecting the learning curves, we observe that the model does not significantly overfit the training set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fix, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(20, 8))\\n\",\n \"\\n\",\n \"plot_hist(hist, ax=ax1)\\n\",\n \"plot_hist(hist, metrics=['auc', 'val_auc'], ax=ax2)\\n\",\n \"plot_hist(hist, metrics=['precision', 'val_precision'], ax=ax3)\\n\",\n \"plot_hist(hist, metrics=['recall', 'val_recall'], ax=ax4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Performance\\n\",\n \"\\n\",\n \"After training, we obtain a model with a slightly lower loss of `0.1298` and a better PR score of `0.97816` compared to the best model found during hyperparameter search, with a loss of `0.1302` and a PR score of `0.97793`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Average PR Score 0.9781612882599011\\n\"\n ]\n }\n ],\n \"source\": [\n \"opt_model = models.load_model('models/opt_model.h5')\\n\",\n \"pred_val_score = predict_proba(opt_model, X_val)\\n\",\n \"plot_pr_curve(y_val.label_is_attack.values, pred_val_score)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.991 0.993 0.992 1348471\\n\",\n \" 1 0.967 0.954 0.960 274823\\n\",\n \"\\n\",\n \" accuracy 0.987 1623294\\n\",\n \" macro avg 0.979 0.974 0.976 1623294\\n\",\n \"weighted avg 0.987 0.987 0.987 1623294\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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misclassifiedtotalpercent_misclassified
Infilteration12466161930.769839
SQL Injection290.222222
Brute Force -Web3610.049180
Brute Force -XSS1230.043478
Benign896613484710.006649
DoS attacks-Slowloris610990.005460
Bot104286190.003634
DDoS attacks-LOIC-HTTP45576190.000781
SSH-Bruteforce2187590.000107
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 12466 16193 0.769839\\n\",\n \"SQL Injection 2 9 0.222222\\n\",\n \"Brute Force -Web 3 61 0.049180\\n\",\n \"Brute Force -XSS 1 23 0.043478\\n\",\n \"Benign 8966 1348471 0.006649\\n\",\n \"DoS attacks-Slowloris 6 1099 0.005460\\n\",\n \"Bot 104 28619 0.003634\\n\",\n \"DDoS attacks-LOIC-HTTP 45 57619 0.000781\\n\",\n \"SSH-Bruteforce 2 18759 0.000107\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pred_val = predict(opt_model, X_val)\\n\",\n \"print_binary_performance(y_val, y_val.label_is_attack, pred_val)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Model Comparison\\n\",\n \"\\n\",\n \"The model obtained during hyperparameter optimization has a higher PR score of `0.97816` compared to the static model, having a score of `0.97735`. \\n\",\n \"Precision and recall are very similar, with values of `0.967` for precision and `0.954` for recall. \\n\",\n \"However, both the number of false-positives and the number of false-negatives are lower for the optimized model, as illustrated in the following table: \\n\",\n \"\\n\",\n \"|Model|PR Score|Precision Positive|Recall Positive|False-Positives|False-Negatives|\\n\",\n \"|---|---|---|---|---|---|\\n\",\n \"|Static model (no class-weights)|0.97718|**0.992**|0.939|**1956**|16898|\\n\",\n \"|Static model (class-weights)|0.97735|0.965|0.954|9375|12649|\\n\",\n \"|Optimized model (class-weights)|**0.97816**|0.967|**0.954**|8966|**12629**|\\n\",\n \"\\n\",\n \"Given the superior performance of the optimized model, we choose to use the this model going forward.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6. Conclusion\\n\",\n \"\\n\",\n \"In this notebook, we explored two approaches of training a deep neural network for the task of binary classification of network traffic, concluding that training should be performed taking class imbalance into account, in order to minimize the amount of false-negatives. \\n\",\n \"Furthermore, we conducted hyperparameter search and optimization to explore the optimal network architecture and parameter configuration, resulting in a model with compelling performance. \\n\",\n \"Even though the model seems to work well on most types of network attacks, it fails to correctly identify infiltration attacks, misclassifiying `77%` of all infiltration traffic as benign traffic. This could be explained by the simmilarity of feature statistics observed in these two kinds of network traffic. \\n\",\n \"As a result, other approaches to reliably detect infiltration attacks have to be explored.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "notebooks/06_dl_classifier/models/c0cb0656-558f-4311-b138-9b91ab4d1fe6.h5", "content": "version https://git-lfs.github.com/spec/v1\noid sha256:8efc348b48af452153dec068d1367cec784ffc2930049df4eaf371d10c0d1caa\nsize 4651784\n" }, { "path": "notebooks/06_dl_classifier/models/model_class_weight.h5", "content": "version https://git-lfs.github.com/spec/v1\noid sha256:60e8392296780d912e8bff335bdadb81deae5b035925d2282e963d45def4ce95\nsize 4231072\n" }, { "path": "notebooks/06_dl_classifier/models/model_no_class_weights.h5", "content": "version https://git-lfs.github.com/spec/v1\noid sha256:e0d9dff11e5600e74974a8e6657be10f7af7d1abe7b66ea2308d9d6eea4d29eb\nsize 4231072\n" }, { "path": "notebooks/06_dl_classifier/models/opt_model.h5", "content": "version https://git-lfs.github.com/spec/v1\noid sha256:d941b094728d0e970231c2f40440da9bfe2c9c5f9898954064954483d210857a\nsize 4655880\n" }, { "path": "notebooks/06_dl_classifier/notebook_utils.py", "content": "import numpy as np\nimport gc\nfrom ml_ids.model_selection import split_x_y, train_val_test_split\nfrom ml_ids.transform.sampling import upsample_minority_classes, downsample\nfrom ml_ids.transform.preprocessing import create_pipeline\nfrom collections import Counter\n\n\ndef transform_data(dataset,\n attack_samples,\n imputer_strategy,\n scaler,\n benign_samples=None,\n random_state=None):\n\n cols_to_impute = dataset.columns[dataset.isna().any()].tolist()\n\n train_data, val_data, test_data = train_val_test_split(dataset,\n val_size=0.1,\n test_size=0.1,\n stratify_col='label_cat',\n random_state=random_state)\n\n if benign_samples:\n train_data = downsample(train_data, default_nr_samples=benign_samples, random_state=random_state)\n\n X_train_raw, y_train = split_x_y(train_data)\n X_val_raw, y_val = split_x_y(val_data)\n X_test_raw, y_test = split_x_y(test_data)\n\n print('Samples:')\n print('========')\n print('Training: {}'.format(X_train_raw.shape))\n print('Val: {}'.format(X_val_raw.shape))\n print('Test: {}'.format(X_test_raw.shape))\n\n print('\\nTraining labels:')\n print('================')\n print(y_train.label.value_counts())\n print('\\nValidation labels:')\n print('==================')\n print(y_val.label.value_counts())\n print('\\nTest labels:')\n print('============')\n print(y_test.label.value_counts())\n\n del train_data, val_data, test_data\n gc.collect()\n\n pipeline, get_col_names = create_pipeline(X_train_raw,\n imputer_strategy=imputer_strategy,\n imputer_cols=cols_to_impute,\n scaler=scaler)\n\n X_train = pipeline.fit_transform(X_train_raw)\n X_val = pipeline.transform(X_val_raw)\n X_test = pipeline.transform(X_test_raw)\n\n column_names = get_col_names()\n\n print('Samples:')\n print('========')\n print('Training: {}'.format(X_train.shape))\n print('Val: {}'.format(X_val.shape))\n print('Test: {}'.format(X_test.shape))\n\n print('\\nMissing values:')\n print('===============')\n print('Training: {}'.format(np.count_nonzero(np.isnan(X_train))))\n print('Val: {}'.format(np.count_nonzero(np.isnan(X_val))))\n print('Test: {}'.format(np.count_nonzero(np.isnan(X_test))))\n\n print('\\nScaling:')\n print('========')\n print('Training: min={}, max={}'.format(np.min(X_train), np.max(X_train)))\n print('Val: min={}, max={}'.format(np.min(X_val), np.max(X_val)))\n print('Test: min={}, max={}'.format(np.min(X_test), np.max(X_test)))\n\n X_train, y_train = upsample_minority_classes(X_train,\n y_train,\n min_samples=attack_samples,\n random_state=random_state)\n\n print('Samples:')\n print('========')\n print('Training: {}'.format(X_train.shape))\n\n print('\\nTraining labels:')\n print('================')\n print(Counter(y_train))\n\n return X_train, y_train, X_val, y_val, X_test, y_test, column_names\n" }, { "path": "notebooks/07_binary_classifier_comparison/binary-classifier-comparison.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Binary Classifier Comparison\\n\",\n \"\\n\",\n \"### Achievement\\n\",\n \"\\n\",\n \"Using the machine learning algorithm of gradient boosted trees in combination with hyperparameter optimization we create a binary classifier, able to distinguish benign from malicious network traffic, with the following performance characteristics:\\n\",\n \"\\n\",\n \"|PR Score|Precision Positive|Recall Positive|False-Positives|False-Negatives|\\n\",\n \"|---|---|---|---|---|\\n\",\n \"|0.98266|0.964|0.957|9784|11748|\\n\",\n \"\\n\",\n \"The classifier created in this notebook yields the best performance yet, compared to the estimators trained in previous experiments.\\n\",\n \"\\n\",\n \"\\n\",\n \"## 1. Introduction\\n\",\n \"\\n\",\n \"In this notebook, we will compare different supervised machine learning approaches to create a binary classifier, able to identify malicious network traffic. \\n\",\n \"\\n\",\n \"In particular, we will compare the following algorithms:\\n\",\n \"* [Random Forest](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) Classifier using [scikit-learn](https://scikit-learn.org/stable/index.html),\\n\",\n \"* [Gradient Boosted Tree](https://catboost.ai/docs/concepts/python-reference_catboostclassifier.html) Classifier using the [CatBoost](https://catboost.ai/docs/) library and a\\n\",\n \"* Deep Neural Network using [Keras](https://keras.io/) and [Tensorflow 2](https://www.tensorflow.org/).\\n\",\n \"\\n\",\n \"The approaches presented in this notebook were explored in detail in the course of previous experiments and can be examined in the notebooks [ML Prototype](https://github.com/cstub/ml-ids/blob/master/notebooks/03_ml-prototype/ml-prototype.ipynb) and [Deep Learning Classifier](https://github.com/cstub/ml-ids/blob/master/notebooks/06_dl_classifier/dl-classifier.ipynb). \\n\",\n \"\\n\",\n \"To ensure a fair comparison of the different models, we will conduct hyperparameter search and optimization for the Random Forest and Gradient Boosted Tree algorithms, as the previous experiments used the default parameters of both algorithms for training. We will utilize the [Hyperopt library](https://github.com/hyperopt/hyperopt) to perform hyperparameter search. \\n\",\n \"The Deep Neural Network presented in this notebook has already been fine-tuned via hyperparameter search in a previous [experiment](https://github.com/cstub/ml-ids/blob/master/notebooks/06_dl_classifier/dl-classifier.ipynb) and will not be retrained, but rather used as created in this experiment.\\n\",\n \"\\n\",\n \"After training and fine-tuning, we will compare the models based on their respective performances on the validation set and select the best performing model. Subsequently, this model will be evaluated on the test set, to obtain an unbiased estimate of the performance of the model.\\n\",\n \"\\n\",\n \"The data used to train and compare the models is taken from the [CIC-IDS-2018 dataset](https://www.unb.ca/cic/datasets/ids-2018.html).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Dataset Preparation\\n\",\n \"\\n\",\n \"In this section we load the dataset and prepare the data for training. The preprocessed dataset from CIC-IDS-2018 can be found at [Google Drive](https://drive.google.com/open?id=1HrTPh0YRSZ4T9DLa_c47lubheKUcPl0r).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dataset_base_path = r'/home/glados/Development/Projects/ids-2018/processed'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"import gc\\n\",\n \"import uuid\\n\",\n \"import joblib as jl\\n\",\n \"import ml_ids.keras.prediction as kp\\n\",\n \"import tensorflow as tf\\n\",\n \"from ml_ids.data.dataset import load_dataset\\n\",\n \"from ml_ids.data.metadata import FEATURES_NO_VARIANCE\\n\",\n \"from ml_ids.visualization import plot_hist, print_binary_performance, plot_pr_curve, plot_pr_curves, plot_pr_threshold_curves\\n\",\n \"from ml_ids.keras.metrics import AveragePrecisionScoreMetric\\n\",\n \"from ml_ids.model_selection import best_precision_for_target_recall\\n\",\n \"from ml_ids.prediction import predict_proba_positive, predict_decision_boundary\\n\",\n \"from ml_ids.tf_utils import enable_gpu_memory_growth\\n\",\n \"from notebook_utils import transform_data, get_best_model_path, print_trial_results\\n\",\n \"from sklearn.metrics import average_precision_score, classification_report\\n\",\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"from catboost import CatBoostClassifier, Pool\\n\",\n \"from tensorflow.keras import models\\n\",\n \"from hyperopt import fmin, hp, tpe, atpe, Trials, STATUS_OK\\n\",\n \"from hyperopt.plotting import main_plot_history, main_plot_vars\\n\",\n \"\\n\",\n \"\\n\",\n \"rand_state = 42\\n\",\n \"tf.random.set_seed(rand_state)\\n\",\n \"np.random.seed(rand_state)\\n\",\n \"\\n\",\n \"enable_gpu_memory_growth()\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.1 Data Loading & Preparation\\n\",\n \"\\n\",\n \"In this part we load the dataset, specifing the features to omit. These encompass all features having no variance and features which should not be taken into account, in order to obtain a better generalization of the model.\\n\",\n \"\\n\",\n \"We use `median` imputation as the strategy to impute missing values, as the distributions of all features, for which imputation is necessary, are highly skewed. \\n\",\n \"Futhermore, we scale the data using a `StandardScaler`, as the neural network demands the data to be scaled, and upsample the malicious network traffic to obtain `100000` samples per attack category. We perform upsampling by using the technique of Synthetic Minority Over-sampling provided by the [SMOTE](https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.over_sampling.SMOTE.html) library.\\n\",\n \"\\n\",\n \"Subsequently, the dataset is splitted into:\\n\",\n \"* training set,\\n\",\n \"* validation set and\\n\",\n \"* test set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Samples:\\n\",\n \"========\\n\",\n \"Training: (12986354, 68)\\n\",\n \"Val: (1623294, 68)\\n\",\n \"Test: (1623295, 68)\\n\",\n \"\\n\",\n \"Training labels:\\n\",\n \"================\\n\",\n \"Benign 10787766\\n\",\n \"DDOS attack-HOIC 548809\\n\",\n \"DDoS attacks-LOIC-HTTP 460953\\n\",\n \"DoS attacks-Hulk 369530\\n\",\n \"Bot 228953\\n\",\n \"FTP-BruteForce 154688\\n\",\n \"SSH-Bruteforce 150071\\n\",\n \"Infilteration 129547\\n\",\n \"DoS attacks-SlowHTTPTest 111912\\n\",\n \"DoS attacks-GoldenEye 33206\\n\",\n \"DoS attacks-Slowloris 8792\\n\",\n \"DDOS attack-LOIC-UDP 1384\\n\",\n \"Brute Force -Web 489\\n\",\n \"Brute Force -XSS 184\\n\",\n \"SQL Injection 70\\n\",\n \"Name: label, dtype: int64\\n\",\n \"\\n\",\n \"Validation labels:\\n\",\n \"==================\\n\",\n \"Benign 1348471\\n\",\n \"DDOS attack-HOIC 68601\\n\",\n \"DDoS attacks-LOIC-HTTP 57619\\n\",\n \"DoS attacks-Hulk 46191\\n\",\n \"Bot 28619\\n\",\n \"FTP-BruteForce 19336\\n\",\n \"SSH-Bruteforce 18759\\n\",\n \"Infilteration 16193\\n\",\n \"DoS attacks-SlowHTTPTest 13989\\n\",\n \"DoS attacks-GoldenEye 4151\\n\",\n \"DoS attacks-Slowloris 1099\\n\",\n \"DDOS attack-LOIC-UDP 173\\n\",\n \"Brute Force -Web 61\\n\",\n \"Brute Force -XSS 23\\n\",\n \"SQL Injection 9\\n\",\n \"Name: label, dtype: int64\\n\",\n \"\\n\",\n \"Test labels:\\n\",\n \"============\\n\",\n \"Benign 1348471\\n\",\n \"DDOS attack-HOIC 68602\\n\",\n \"DDoS attacks-LOIC-HTTP 57619\\n\",\n \"DoS attacks-Hulk 46191\\n\",\n \"Bot 28619\\n\",\n \"FTP-BruteForce 19336\\n\",\n \"SSH-Bruteforce 18759\\n\",\n \"Infilteration 16194\\n\",\n \"DoS attacks-SlowHTTPTest 13989\\n\",\n \"DoS attacks-GoldenEye 4151\\n\",\n \"DoS attacks-Slowloris 1099\\n\",\n \"DDOS attack-LOIC-UDP 173\\n\",\n \"Brute Force -Web 61\\n\",\n \"Brute Force -XSS 23\\n\",\n \"SQL Injection 8\\n\",\n \"Name: label, dtype: int64\\n\",\n \"Samples:\\n\",\n \"========\\n\",\n \"Training: (12986354, 68)\\n\",\n \"Val: (1623294, 68)\\n\",\n \"Test: (1623295, 68)\\n\",\n \"\\n\",\n \"Missing values:\\n\",\n \"===============\\n\",\n \"Training: 0\\n\",\n \"Val: 0\\n\",\n \"Test: 0\\n\",\n \"\\n\",\n \"Scaling:\\n\",\n \"========\\n\",\n \"Training: min=-2.338481849667957, max=3467.6006434451638\\n\",\n \"Val: min=-2.338481849667957, max=1919.8280533361315\\n\",\n \"Test: min=-2.338481849667957, max=2698.612041157329\\n\",\n \"Samples:\\n\",\n \"========\\n\",\n \"Training: (13542229, 68)\\n\",\n \"\\n\",\n \"Training labels:\\n\",\n \"================\\n\",\n \"Counter({0: 10787766, 8: 548809, 10: 460953, 5: 369530, 1: 228953, 11: 154688, 14: 150071, 12: 129547, 6: 111912, 2: 100000, 4: 100000, 7: 100000, 9: 100000, 13: 100000, 3: 100000})\\n\"\n ]\n }\n ],\n \"source\": [\n \"dataset = load_dataset(dataset_base_path,\\n\",\n \" omit_cols=FEATURES_NO_VARIANCE + ['timestamp', 'dst_port', 'protocol'],\\n\",\n \" preserve_neg_value_cols=['init_fwd_win_byts', 'init_bwd_win_byts'])\\n\",\n \"\\n\",\n \"X_train, y_train, X_val, y_val, X_test, y_test, column_names = transform_data(dataset=dataset,\\n\",\n \" imputer_strategy='median',\\n\",\n \" scaler=StandardScaler,\\n\",\n \" attack_samples=100000,\\n\",\n \" random_state=rand_state)\\n\",\n \"\\n\",\n \"del dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Target Grouping & Class Weight Calculation\\n\",\n \"\\n\",\n \"In this step we create the binary target variable by grouping all malicious network traffic into a single attack category represented by the positive class `1`, whereas benign traffic is represented as the negative class `0`.\\n\",\n \"\\n\",\n \"Furthermore, we calculate the weights for each class, given by the proportion of samples of this class in the training set. \\n\",\n \"For the majority class, the class weight is `1`. \\n\",\n \"For the minority class, the class weight is calculated with the following formula:\\n\",\n \"\\\\begin{align}\\n\",\n \"\\\\frac{(sum\\\\_majority\\\\_class)}{(sum\\\\_minority\\\\_class)} \\\\\\\\\\n\",\n \"\\\\end{align}\\n\",\n \"In our case this translates to:\\n\",\n \"\\\\begin{align}\\n\",\n \"\\\\frac{(sum\\\\_negative\\\\_class)}{(sum\\\\_positive\\\\_class)} \\\\\\\\\\n\",\n \"\\\\end{align}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y_train_is_attack = (y_train != 0).astype('int')\\n\",\n \"\\n\",\n \"minority_class_weight = len(y_train_is_attack[y_train_is_attack == 0]) / len(y_train_is_attack[y_train_is_attack == 1])\\n\",\n \"\\n\",\n \"class_weights = { \\n\",\n \" 0: 1, \\n\",\n \" 1: minority_class_weight\\n\",\n \"}\\n\",\n \"\\n\",\n \"class_weights_catboost = [1, minority_class_weight]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 3. Random Forest Model\\n\",\n \"\\n\",\n \"### 3.1 Hyperparameter Search-Space Definition and Training\\n\",\n \"\\n\",\n \"In this section of the notebook, we train a [Random Forest Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) using scikit-learn. We aim to find the optimal parameter configuration for the Random Forest algorithm by conducting hyperparameter search via Hyperopt.\\n\",\n \"\\n\",\n \"To use Hyperopt, we have to define the search-spaces for all parameters that should be optimized. The search-spaces are defined as follows:\\n\",\n \"* Number of tree estimators *(n_estimators)*: uniform integer space in the interval of `[10, 100]`.\\n\",\n \"* Information gain criterion *(criterion)*: choice of `gini` or `entropy`.\\n\",\n \"* Maximum depth of a single tree *(max_depth)*: uniform integer space in the interval of `[10, 100]` or `None` resulting in an unbounded tree.\\n\",\n \"* Maximum features for the best split *(max_features)*: choice of `sqrt` or `log2`.\\n\",\n \"* Minimum samples to split a node *(min_samples_split)*: uniform integer space in the interval of `[1, 10]`.\\n\",\n \"* Minimum samples at a leaf node *(min_samples_leaf)*: uniform integer space in the interval of `[2, 10]`.\\n\",\n \"\\n\",\n \"Training is performed on `50` models, using the [Tree of Parzen Estimators (TPE)](https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf) search algorithm provided by Hyperopt, to optimize the search process. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def train_random_forest(args):\\n\",\n \" print('\\\\nRun') \\n\",\n \" print('==========')\\n\",\n \" print('Parameters:\\\\n{}'.format(args))\\n\",\n \" \\n\",\n \" nr_estimators = int(args['nr_estimators'])\\n\",\n \" criterion = args['criterion']\\n\",\n \" max_depth = int(args['max_depth']['depth']) if args['max_depth']['depth'] else None\\n\",\n \" max_features = args['max_features']\\n\",\n \" min_samples_split = int(args['min_samples_split'])\\n\",\n \" min_samples_leaf = int(args['min_samples_leaf'])\\n\",\n \" \\n\",\n \" model_path = 'models/rf_{}.joblib'.format(uuid.uuid4())\\n\",\n \" \\n\",\n \" clf = RandomForestClassifier(n_estimators=nr_estimators,\\n\",\n \" criterion=criterion,\\n\",\n \" max_depth=max_depth,\\n\",\n \" max_features=max_features,\\n\",\n \" min_samples_split=min_samples_split,\\n\",\n \" min_samples_leaf=min_samples_leaf,\\n\",\n \" class_weight='balanced',\\n\",\n \" n_jobs=-1,\\n\",\n \" verbose=1,\\n\",\n \" random_state=rand_state)\\n\",\n \" \\n\",\n \" clf.fit(X_train, y_train_is_attack)\\n\",\n \" \\n\",\n \" pred = predict_proba_positive(clf, X_val)\\n\",\n \" pr_score = average_precision_score(y_val.label_is_attack, pred)\\n\",\n \" \\n\",\n \" jl.dump(clf, model_path)\\n\",\n \"\\n\",\n \" print('PR Score: {}'.format(pr_score))\\n\",\n \" \\n\",\n \" return {\\n\",\n \" 'loss': -pr_score,\\n\",\n \" 'status': STATUS_OK,\\n\",\n \" 'model_path': model_path\\n\",\n \" }\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"trials_rf = Trials()\\n\",\n \"\\n\",\n \"space = { \\n\",\n \" 'nr_estimators': hp.quniform('nr_estimators', 10, 100, 1),\\n\",\n \" 'criterion': hp.choice('criterion', ['gini', 'entropy']),\\n\",\n \" 'max_depth': hp.choice('max_depth', [\\n\",\n \" {\\n\",\n \" 'depth': None\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'depth': hp.quniform('nr_max_depth', 10, 100, 1),\\n\",\n \" }\\n\",\n \" ]), \\n\",\n \" 'max_features': hp.choice('max_features', ['sqrt', 'log2']),\\n\",\n \" 'min_samples_split': hp.quniform('min_samples_split', 2, 10, 1),\\n\",\n \" 'min_samples_leaf': hp.quniform('min_samples_leaf', 1, 10, 1)\\n\",\n \"}\\n\",\n \"\\n\",\n \"best_run_rf = fmin(fn=train_random_forest,\\n\",\n \" space=space,\\n\",\n \" algo=tpe.suggest,\\n\",\n \" max_evals=50,\\n\",\n \" trials=trials_rf)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[Output omitted for brevity]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.2 Hyperparameter Search Results\\n\",\n \"\\n\",\n \"After completion of the hyperparameter search, we plot the parameter search-spaces and highlight the values used for the ten best performing models in each respective search-space. \\n\",\n \"Evaluating the hyperparameter search results, we can infer the following optimal value-ranges for each parameter:\\n\",\n \"\\n\",\n \"* Number of tree estimators: the optimal number of tree estimators seems to be in the interval of `[60, 100]`.\\n\",\n \"* Information gain criterion: the `entropy` criterion yields a better performance than the `gini` criterion in our search run.\\n\",\n \"* Maximum depth of a single tree: restricting the depth of the trees seems to perform better than using unrestricted trees. A value in the interval of `[20, 50]` for the maximum tree depth yields a good performance.\\n\",\n \"* Maximum features for the best split: the `sqrt` option seems to perform better than `log2`.\\n\",\n \"* Minimum samples to split a node: the optimal number of minimum samples to split a node is in the interval of `[4, 6]`.\\n\",\n \"* Minimum samples of a leaf node: given our search run, there is no conclusive best option for the minimum numbers of samples in a leaf node. However, the values `{3, 6, 7, 8}` seem to perform well.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"avg best error: -0.9810239466346429\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"finite loss range -0.9810239466346429 -0.9751055486286375 -0.9805809112883349\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(20, 5))\\n\",\n \"main_plot_history(trials_rf)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(20, 20))\\n\",\n \"main_plot_vars(trials_rf, columns=3, colorize_best=10, arrange_by_loss=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.3 Optimal Model Parameters\\n\",\n \"\\n\",\n \"Looking at the best performing model, we can observe the following parameter configuration:\\n\",\n \"* `80` tree estimators,\\n\",\n \"* the `entropy` information gain criterion,\\n\",\n \"* a maximum depth of `32` nodes per tree,\\n\",\n \"* `sqrt` as the selector for the maximum number of features considered when splitting a node,\\n\",\n \"* a minimum of `3` samples per leaf node and\\n\",\n \"* a minimum of `4` samples necessary to split a node.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Best validation score: 0.9810239466346429\\n\",\n \"Best model path: models/rf_1af6dcf7-4407-469b-89a0-b2b78661b00e.joblib\\n\",\n \"\\n\",\n \"Best model parameters:\\n\",\n \"======================\\n\",\n \"{'criterion': 1, 'max_depth': 1, 'max_features': 0, 'min_samples_leaf': 3.0, 'min_samples_split': 4.0, 'nr_estimators': 80.0, 'nr_max_depth': 32.0}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print_trial_results(trials_rf, best_run_rf)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 3.4 Performance\\n\",\n \"\\n\",\n \"The Random Forest model yields a PR score of `0.9810`, with a precision of `0.967` and a recall of `0.955` in respect to the positive class. \\n\",\n \"The model performs quite well, with the exception of the attack category `Infiltration`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clf_rf = jl.load(get_best_model_path(trials_rf))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[Parallel(n_jobs=24)]: Using backend ThreadingBackend with 24 concurrent workers.\\n\",\n \"[Parallel(n_jobs=24)]: Done 2 tasks | elapsed: 0.6s\\n\",\n \"[Parallel(n_jobs=24)]: Done 80 out of 80 | elapsed: 2.9s finished\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Average PR Score 0.9810239466346281\\n\"\n ]\n }\n ],\n \"source\": [\n \"pred_val_proba_rf = predict_proba_positive(clf_rf, X_val)\\n\",\n \"plot_pr_curve(y_val.label_is_attack.values, pred_val_proba_rf)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[Parallel(n_jobs=24)]: Using backend ThreadingBackend with 24 concurrent workers.\\n\",\n \"[Parallel(n_jobs=24)]: Done 2 tasks | elapsed: 0.6s\\n\",\n \"[Parallel(n_jobs=24)]: Done 80 out of 80 | elapsed: 2.8s finished\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.991 0.993 0.992 1348471\\n\",\n \" 1 0.967 0.955 0.961 274823\\n\",\n \"\\n\",\n \" accuracy 0.987 1623294\\n\",\n \" macro avg 0.979 0.974 0.977 1623294\\n\",\n \"weighted avg 0.987 0.987 0.987 1623294\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
misclassifiedtotalpercent_misclassified
Infilteration12252161930.756623
SQL Injection190.111111
Brute Force -Web4610.065574
Brute Force -XSS1230.043478
DoS attacks-Slowloris810990.007279
Benign882013484710.006541
Bot17286190.000594
DDOS attack-HOIC22686010.000321
DDoS attacks-LOIC-HTTP17576190.000295
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 12252 16193 0.756623\\n\",\n \"SQL Injection 1 9 0.111111\\n\",\n \"Brute Force -Web 4 61 0.065574\\n\",\n \"Brute Force -XSS 1 23 0.043478\\n\",\n \"DoS attacks-Slowloris 8 1099 0.007279\\n\",\n \"Benign 8820 1348471 0.006541\\n\",\n \"Bot 17 28619 0.000594\\n\",\n \"DDOS attack-HOIC 22 68601 0.000321\\n\",\n \"DDoS attacks-LOIC-HTTP 17 57619 0.000295\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pred_val_rf = clf_rf.predict(X_val)\\n\",\n \"print_binary_performance(y_val, y_val.label_is_attack, pred_val_rf)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 4. Gradient Boosted Tree Model\\n\",\n \"\\n\",\n \"### 4.1 Hyperparameter Search-Space Definition and Training\\n\",\n \"\\n\",\n \"In this section, we train a [Gradient Boosted Tree Classifier](https://catboost.ai/docs/concepts/python-reference_catboostclassifier.html) utilizing the [CatBoost](https://catboost.ai/docs/) library.\\n\",\n \"\\n\",\n \"Once again, we perform hyperparameter search to find the optimal parameter configuration for the algorithm, given our trainging data. \\n\",\n \"The parameter search-spaces are defined as follows:\\n\",\n \"* Maximum number of trees *(nr_iterations)*: uniform integer space in the interval of `[100, 2000]`.\\n\",\n \"* Maximum depth of a tree *(depth)*: uniform integer space in the interval of `[4, 10]`.\\n\",\n \"* L2 regularization coefficient *(l2_leaf_reg)*: uniform space in the interval of `[1, 10]`.\\n\",\n \"* Number of splits for numerical features *(border_count)*: a choice of `128` and `254`.\\n\",\n \"* Amount of randomness used for scoring splits *(random_strength)*: uniform integer space in the interval of `[0, 5]`.\\n\",\n \"\\n\",\n \"For a more detailed explanation of the individual parameters consult the CatBoost documentation on [parameter tuning](https://catboost.ai/docs/concepts/parameter-tuning.html).\\n\",\n \"\\n\",\n \"Training is performed on `100` models using the Tree of Parzen Estimators (TPE) search algorithm.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"train_pool = Pool(X_train, y_train_is_attack)\\n\",\n \"val_pool = Pool(X_val, y_val.label_is_attack)\\n\",\n \"\\n\",\n \"NR_LOGS = 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def train_gradient_boost(args):\\n\",\n \" print('\\\\nRun') \\n\",\n \" print('==========')\\n\",\n \" print('Parameters:\\\\n{}'.format(args))\\n\",\n \" \\n\",\n \" nr_iterations = int(args['nr_iterations'])\\n\",\n \" tree_depth = int(args['tree_depth'])\\n\",\n \" l2_reg = args['l2_reg']\\n\",\n \" border_count = int(args['border_count'])\\n\",\n \" random_strength = int(args['random_strength'])\\n\",\n \" \\n\",\n \" model_path = 'models/gb_{}.catboost'.format(uuid.uuid4())\\n\",\n \" \\n\",\n \" clf = CatBoostClassifier(loss_function='Logloss', \\n\",\n \" iterations=nr_iterations,\\n\",\n \" depth=tree_depth,\\n\",\n \" l2_leaf_reg=l2_reg,\\n\",\n \" border_count=border_count,\\n\",\n \" random_strength=random_strength,\\n\",\n \" task_type='GPU',\\n\",\n \" class_weights=class_weights_catboost,\\n\",\n \" verbose=(nr_iterations // NR_LOGS),\\n\",\n \" random_seed=rand_state)\\n\",\n \"\\n\",\n \" clf.fit(train_pool, eval_set=val_pool)\\n\",\n \" \\n\",\n \" pred = predict_proba_positive(clf, val_pool)\\n\",\n \" pr_score = average_precision_score(y_val.label_is_attack, pred)\\n\",\n \" \\n\",\n \" clf.save_model(model_path)\\n\",\n \" \\n\",\n \" print('PR Score: {}'.format(pr_score))\\n\",\n \" \\n\",\n \" return {\\n\",\n \" 'loss': -pr_score,\\n\",\n \" 'status': STATUS_OK,\\n\",\n \" 'model_path': model_path\\n\",\n \" }\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"trials_gb = Trials()\\n\",\n \"\\n\",\n \"space = { \\n\",\n \" 'nr_iterations': hp.quniform('nr_iterations', 100, 2000, 100),\\n\",\n \" 'tree_depth': hp.quniform('tree_depth', 4, 10, 1),\\n\",\n \" 'l2_reg': hp.uniform('l2_reg', 1, 10),\\n\",\n \" 'border_count': hp.choice('border_count', [128, 254]),\\n\",\n \" 'random_strength': hp.quniform('random_strength', 0, 5, 1)\\n\",\n \"}\\n\",\n \"\\n\",\n \"best_run_gb = fmin(fn=train_gradient_boost,\\n\",\n \" space=space,\\n\",\n \" algo=tpe.suggest,\\n\",\n \" max_evals=100,\\n\",\n \" trials=trials_gb)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"[Output omitted for brevity]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.2 Hyperparameter Search Results\\n\",\n \"\\n\",\n \"After completion of the hyperparameter search, we inspect the results by plotting the parameter values per search-space and highlight the values resulting in the ten best performing models. \\n\",\n \"\\n\",\n \"From the plots we can infer the following optimal value-ranges per parameter:\\n\",\n \"* Maximum number of trees *(nr_iterations)*: a value in the interval of `[1600, 1900]` seems to perform best.\\n\",\n \"* Maximum depth of a tree *(depth)*: the optimal value for the maximum dept of trees is `10` in all best performing cases. This suggests that another round of hyperparameter search with a higher value might yield a better result.\\n\",\n \"* L2 regularization coefficient *(l2_leaf_reg)*: a value in the interval of `[2, 6]` yields good results.\\n\",\n \"* Number of splits for numerical features *(border_count)*: a border count of `254` was chosen for all of the best models.\\n\",\n \"* Amount of randomness used for scoring splits *(random_strength)*: the optimal value seems to be in the inteval of `[3, 5]`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"avg best error: -0.9826628201037251\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"finite loss range -0.9826628201037251 -0.9720802673898308 -0.9825280984036184\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(20, 5))\\n\",\n \"main_plot_history(trials_gb)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(20, 20))\\n\",\n \"main_plot_vars(trials_gb, columns=3, colorize_best=10, arrange_by_loss=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.3 Optimal Model Parameters\\n\",\n \"\\n\",\n \"The optimal parameter configuration obtained via the best performing model is as follows:\\n\",\n \"* usage of `1900` trees, \\n\",\n \"* a maximum depth of `10` per tree,\\n\",\n \"* a L2 regularization coefficient of `4.8139`,\\n\",\n \"* a border count of `254` and\\n\",\n \"* a random strenght parameter of `5`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Best validation score: 0.9826628201037251\\n\",\n \"Best model path: models/gb_835066e8-2427-48ca-a521-67195008cb91.catboost\\n\",\n \"\\n\",\n \"Best model parameters:\\n\",\n \"======================\\n\",\n \"{'border_count': 1, 'l2_reg': 4.813919374945952, 'nr_iterations': 1900.0, 'random_strength': 5.0, 'tree_depth': 10.0}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print_trial_results(trials_gb, best_run_gb)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 4.4 Performance\\n\",\n \"\\n\",\n \"We obtain a model with a PR score of `0.9826`, a precision of `0.964` and a recall of `0.957` for the positive class. \\n\",\n \"Looking at the confusion matrix and misclassification report, we observe a generally well performing model with the exception of the `Infiltration` attack category, which has a rather high misclassification rate.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"clf_gb = CatBoostClassifier()\\n\",\n \"clf_gb.load_model(get_best_model_path(trials_gb))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Average PR Score 0.9826628201037251\\n\"\n ]\n }\n ],\n \"source\": [\n \"pred_val_proba_gb = predict_proba_positive(clf_gb, val_pool)\\n\",\n \"plot_pr_curve(y_val.label_is_attack.values, pred_val_proba_gb)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.991 0.993 0.992 1348471\\n\",\n \" 1 0.964 0.957 0.961 274823\\n\",\n \"\\n\",\n \" accuracy 0.987 1623294\\n\",\n \" macro avg 0.978 0.975 0.976 1623294\\n\",\n \"weighted avg 0.987 0.987 0.987 1623294\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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misclassifiedtotalpercent_misclassified
Infilteration11690161930.721917
SQL Injection190.111111
Brute Force -XSS1230.043478
Brute Force -Web2610.032787
Benign978413484710.007256
DoS attacks-Slowloris610990.005460
Bot35286190.001223
DDoS attacks-LOIC-HTTP13576190.000226
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 11690 16193 0.721917\\n\",\n \"SQL Injection 1 9 0.111111\\n\",\n \"Brute Force -XSS 1 23 0.043478\\n\",\n \"Brute Force -Web 2 61 0.032787\\n\",\n \"Benign 9784 1348471 0.007256\\n\",\n \"DoS attacks-Slowloris 6 1099 0.005460\\n\",\n \"Bot 35 28619 0.001223\\n\",\n \"DDoS attacks-LOIC-HTTP 13 57619 0.000226\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pred_val_gb = clf_gb.predict(X_val)\\n\",\n \"print_binary_performance(y_val, y_val.label_is_attack, pred_val_gb)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 5. Deep Neural Network Model\\n\",\n \"\\n\",\n \"In this section, we load the Deep Neural Network model trained in a previous [experiment](https://github.com/cstub/ml-ids/blob/master/notebooks/06_dl_classifier/dl-classifier.ipynb), to compare the models performance on the validation set with the performances of the other models trained in this notebook.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clf_dl = models.load_model('../06_dl_classifier/models/opt_model.h5')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Average PR Score 0.9781612882599011\\n\"\n ]\n }\n ],\n \"source\": [\n \"pred_val_proba_dl = kp.predict_proba(clf_dl, X_val)\\n\",\n \"plot_pr_curve(y_val.label_is_attack.values, pred_val_proba_dl)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.991 0.993 0.992 1348471\\n\",\n \" 1 0.967 0.954 0.960 274823\\n\",\n \"\\n\",\n \" accuracy 0.987 1623294\\n\",\n \" macro avg 0.979 0.974 0.976 1623294\\n\",\n \"weighted avg 0.987 0.987 0.987 1623294\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n 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misclassifiedtotalpercent_misclassified
Infilteration12466161930.769839
SQL Injection290.222222
Brute Force -Web3610.049180
Brute Force -XSS1230.043478
Benign896613484710.006649
DoS attacks-Slowloris610990.005460
Bot104286190.003634
DDoS attacks-LOIC-HTTP45576190.000781
SSH-Bruteforce2187590.000107
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 12466 16193 0.769839\\n\",\n \"SQL Injection 2 9 0.222222\\n\",\n \"Brute Force -Web 3 61 0.049180\\n\",\n \"Brute Force -XSS 1 23 0.043478\\n\",\n \"Benign 8966 1348471 0.006649\\n\",\n \"DoS attacks-Slowloris 6 1099 0.005460\\n\",\n \"Bot 104 28619 0.003634\\n\",\n \"DDoS attacks-LOIC-HTTP 45 57619 0.000781\\n\",\n \"SSH-Bruteforce 2 18759 0.000107\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pred_val_dl = kp.predict(clf_dl, X_val)\\n\",\n \"print_binary_performance(y_val, y_val.label_is_attack, pred_val_dl)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 6. Model Comparison\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following plot illustrates the Precision / Recall curves of the different models.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_pr_curves(y_val.label_is_attack, \\n\",\n \" {\\n\",\n \" 'Random Forest': pred_val_proba_rf,\\n\",\n \" 'Gradient Boost': pred_val_proba_gb,\\n\",\n \" 'Neural Network': pred_val_proba_dl\\n\",\n \" }, \\n\",\n \" size=(20, 8))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Comparing the results, we observe similar performance metrics across the different models. \\n\",\n \"\\n\",\n \"Both the Random Forest and Gradient Boosted Tree models perform better than the Neural Network though, achieving a slighty higher PR score, precision and recall.\\n\",\n \"\\n\",\n \"Overall, the Gradient Boosted Tree model yields the best performance, having the highest PR score and recall in respect to the positive class, with only a slightly lower precision compared to the Random Forest model. Consequently, this model also returns the lowest amount of false-negatives. \\n\",\n \"\\n\",\n \"|Model|PR Score|Precision Positive|Recall Positive|False-Positives|False-Negatives|\\n\",\n \"|---|---|---|---|---|---|\\n\",\n \"|Random Forest |0.98102|**0.967**|0.955|**8820**|12322|\\n\",\n \"|Gradient Boosted Trees|**0.98266**|0.964|**0.957**|9784|**11748**|\\n\",\n \"|Deep Neural Network |0.97816|**0.967**|0.954|8966|12629|\\n\",\n \"\\n\",\n \"We choose to use the Gradient Bosted Tree model going forward, as it achieves the best overall performance.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7. Precision / Recall Decision Boundary\\n\",\n \"\\n\",\n \"All models presented in this notebook use class probabilities (in the interval `[0, 1]`) to predict the class of a given sample. In the case of binary classification a probability threshold for the positive class is chosen, that when exceeded, assigns the sample to the positive class. \\n\",\n \"This probability threshold is called the *decision boundary* and is usually defined at the value of `0.5`.\\n\",\n \"\\n\",\n \"Changing the decision boundary allows us to modify the metrics for precision and recall, as a decreased decision boundary leads to a lower precision but higher recall, whereas an increased decision boundary has the opposite result, a higher precision and lower recall. This is called the precision-recall tradeoff.\\n\",\n \"\\n\",\n \"The following graphic illustrates this tradeoff by plotting the precision and recall for different values of the decision boundary. We can observe that an increase of precision or recall close to `1.00` leads to a drastic decrease in the opposite metric, so we have to be careful while tuning either one of them.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_pr_threshold_curves(y_val.label_is_attack, pred_val_proba_gb)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As the graphic depiction might be hard to interpret precisely, we now print the performance metrics for different choices of the decision boundary. \\n\",\n \"The decision boundaries are chosen for a range of different target recalls ranging from `0.96 - 1.00` in steps of `0.01`. A specific decision boundary is determined by choosing a threshold from the precision-recall curve having the best precision while still satisfying the recall objective.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target Recall of 0.96\\n\",\n \"=====================\\n\",\n \"Decision Boundary: 0.3556530142617532\\n\",\n \"\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.992 0.990 0.991 1348471\\n\",\n \" 1 0.952 0.960 0.956 274823\\n\",\n \"\\n\",\n \" accuracy 0.985 1623294\\n\",\n \" macro avg 0.972 0.975 0.974 1623294\\n\",\n \"weighted avg 0.985 0.985 0.985 1623294\\n\",\n \"\\n\",\n \"\\n\",\n \"Target Recall of 0.97\\n\",\n \"=====================\\n\",\n \"Decision Boundary: 0.07857565826548527\\n\",\n \"\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.994 0.942 0.967 1348471\\n\",\n \" 1 0.774 0.970 0.861 274823\\n\",\n \"\\n\",\n \" accuracy 0.947 1623294\\n\",\n \" macro avg 0.884 0.956 0.914 1623294\\n\",\n \"weighted avg 0.956 0.947 0.949 1623294\\n\",\n \"\\n\",\n \"\\n\",\n \"Target Recall of 0.98\\n\",\n \"=====================\\n\",\n \"Decision Boundary: 0.05394934038983959\\n\",\n \"\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.995 0.832 0.906 1348471\\n\",\n \" 1 0.543 0.980 0.699 274823\\n\",\n \"\\n\",\n \" accuracy 0.857 1623294\\n\",\n \" macro avg 0.769 0.906 0.803 1623294\\n\",\n \"weighted avg 0.919 0.857 0.871 1623294\\n\",\n \"\\n\",\n \"\\n\",\n \"Target Recall of 0.99\\n\",\n \"=====================\\n\",\n \"Decision Boundary: 0.04206439940655072\\n\",\n \"\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.997 0.671 0.802 1348471\\n\",\n \" 1 0.380 0.990 0.549 274823\\n\",\n \"\\n\",\n \" accuracy 0.725 1623294\\n\",\n \" macro avg 0.688 0.830 0.675 1623294\\n\",\n \"weighted avg 0.892 0.725 0.759 1623294\\n\",\n \"\\n\",\n \"\\n\",\n \"Target Recall of 1.0\\n\",\n \"=====================\\n\",\n \"Decision Boundary: 0.0007288180142539065\\n\",\n \"\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 1.000 0.189 0.319 1348471\\n\",\n \" 1 0.201 1.000 0.335 274823\\n\",\n \"\\n\",\n \" accuracy 0.327 1623294\\n\",\n \" macro avg 0.600 0.595 0.327 1623294\\n\",\n \"weighted avg 0.865 0.327 0.321 1623294\\n\",\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"recalls = np.arange(0.96, 1, 0.01)\\n\",\n \"\\n\",\n \"pred_val_proba_gb = predict_proba_positive(clf_gb, X_val)\\n\",\n \"\\n\",\n \"for recall in recalls:\\n\",\n \" b = best_precision_for_target_recall(y_val.label_is_attack, pred_val_proba_gb, target_recall=recall)\\n\",\n \" pred_val_gb = predict_decision_boundary(clf_gb, X_val, b)\\n\",\n \" print('Target Recall of {}'.format(recall))\\n\",\n \" print('=====================')\\n\",\n \" print('Decision Boundary: {}\\\\n'.format(b))\\n\",\n \" print(classification_report(y_val.label_is_attack, pred_val_gb, digits=3))\\n\",\n \" print('')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Comparing the different decision boundaries, we obtain the following performance metrics:\\n\",\n \"\\n\",\n \"|Decision Boundary|Precision Positive|Recall Positive|F1 (macro)|\\n\",\n \"|---|---|---|---|\\n\",\n \"|0.5 (default)|0.964|0.957|0.976|\\n\",\n \"|0.3556 |0.952|0.960|0.974|\\n\",\n \"|0.0786 |0.774|0.970|0.914|\\n\",\n \"|0.0539 |0.543|0.980|0.803|\\n\",\n \"|0.0420 |0.380|0.990|0.675|\\n\",\n \"|0.0007 |0.201|1.000|0.327|\\n\",\n \"\\n\",\n \"Increasing the recall to a value higher than `0.96`, we observe a drastic decrease in precision, as the number of false-positives increases dramatically. \\n\",\n \"Given this insight and the fact that the default decision boundary at `0.5` already leads to a recall of approximately `0.96`, changing the decision boundary does not seem worthwhile, as the negative performance impact for further increasing the recall is to severe.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 7. Performance on Test Set\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this section we use our best performing model to make predictions on the test set in order to obtain an unbiased performance estimate of the model.\\n\",\n \"\\n\",\n \"The results demonstrate that the performance on the test set is very similar to the performance on the validation set, suggesting that we have built a model that generalizes well to unseen data, provided that the data has similar statistical characteristics as the test set.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 71,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Classification Report:\\n\",\n \"======================\\n\",\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.991 0.993 0.992 1348471\\n\",\n \" 1 0.964 0.957 0.961 274824\\n\",\n \"\\n\",\n \" accuracy 0.987 1623295\\n\",\n \" macro avg 0.978 0.975 0.976 1623295\\n\",\n \"weighted avg 0.987 0.987 0.987 1623295\\n\",\n \"\\n\",\n \"Confusion Matrix:\\n\",\n \"=================\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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misclassifiedtotalpercent_misclassified
Infilteration11811161940.729344
Brute Force -Web3610.049180
Brute Force -XSS1230.043478
DoS attacks-Slowloris810990.007279
Benign969013484710.007186
Bot24286190.000839
DDoS attacks-LOIC-HTTP9576190.000156
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" misclassified total percent_misclassified\\n\",\n \"Infilteration 11811 16194 0.729344\\n\",\n \"Brute Force -Web 3 61 0.049180\\n\",\n \"Brute Force -XSS 1 23 0.043478\\n\",\n \"DoS attacks-Slowloris 8 1099 0.007279\\n\",\n \"Benign 9690 1348471 0.007186\\n\",\n \"Bot 24 28619 0.000839\\n\",\n \"DDoS attacks-LOIC-HTTP 9 57619 0.000156\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pred_test_gb = clf_gb.predict(X_test)\\n\",\n \"print_binary_performance(y_test, y_test.label_is_attack, pred_test_gb)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 8. Conclusion\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this notebook, we used different machine learning algorithms in combination with hyperparameter optimization to train a variety of models and compare their respective performances. \\n\",\n \"Using the method of Gradient Boosted Trees we obtained the best performing binary classifier, achieving a very compelling performance on the validation and test sets, only falling short in correctly classifying malicious network traffic of the type `Infiltration`. \\n\",\n \"As concluded in an earlier [experiment](https://github.com/cstub/ml-ids/blob/master/notebooks/06_dl_classifier/dl-classifier.ipynb), other approaches of reliably detecting infiltration attacks have to be explored in the future.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "notebooks/07_binary_classifier_comparison/models/gb_835066e8-2427-48ca-a521-67195008cb91.catboost", "content": "version https://git-lfs.github.com/spec/v1\noid sha256:ceccc696d2c5eae0d550425f772221088e7a66b26a626461642e14c2b42099ce\nsize 31179384\n" }, { "path": "notebooks/07_binary_classifier_comparison/notebook_utils.py", "content": "import numpy as np\nimport gc\nfrom ml_ids.model_selection import split_x_y, train_val_test_split\nfrom ml_ids.transform.sampling import upsample_minority_classes, downsample\nfrom ml_ids.transform.preprocessing import create_pipeline\nfrom collections import Counter\n\n\ndef get_best_model_path(trials, model_path_var='model_path'):\n return trials.results[np.argmin(trials.losses())][model_path_var]\n\n\ndef print_trial_results(trials, best_run, model_path_var='model_path'):\n best_model_path = get_best_model_path(trials, model_path_var)\n\n print('Best validation score: {}'.format(-np.min(trials.losses())))\n print('Best model path: {}\\n'.format(best_model_path))\n print('Best model parameters:')\n print('======================')\n print(best_run)\n\n\ndef transform_data(dataset,\n attack_samples,\n imputer_strategy,\n scaler,\n benign_samples=None,\n random_state=None):\n\n cols_to_impute = dataset.columns[dataset.isna().any()].tolist()\n\n train_data, val_data, test_data = train_val_test_split(dataset,\n val_size=0.1,\n test_size=0.1,\n stratify_col='label_cat',\n random_state=random_state)\n\n if benign_samples:\n train_data = downsample(train_data, default_nr_samples=benign_samples, random_state=random_state)\n\n X_train_raw, y_train = split_x_y(train_data)\n X_val_raw, y_val = split_x_y(val_data)\n X_test_raw, y_test = split_x_y(test_data)\n\n print('Samples:')\n print('========')\n print('Training: {}'.format(X_train_raw.shape))\n print('Val: {}'.format(X_val_raw.shape))\n print('Test: {}'.format(X_test_raw.shape))\n\n print('\\nTraining labels:')\n print('================')\n print(y_train.label.value_counts())\n print('\\nValidation labels:')\n print('==================')\n print(y_val.label.value_counts())\n print('\\nTest labels:')\n print('============')\n print(y_test.label.value_counts())\n\n del train_data, val_data, test_data\n gc.collect()\n\n pipeline, get_col_names = create_pipeline(X_train_raw,\n imputer_strategy=imputer_strategy,\n imputer_cols=cols_to_impute,\n scaler=scaler)\n\n X_train = pipeline.fit_transform(X_train_raw)\n X_val = pipeline.transform(X_val_raw)\n X_test = pipeline.transform(X_test_raw)\n\n column_names = get_col_names()\n\n print('Samples:')\n print('========')\n print('Training: {}'.format(X_train.shape))\n print('Val: {}'.format(X_val.shape))\n print('Test: {}'.format(X_test.shape))\n\n print('\\nMissing values:')\n print('===============')\n print('Training: {}'.format(np.count_nonzero(np.isnan(X_train))))\n print('Val: {}'.format(np.count_nonzero(np.isnan(X_val))))\n print('Test: {}'.format(np.count_nonzero(np.isnan(X_test))))\n\n print('\\nScaling:')\n print('========')\n print('Training: min={}, max={}'.format(np.min(X_train), np.max(X_train)))\n print('Val: min={}, max={}'.format(np.min(X_val), np.max(X_val)))\n print('Test: min={}, max={}'.format(np.min(X_test), np.max(X_test)))\n\n X_train, y_train = upsample_minority_classes(X_train,\n y_train,\n min_samples=attack_samples,\n random_state=random_state)\n\n print('Samples:')\n print('========')\n print('Training: {}'.format(X_train.shape))\n\n print('\\nTraining labels:')\n print('================')\n print(Counter(y_train))\n\n return X_train, y_train, X_val, y_val, X_test, y_test, column_names\n" }, { "path": "setup.cfg", "content": "[aliases]\ntest=pytest\n\n[mypy-numpy.*]\nignore_missing_imports = True\n\n[mypy-pandas.*]\nignore_missing_imports = True\n\n[mypy-matplotlib.*]\nignore_missing_imports = True\n\n[mypy-IPython.*]\nignore_missing_imports = True\n\n[mypy-sklearn.*]\nignore_missing_imports = True\n\n[mypy-seaborn.*]\nignore_missing_imports = True\n\n[mypy-tensorflow.*]\nignore_missing_imports = True\n\n[mypy-mlflow.*]\nignore_missing_imports = True\n\n[mypy-catboost.*]\nignore_missing_imports = True\n\n[mypy-imblearn.*]\nignore_missing_imports = True" }, { "path": "setup.py", "content": "from distutils.core import setup\n\nsetup(\n name='ml-ids',\n version='0.1',\n description='Machine learning based Intrusion Detection System',\n long_description='Machine learning based Intrusion Detection System',\n classifiers=[\n 'Programming Language :: Python :: 3',\n ],\n url='https://github.com/cstub/ml-ids',\n author='cstub',\n author_email='stumpf.christoph@gmail.com',\n license='MIT',\n packages=['ml_ids'],\n install_requires=[\n ],\n setup_requires=['pytest-runner'],\n tests_require=['pytest']\n)\n" }, { "path": "tests/data/test_dataset.py", "content": "import pytest\nimport pandas as pd\nimport numpy as np\nimport os\nfrom ml_ids import conf\nfrom ml_ids.data.dataset import load_dataset\n\n\n@pytest.fixture\ndef val_data():\n validation_data_path = os.path.join(conf.TEST_DATA_DIR, 'validation.csv')\n return pd.read_csv(validation_data_path)\n\n\ndef inf_value_count(df):\n return df[(df == np.inf) | (df == -np.inf)].count().sum()\n\n\ndef neg_value_count(df):\n numeric_cols = df.select_dtypes(include=[np.number]).columns.values\n df_num = df[numeric_cols]\n return df_num[df_num < 0].count().sum()\n\n\ndef nan_value_count(df):\n return df.isna().sum().sum()\n\n\ndef negative_value_columns(df):\n numeric_cols = df.select_dtypes(include=[np.number]).columns.values\n return [c for c in numeric_cols if df[df[c] < 0][c].count() > 0]\n\n\ndef test_loaded_dataset_must_not_contain_inf_values():\n df = load_dataset(conf.TEST_DATA_DIR)\n\n assert inf_value_count(df) == 0\n\n\ndef test_loaded_dataset_must_not_contain_negative_values():\n df = load_dataset(conf.TEST_DATA_DIR)\n\n assert neg_value_count(df) == 0\n\n\ndef test_loaded_dataset_must_not_contain_negative_values_except_excluded_cols():\n df = load_dataset(conf.TEST_DATA_DIR, preserve_neg_value_cols=['init_fwd_win_byts', 'init_bwd_win_byts'])\n\n assert neg_value_count(df) != 0\n assert set(negative_value_columns(df)) == {'init_bwd_win_byts', 'init_fwd_win_byts'}\n\n\ndef test_loaded_dataset_must_contain_label_category():\n df = load_dataset(conf.TEST_DATA_DIR)\n\n assert len(df.label_cat.value_counts()) == len(df.label.value_counts())\n\n\ndef test_loaded_dataset_must_contain_label_is_attack():\n df = load_dataset(conf.TEST_DATA_DIR)\n\n all_sample_count = len(df)\n benign_sample_count = len(df[df.label == 'Benign'])\n attack_sample_count = all_sample_count - benign_sample_count\n\n assert len(df[df.label_is_attack == 0]) == benign_sample_count\n assert len(df[df.label_is_attack == 1]) == attack_sample_count\n\n\ndef test_loaded_dataset_must_replace_invalid_value_with_nan(val_data):\n df = load_dataset(conf.TEST_DATA_DIR)\n\n inf_value_c = inf_value_count(val_data)\n neg_value_c = neg_value_count(val_data)\n\n assert (inf_value_c + neg_value_c) == nan_value_count(df)\n\n\ndef test_loaded_dataset_must_contain_only_specified_columns():\n df = load_dataset(conf.TEST_DATA_DIR, use_cols=['dst_port'])\n\n assert df.columns == ['dst_port']\n\n\ndef test_loaded_dataset_must_omit_specified_columns():\n df = load_dataset(conf.TEST_DATA_DIR, omit_cols=['dst_port'])\n\n assert 'dst_port' not in df.columns\n" }, { "path": "tests/transform/test_preprocessing.py", "content": "import pytest\nimport numpy as np\nfrom numpy.testing import assert_array_equal\nfrom sklearn.preprocessing import FunctionTransformer, MinMaxScaler\n\nfrom ml_ids import conf\nfrom ml_ids.data.dataset import load_dataset\nfrom ml_ids.model_selection import split_x_y\nfrom ml_ids.transform.preprocessing import create_pipeline\n\n\n@pytest.fixture\ndef feature_df():\n df = load_dataset(conf.TEST_DATA_DIR, omit_cols=['timestamp'])\n X, _ = split_x_y(df)\n return X\n\n\ndef nan_value_count(x):\n return np.count_nonzero(np.isnan(x))\n\n\ndef test_pipeline_must_impute_all_missing_values(feature_df):\n pipeline, _ = create_pipeline(feature_df,\n imputer_strategy='mean',\n scaler=FunctionTransformer,\n scaler_args={'validate': False})\n transformed = pipeline.fit_transform(feature_df)\n\n assert nan_value_count(feature_df.values) != 0\n assert nan_value_count(transformed) == 0\n\n\ndef test_pipeline_must_impute_selected_columns_only(feature_df):\n pipeline, _ = create_pipeline(feature_df,\n imputer_strategy='mean',\n imputer_cols=['flow_duration', 'flow_pkts_s'],\n scaler=FunctionTransformer,\n scaler_args={'validate': False})\n\n missing_vals_selected_columns = \\\n nan_value_count(feature_df.flow_duration.values) + nan_value_count(feature_df.flow_pkts_s.values)\n\n transformed = pipeline.fit_transform(feature_df)\n\n assert nan_value_count(transformed) == (nan_value_count(feature_df.values) - missing_vals_selected_columns)\n\n\ndef test_pipeline_must_not_impute_values_if_imputer_strategy_none(feature_df):\n pipeline, get_col_names = create_pipeline(feature_df,\n imputer_strategy=None,\n scaler=FunctionTransformer,\n scaler_args={'validate': False})\n\n transformed = pipeline.fit_transform(feature_df)\n\n assert nan_value_count(feature_df.values) == nan_value_count(transformed)\n assert len(feature_df.columns) == len(get_col_names())\n\n\ndef test_pipeline_must_reorder_columns(feature_df):\n pipeline, get_col_names = create_pipeline(feature_df,\n imputer_strategy='mean',\n imputer_cols=['flow_duration', 'flow_pkts_s'],\n scaler=FunctionTransformer,\n scaler_args={'validate': False})\n\n _ = pipeline.fit_transform(feature_df)\n column_names = get_col_names()\n\n assert len(feature_df.columns) == len(column_names)\n assert_array_equal(column_names[:2], ['flow_duration', 'flow_pkts_s'])\n\n\ndef test_pipeline_must_impute_all_missing_values_with_mean(feature_df):\n pipeline, get_col_names = create_pipeline(feature_df,\n imputer_strategy='mean',\n scaler=FunctionTransformer,\n scaler_args={'validate': False})\n transformed = pipeline.fit_transform(feature_df)\n\n col_idx = np.where(get_col_names() == 'flow_duration')[0]\n nan_idx = np.where(np.isnan(feature_df.flow_duration.values))[0]\n\n assert len(nan_idx) == 10\n assert np.unique(transformed[nan_idx, col_idx]) == feature_df.flow_duration.mean()\n\n\ndef test_pipeline_must_impute_all_missing_values_with_median(feature_df):\n pipeline, get_col_names = create_pipeline(feature_df,\n imputer_strategy='median',\n scaler=FunctionTransformer,\n scaler_args={'validate': False})\n transformed = pipeline.fit_transform(feature_df)\n\n col_idx = np.where(get_col_names() == 'flow_duration')[0]\n nan_idx = np.where(np.isnan(feature_df.flow_duration.values))[0]\n\n assert len(nan_idx) == 10\n assert np.unique(transformed[nan_idx, col_idx]) == feature_df.flow_duration.median()\n\n\ndef test_pipeline_must_scale_all_values(feature_df):\n pipeline, _ = create_pipeline(feature_df, scaler=MinMaxScaler)\n transformed = pipeline.fit_transform(feature_df)\n\n assert np.min(transformed) == 0\n assert np.max(transformed) == 1\n\n\ndef test_pipeline_must_one_hot_encode_categorical_values(feature_df):\n nr_categories = 3\n pipeline, _ = create_pipeline(feature_df, cat_cols=['protocol'])\n transformed = pipeline.fit_transform(feature_df)\n\n one_hot_encoded = transformed[:, -nr_categories:]\n\n print(np.unique(one_hot_encoded))\n\n assert transformed.shape[1] == feature_df.shape[1] + (nr_categories - 1)\n assert_array_equal(np.unique(one_hot_encoded), [0., 1.])\n" }, { "path": "tests/validation_data/validation.csv", "content": ",dst_port,protocol,timestamp,flow_duration,tot_fwd_pkts,tot_bwd_pkts,totlen_fwd_pkts,totlen_bwd_pkts,fwd_pkt_len_max,fwd_pkt_len_min,fwd_pkt_len_mean,fwd_pkt_len_std,bwd_pkt_len_max,bwd_pkt_len_min,bwd_pkt_len_mean,bwd_pkt_len_std,flow_byts_s,flow_pkts_s,flow_iat_mean,flow_iat_std,flow_iat_max,flow_iat_min,fwd_iat_tot,fwd_iat_mean,fwd_iat_std,fwd_iat_max,fwd_iat_min,bwd_iat_tot,bwd_iat_mean,bwd_iat_std,bwd_iat_max,bwd_iat_min,fwd_psh_flags,bwd_psh_flags,fwd_urg_flags,bwd_urg_flags,fwd_header_len,bwd_header_len,fwd_pkts_s,bwd_pkts_s,pkt_len_min,pkt_len_max,pkt_len_mean,pkt_len_std,pkt_len_var,fin_flag_cnt,syn_flag_cnt,rst_flag_cnt,psh_flag_cnt,ack_flag_cnt,urg_flag_cnt,cwe_flag_count,ece_flag_cnt,down_up_ratio,pkt_size_avg,fwd_seg_size_avg,bwd_seg_size_avg,fwd_byts_b_avg,fwd_pkts_b_avg,fwd_blk_rate_avg,bwd_byts_b_avg,bwd_pkts_b_avg,bwd_blk_rate_avg,subflow_fwd_pkts,subflow_fwd_byts,subflow_bwd_pkts,subflow_bwd_byts,init_fwd_win_byts,init_bwd_win_byts,fwd_act_data_pkts,fwd_seg_size_min,active_mean,active_std,active_max,active_min,idle_mean,idle_std,idle_max,idle_min,label\n246433,0,0,10/01/1970 03:04:26,-188505000000,9,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-4.7744091668656e-05,-23563125000.0,279482181562.075,324807000000.0,-449709000000.0,-188505000000.0,-23563125000.0,279482181562.075,324807000000.0,-449709000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,9,0,0,0,-1,-1,0,0,0.0,0.0,0.0,0.0,197474750000.0,141251680160.80402,324807000000.0,21756000000.0,Benign\n692527,80,6,16/02/2018 01:47:37,66,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,30303.0303,66.0,0.0,66.0,66.0,66.0,66.0,0.0,66.0,66.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,30303.0303,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,225,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Hulk\n6400783,443,6,20/02/2018 10:42:55,60112372,8,6,1191.0,3977.0,821.0,0.0,148.875,284.329505780077,1460.0,0.0,662.8333333333333,670.4880063555697,85.97231864348991,0.2328971480280299,4624028.615384616,16624056.10338419,59952200.0,1.0,60112372.0,8587481.714285715,22649728.45845771,59952200.0,270.0,75209.0,15041.8,13802.04190328373,27358.0,1.0,0,0,0,0,172,132,0.13308408458744567,0.09981306344058424,0.0,1460.0,344.53333333333336,524.1676213179285,274751.69523809524,0,0,0,1,0,0,0,0,0.0,369.1428571428572,148.875,662.8333333333334,0,0,0,0,0,0,8,1191,6,3977,8192,1022,3,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n1578,80,6,23/02/2018 03:16:50,5017082,4,4,709.0,1910.0,709.0,0.0,177.25,354.5,1910.0,0.0,477.5,955.0,522.0165825473999,1.5945523713,716726.0,1890471.37373152,5003905.0,19.0,13177.0,4392.3333333333,4708.1925760671,9551.0,327.0,5017056.0,1672352.0,2885525.99413365,5004268.0,2917.0,0,0,0,0,92,92,0.7972761856,0.7972761856,0.0,1910.0,291.0,650.8313913757,423581.5,0,0,1,1,0,0,0,1,1.0,327.375,177.25,477.5,0,0,0,0,0,0,4,709,4,1910,8192,222,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SQL Injection\n846479,80,6,21/02/2018 02:28:49,15009,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,133.2533813,15009.0,0.0,15009.0,15009.0,15009.0,15009.0,0.0,15009.0,15009.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,133.2533813,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,32738,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-HOIC\n367,80,6,15/02/2018 09:27:54,11966042,4,4,523.0,662.0,523.0,0.0,130.75,261.5,662.0,0.0,165.5,331.0,99.03023907149999,0.668558576,1709434.57142857,2973041.09852111,6960924.0,3.0,6962589.0,2320863.0,4018704.30066222,6961263.0,294.0,11966039.0,3988679.66666667,3589126.81538342,6960924.0,1327.0,0,0,0,0,136,136,0.334279288,0.334279288,0.0,662.0,131.6666666667,263.568776603,69468.5,0,0,0,1,0,0,0,0,1.0,148.125,130.75,165.5,0,0,0,0,0,0,4,523,4,662,26883,219,1,32,1330.0,0.0,1330.0,1330.0,6960924.0,0.0,6960924.0,6960924.0,DoS attacks-GoldenEye\n297220,22,6,14/02/2018 02:51:41,7,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,285714.28571428603,7.0,0.0,7.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,32,32,142857.14285714302,142857.14285714302,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,1,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,241,230,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SSH-Bruteforce\n364950,22,6,14/02/2018 03:30:26,6,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,333333.333333333,6.0,0.0,6.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,32,32,166666.666666667,166666.666666667,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,1,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,241,230,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SSH-Bruteforce\n160544,80,6,20/02/2018 10:48:44,1529522,3,4,20.0,964.0,20.0,0.0,6.666666667,11.54700538,964.0,0.0,241.0,482.0,643.3382455,4.57659321,254920.3333,624236.1423,1529137.0,4.0,342.0,171.0,206.4751801,317.0,25.0,1529518.0,509839.3333,882737.6865,1529137.0,38.0,0,0,0,0,72,92,1.96139709,2.61519612,0.0,964.0,123.0,339.8873763,115523.4286,0,0,1,1,0,0,0,1,1.0,140.5714286,6.666666667,241.0,0,0,0,0,0,0,3,20,4,964,8192,211,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDoS attacks-LOIC-HTTP\n1568,80,6,23/02/2018 03:16:01,5009382,5,3,733.0,1910.0,733.0,0.0,146.6,327.8075655015,1910.0,0.0,636.6666666666999,1102.7390141522,527.6099926099,1.5970033828999999,715626.0,1890042.86998717,5001839.0,19.0,5009382.0,1252345.5,2499662.78920771,5001839.0,400.0,7032.0,3516.0,999.8489885978,4223.0,2809.0,0,0,0,0,112,72,0.9981271143000001,0.5988762686,0.0,1910.0,293.6666666667,652.8043351572001,426153.5,0,0,1,1,0,0,0,1,0.0,330.375,146.6,636.6666666666999,0,0,0,0,0,0,5,733,3,1910,8192,222,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SQL Injection\n951187,80,6,16/02/2018 01:48:33,148,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,13513.513509999999,148.0,0.0,148.0,148.0,148.0,148.0,0.0,148.0,148.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,13513.513509999999,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,225,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Hulk\n5769402,53400,6,20/02/2018 04:18:51,0,2,0,31.0,0.0,31.0,0.0,15.5,21.92031021678297,0.0,0.0,0.0,0.0,inf,inf,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,40,0,0.0,0.0,0.0,31.0,20.666666666666664,17.8978583448784,320.33333333333337,0,1,0,0,1,0,0,0,0.0,31.0,15.5,0.0,0,0,0,0,0,0,2,31,0,0,946,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n412184,0,0,12/01/1970 09:44:12,-529801000000,505,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0009531881,-1051192460.31746,413673944278.105,948431000000.0,-940829000000.0,-529801000000.0,-1051192460.31746,413673944278.105,948431000000.0,-940829000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,505,0,0,0,-1,-1,0,0,0.0,0.0,0.0,0.0,333755554687.5,237751721149.946,948431000000.0,4908000000.0,Benign\n2092624,52578,6,20/02/2018 02:35:46,8,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,250000.0,8.0,0.0,8.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,20,20,125000.0,125000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,1,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,172,257,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n517519,80,6,16/02/2018 01:46:59,25303,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,79.04201083,25303.0,0.0,25303.0,25303.0,25303.0,25303.0,0.0,25303.0,25303.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,79.04201083,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,225,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Hulk\n35435,80,6,15/02/2018 09:52:24,57787567,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0346095208,57787567.0,0.0,57787567.0,57787567.0,57787567.0,57787567.0,0.0,57787567.0,57787567.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,0.0346095208,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,226,-1,0,32,0.0,0.0,0.0,0.0,57787567.0,0.0,57787567.0,57787567.0,DoS attacks-GoldenEye\n480791,80,6,21/02/2018 02:21:09,7491,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,266.9870511,7491.0,0.0,7491.0,7491.0,7491.0,7491.0,0.0,7491.0,7491.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,266.9870511,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,32738,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-HOIC\n1758,80,17,21/02/2018 10:23:28,119858121,101835,0,3258720.0,0.0,32.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,27188.14522,849.6295382000001,1176.9951199999998,23482.24695,671369.0,0.0,120000000.0,1176.9951199999998,23482.24695,671369.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,814680,0,849.6295382000001,0.0,32.0,32.0,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,32.00031423,32.0,0.0,0,0,0,0,0,0,101835,3258720,0,0,-1,-1,101834,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-LOIC-UDP\n609141,85,6,28/02/2018 11:59:12,2,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1000000.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,24,20,500000.0,500000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,1024,0,0,24,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Infilteration\n128847,21,6,14/02/2018 11:38:04,1,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2000000.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,1000000.0,1000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,FTP-BruteForce\n1186,80,6,22/02/2018 02:01:58,56912882,203,104,56330.0,189991.0,680.0,0.0,277.4876847291,292.86878012569997,1935.0,0.0,1826.8365384615,320.4275393476,4328.0359620516,5.3942093461,185989.81045751603,363760.26126485,1053742.0,3.0,56910623.0,281735.757425742,416749.533844595,1053742.0,3.0,56912860.0,552552.038834951,440525.034371975,1056429.0,31.0,0,0,0,0,4072,2092,3.56685504,1.8273543062,0.0,1935.0,799.7435064935,794.1585035653,630687.728785059,0,0,1,1,0,0,0,1,0.0,802.348534202,277.4876847291,1826.8365384615,0,0,0,0,0,0,203,56330,104,189991,8192,1284,101,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -XSS\n4803254,53,17,20/02/2018 09:15:42,1137,1,1,46.0,62.0,46.0,46.0,46.0,0.0,62.0,62.0,62.0,0.0,94986.80738786279,1759.0149516270885,1137.0,0.0,1137.0,1137.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,879.5074758135443,879.5074758135443,46.0,62.0,51.33333333333334,9.237604307034012,85.33333333333333,0,0,0,0,0,0,0,0,1.0,77.0,46.0,62.0,0,0,0,0,0,0,1,46,1,62,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n671,500,17,23/02/2018 10:55:43,89479799,6,0,3000.0,0.0,500.0,500.0,500.0,0.0,0.0,0.0,0.0,0.0,33.5271204621,0.06705424089999999,17895959.8,15345252.5303685,41990569.0,4000290.0,89479799.0,17895959.8,15345252.5303685,41990569.0,4000290.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,48,0,0.06705424089999999,0.0,500.0,500.0,500.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,583.3333333333001,500.0,0.0,0,0,0,0,0,0,6,3000,0,0,-1,-1,5,8,4000290.0,0.0,4000290.0,4000290.0,21369877.25,15281198.163045902,41990569.0,7200330.0,Brute Force -Web\n410957,0,0,08/01/1970 07:32:33,-681402000000,3,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-4.4026874003892e-06,-340701000000.0,256344592956.434,-159438000000.0,-521964000000.0,-681402000000.0,-340701000000.0,256344592956.434,-159438000000.0,-521964000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,3,0,0,0,-1,-1,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n78367,21,6,16/02/2018 10:51:30,2,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1000000.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,500000.0,500000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-SlowHTTPTest\n63963,21,6,14/02/2018 11:05:30,1,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2000000.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,1000000.0,1000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,FTP-BruteForce\n381,500,17,23/02/2018 10:08:57,89479510,6,0,3000.0,0.0,500.0,500.0,500.0,0.0,0.0,0.0,0.0,0.0,33.5272287477,0.0670544575,17895902.0,15345149.2125237,41990265.0,4000279.0,89479510.0,17895902.0,15345149.2125237,41990265.0,4000279.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,48,0,0.0670544575,0.0,500.0,500.0,500.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,583.3333333333001,500.0,0.0,0,0,0,0,0,0,6,3000,0,0,-1,-1,5,8,4000279.0,0.0,4000279.0,4000279.0,21369807.75,15281077.5603992,41990265.0,7200304.0,Brute Force -Web\n273580,80,6,21/02/2018 02:16:50,3422,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,584.4535359,3422.0,0.0,3422.0,3422.0,3422.0,3422.0,0.0,3422.0,3422.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,584.4535359,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,32738,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-HOIC\n245880,8080,6,02/03/2018 11:06:06,553,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3616.636528,553.0,0.0,553.0,553.0,553.0,553.0,0.0,553.0,553.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,3616.636528,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,256,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bot\n110301,80,6,21/02/2018 02:13:26,28092,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,71.19464616,28092.0,0.0,28092.0,28092.0,28092.0,28092.0,0.0,28092.0,28092.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,71.19464616,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,32738,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-HOIC\n363804,22,6,14/02/2018 03:31:25,7,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,285714.28571428603,7.0,0.0,7.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,32,32,142857.14285714302,142857.14285714302,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,1,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,241,230,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SSH-Bruteforce\n169065,8080,6,02/03/2018 10:52:02,546,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3663.0036630000004,546.0,0.0,546.0,546.0,546.0,546.0,0.0,546.0,546.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,3663.0036630000004,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,256,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bot\n450476,80,6,20/02/2018 10:45:32,2041230,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9798013940000001,2041230.0,0.0,2041230.0,2041230.0,2041230.0,2041230.0,0.0,2041230.0,2041230.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,0.9798013940000001,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2049,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDoS attacks-LOIC-HTTP\n43713,21,6,16/02/2018 10:34:07,2,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1000000.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,500000.0,500000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-SlowHTTPTest\n176058,21,6,14/02/2018 12:01:46,2,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1000000.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,500000.0,500000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,FTP-BruteForce\n396569,1577,6,28/02/2018 02:18:57,9045530,3,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.331655525,4522765.0,2121337.3140000002,6022777.0,3022753.0,9045530.0,4522765.0,2121337.3140000002,6022777.0,3022753.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,72,0,0.331655525,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,3,0,0,0,8192,-1,0,24,3022753.0,0.0,3022753.0,3022753.0,6022777.0,0.0,6022777.0,6022777.0,Infilteration\n1043329,21,6,16/02/2018 10:33:55,3,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,666666.6667,3.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,333333.3333,333333.3333,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-SlowHTTPTest\n394,80,6,22/02/2018 10:29:02,56623657,154,104,54995.0,72491.0,646.0,0.0,357.11038961040003,272.0749262837,1069.0,0.0,697.0288461538,368.2731008772,2251.4617874292003,4.5563994569,220325.51361867698,414280.317925604,1134701.0,4.0,56622051.0,370078.764705883,483062.049463202,1134701.0,291.0,56623653.0,549744.203883495,555565.911708412,1191736.0,30.0,0,0,0,0,3092,2092,2.7197113037,1.8366881532,0.0,1069.0,492.2239382239,356.0719976167,126787.267486756,0,0,1,1,0,0,0,1,0.0,494.1317829457,357.11038961040003,697.0288461538,0,0,0,0,0,0,154,54995,104,72491,8192,1228,101,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -Web\n51607,80,6,15/02/2018 11:05:54,108094126,15,3,2530.0,0.0,230.0,0.0,168.6666666667,105.2796728899,0.0,0.0,0.0,0.0,23.405527142199997,0.166521537,6358478.0,13511315.902151,53247509.0,1.0,108094125.0,7721008.92857143,14606673.4113702,53247509.0,359.0,107070860.0,53535430.0,31870350.5603274,76071171.0,30999689.0,0,0,0,0,496,100,0.1387679475,0.027753589500000002,0.0,230.0,133.15789473680002,116.6691729054,13611.6959064327,0,0,0,1,0,0,0,0,0.0,140.5555555556,168.6666666667,0.0,0,0,0,0,0,0,15,2530,3,0,26883,0,11,32,6155889.5,2244808.76471928,7743209.0,4568570.0,23945586.5,20634791.4660677,53247509.0,6655840.0,DoS attacks-Slowloris\n336105,22,6,14/02/2018 03:24:02,389630,22,20,1944.0,2665.0,640.0,0.0,88.36363636360001,137.8690011274,976.0,0.0,133.25,268.77125374959996,11829.1712650463,107.7945743398,9503.1707317073,31938.7715245142,199396.0,3.0,388696.0,18509.3333333333,43985.9004003935,199396.0,268.0,389605.0,20505.5263157895,54602.5355713333,236178.0,7.0,0,0,0,0,712,648,56.4638246542,51.330749685600004,0.0,976.0,107.1860465116,207.28152650259997,42965.6312292359,0,0,0,1,0,0,0,0,0.0,109.7380952381,88.36363636360001,133.25,0,0,0,0,0,0,22,1944,20,2665,26883,230,16,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SSH-Bruteforce\n38008,80,6,15/02/2018 11:18:00,107675929,15,3,2530.0,0.0,230.0,0.0,168.6666666667,105.2796728899,0.0,0.0,0.0,0.0,23.496430664600002,0.1671682814,6333878.17647059,13481163.1724836,53247545.0,24.0,107675905.0,7691136.07142857,14575344.855252301,53247545.0,423.0,106655339.0,53327669.5,31578419.404394202,75656984.0,30998355.0,0,0,0,0,496,100,0.1393069012,0.0278613802,0.0,230.0,133.15789473680002,116.6691729054,13611.6959064327,0,0,0,1,0,0,0,0,0.0,140.5555555556,168.6666666667,0.0,0,0,0,0,0,0,15,2530,3,0,26883,0,11,32,6153764.0,2017753.24174637,7580531.0,4726997.0,23842094.25,20643314.2588985,53247545.0,6655941.0,DoS attacks-Slowloris\n1624,80,6,22/02/2018 04:17:35,35,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,57142.8571428571,35.0,0.0,35.0,35.0,35.0,35.0,0.0,35.0,35.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,57142.8571428571,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2053,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SQL Injection\n1284,80,6,22/02/2018 02:22:33,60084541,203,104,56083.0,189899.0,680.0,0.0,276.2709359606,291.6989844299,1935.0,0.0,1825.9519230769001,320.3666400765,4093.9315821685,5.109467342,196354.709150327,369313.036791724,1060917.0,19.0,60082355.0,297437.400990099,420417.499999185,1060917.0,463.0,60084510.0,583344.757281553,427714.478601694,1063567.0,32.0,0,0,0,0,4072,2092,3.3785728679,1.7308944741999999,0.0,1935.0,798.6428571429,794.0085057558999,630449.507212657,0,0,1,1,0,0,0,1,0.0,801.2442996742999,276.2709359606,1825.9519230769001,0,0,0,0,0,0,203,56083,104,189899,8192,1281,101,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -XSS\n204625,8080,6,02/03/2018 11:12:42,9309,3,4,326.0,129.0,326.0,0.0,108.66666670000001,188.2161878,112.0,0.0,32.25,53.7672453,48877.43044,751.9604684,1551.5,3317.3284280000003,8308.0,16.0,610.0,305.0,377.59502119999996,572.0,38.0,8753.0,2917.666667,4672.215784,8308.0,28.0,0,0,0,0,72,92,322.2687722,429.6916962,0.0,326.0,56.875,115.4066568,13318.69643,0,0,1,1,0,0,0,1,1.0,65.0,108.66666670000001,32.25,0,0,0,0,0,0,3,326,4,129,8192,219,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bot\n2680,80,6,15/02/2018 09:29:47,5001593,4,4,537.0,972.0,537.0,0.0,134.25,268.5,972.0,0.0,243.0,486.0,301.7038771447,1.5994904024,714513.2857142859,1890079.63669909,5000811.0,5.0,782.0,260.6666666667,241.50431328099998,503.0,20.0,5001588.0,1667196.0,2887223.04120967,5001074.0,234.0,0,0,0,0,136,136,0.7997452012,0.7997452012,0.0,972.0,167.6666666667,350.0257133412,122518.0,0,0,0,1,0,0,0,0,1.0,188.625,134.25,243.0,0,0,0,0,0,0,4,537,4,972,26883,219,1,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-GoldenEye\n6808254,49859,6,20/02/2018 10:34:55,0,2,0,31.0,0.0,31.0,0.0,15.5,21.92031021678297,0.0,0.0,0.0,0.0,inf,inf,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,40,0,0.0,0.0,0.0,31.0,20.666666666666664,17.8978583448784,320.33333333333337,0,1,0,0,1,0,0,0,0.0,31.0,15.5,0.0,0,0,0,0,0,0,2,31,0,0,290,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n48590,80,6,15/02/2018 09:50:24,1876380,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0658821774,1876380.0,0.0,1876380.0,1876380.0,1876380.0,1876380.0,0.0,1876380.0,1876380.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,1.0658821774,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,226,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-GoldenEye\n4754739,49956,6,20/02/2018 08:51:22,0,2,0,31.0,0.0,31.0,0.0,15.5,21.92031021678297,0.0,0.0,0.0,0.0,inf,inf,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,40,0,0.0,0.0,0.0,31.0,20.666666666666664,17.8978583448784,320.33333333333337,0,1,0,0,1,0,0,0,0.0,31.0,15.5,0.0,0,0,0,0,0,0,2,31,0,0,946,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n531663,80,6,20/02/2018 10:41:42,16746826,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.119425615,16700000.0,0.0,16700000.0,16700000.0,16700000.0,16700000.0,0.0,16700000.0,16700000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,0.119425615,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2049,-1,0,20,0.0,0.0,0.0,0.0,16700000.0,0.0,16700000.0,16700000.0,DDoS attacks-LOIC-HTTP\n555355,53,17,28/02/2018 02:36:24,875,1,1,40.0,139.0,40.0,40.0,40.0,0.0,139.0,139.0,139.0,0.0,204571.4286,2285.714286,875.0,0.0,875.0,875.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,1142.857143,1142.857143,40.0,139.0,73.0,57.15767665,3267.0,0,0,0,0,0,0,0,0,1.0,109.5,40.0,139.0,0,0,0,0,0,0,1,40,1,139,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Infilteration\n146378,8080,6,02/03/2018 02:03:34,585,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3418.803419,585.0,0.0,585.0,585.0,585.0,585.0,0.0,585.0,585.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,3418.803419,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2052,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bot\n211712,22,6,14/02/2018 02:24:07,388030,22,22,1912.0,2665.0,640.0,0.0,86.9090909091,137.68802178110002,976.0,0.0,121.13636363639999,258.64156029139997,11795.4797309486,113.39329433290001,9023.953488372099,21149.288549278503,107086.0,5.0,386716.0,18415.0476190476,31544.3523272807,107086.0,204.0,388022.0,18477.2380952381,36808.4626287281,144643.0,7.0,0,0,0,0,712,712,56.69664716649999,56.69664716649999,0.0,976.0,101.71111111110001,203.73719951119998,41508.8464646465,0,0,0,1,0,0,0,0,1.0,104.02272727270001,86.9090909091,121.13636363639999,0,0,0,0,0,0,22,1912,22,2665,26883,230,16,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SSH-Bruteforce\n122001,53,17,01/03/2018 02:12:02,289,1,1,68.0,196.0,68.0,68.0,68.0,0.0,196.0,196.0,196.0,0.0,913494.8097,6920.415225,289.0,0.0,289.0,289.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,3460.2076119999997,3460.2076119999997,68.0,196.0,110.66666670000001,73.90083446,5461.333333,0,0,0,0,0,0,0,0,1.0,166.0,68.0,196.0,0,0,0,0,0,0,1,68,1,196,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Infilteration\n1654,80,6,22/02/2018 04:22:05,22,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,90909.0909090909,22.0,0.0,22.0,22.0,22.0,22.0,0.0,22.0,22.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,90909.0909090909,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2051,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SQL Injection\n689,500,17,23/02/2018 10:58:28,89479543,6,0,3000.0,0.0,500.0,500.0,500.0,0.0,0.0,0.0,0.0,0.0,33.5272163829,0.06705443280000001,17895908.6,15345116.125980802,41990230.0,4000299.0,89479543.0,17895908.6,15345116.125980802,41990230.0,4000299.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,48,0,0.06705443280000001,0.0,500.0,500.0,500.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,583.3333333333001,500.0,0.0,0,0,0,0,0,0,6,3000,0,0,-1,-1,5,8,4000299.0,0.0,4000299.0,4000299.0,21369811.0,15281038.337155301,41990230.0,7200455.0,Brute Force -Web\n719455,51607,6,15/02/2018 01:03:11,0,2,0,31.0,0.0,31.0,0.0,15.5,21.920310216799997,0.0,0.0,0.0,0.0,inf,inf,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,40,0,0.0,0.0,0.0,31.0,20.6666666667,17.8978583449,320.3333333333,0,1,0,0,1,0,0,0,0.0,31.0,15.5,0.0,0,0,0,0,0,0,2,31,0,0,66,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n437953,80,6,20/02/2018 11:06:26,20435529,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.097868766,20400000.0,0.0,20400000.0,20400000.0,20400000.0,20400000.0,0.0,20400000.0,20400000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,0.097868766,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2049,-1,0,20,0.0,0.0,0.0,0.0,20400000.0,0.0,20400000.0,20400000.0,DDoS attacks-LOIC-HTTP\n335662,8080,6,02/03/2018 12:28:15,501,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3992.015968,501.0,0.0,501.0,501.0,501.0,501.0,0.0,501.0,501.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,3992.015968,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2052,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bot\n211321,8080,6,02/03/2018 11:52:24,505,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3960.3960399999996,505.0,0.0,505.0,505.0,505.0,505.0,0.0,505.0,505.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,3960.3960399999996,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2052,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bot\n1602,80,6,22/02/2018 04:14:30,5061043,5,5,1295.0,1411.0,649.0,0.0,259.0,354.6519420502,1047.0,0.0,282.2,455.66456961230006,534.672398555,1.9758773043,562338.111111111,1645250.29767673,4949367.0,21.0,111676.0,27919.0,23530.8576270961,57308.0,412.0,5061020.0,1265255.0,2493288.32695472,5005136.0,790.0,0,0,0,0,112,112,0.9879386522,0.9879386522,0.0,1047.0,246.0,374.3722746145,140154.6,0,0,1,1,0,0,0,1,1.0,270.6,259.0,282.2,0,0,0,0,0,0,5,1295,5,1411,8192,231,2,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SQL Injection\n5403857,53,17,20/02/2018 09:43:12,41223,2,2,78.0,278.0,39.0,39.0,39.0,0.0,139.0,139.0,139.0,0.0,8635.955655823205,97.03320961599108,13741.0,13999.333019826336,27986.0,1.0,27986.0,27986.0,0.0,27986.0,27986.0,1.0,1.0,0.0,1.0,1.0,0,0,0,0,16,16,48.51660480799554,48.51660480799554,39.0,139.0,79.0,54.77225575051661,3000.0,0,0,0,0,0,0,0,0,1.0,98.75,39.0,139.0,0,0,0,0,0,0,2,78,2,278,-1,-1,1,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n939871,80,6,16/02/2018 01:48:31,40520,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,49.35834156,40520.0,0.0,40520.0,40520.0,40520.0,40520.0,0.0,40520.0,40520.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,49.35834156,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,225,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Hulk\n246437,0,0,12/01/1970 06:40:49,-578768000000,48,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-8.29347856135792e-05,-12314212765.9575,394984133805.008,722821000000.0,-881562000000.0,-578768000000.0,-12314212765.9575,394984133805.008,722821000000.0,-881562000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,48,0,0,0,-1,-1,0,0,0.0,0.0,0.0,0.0,395571421052.63104,189762242840.677,722821000000.0,6352000000.0,Benign\n678,80,6,22/02/2018 11:15:28,5008335,4,4,646.0,364.0,646.0,0.0,161.5,323.0,364.0,0.0,91.0,182.0,201.6638264014,1.5973372388,715476.4285714291,1867795.80315942,4950997.0,17.0,57338.0,19112.6666666667,32183.8581641998,56275.0,385.0,5008310.0,1669436.66666667,2888843.70781362,5005186.0,1055.0,0,0,0,0,92,92,0.7986686194,0.7986686194,0.0,646.0,112.22222222219999,233.5774913052,54558.4444444445,0,0,1,1,0,0,0,1,1.0,126.25,161.5,91.0,0,0,0,0,0,0,4,646,4,364,8192,221,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -Web\n883114,80,6,21/02/2018 02:29:37,1567,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1276.3241859999998,1567.0,0.0,1567.0,1567.0,1567.0,1567.0,0.0,1567.0,1567.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,1276.3241859999998,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,32738,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-HOIC\n276577,22,6,14/02/2018 02:41:45,6,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,333333.333333333,6.0,0.0,6.0,6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,32,32,166666.666666667,166666.666666667,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,1,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,241,230,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SSH-Bruteforce\n354,80,6,23/02/2018 10:07:19,83,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,24096.3855421687,83.0,0.0,83.0,83.0,83.0,83.0,0.0,83.0,83.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,24096.3855421687,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2047,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -Web\n1931,80,17,21/02/2018 10:25:17,119880859,95163,0,3045216.0,0.0,32.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,25402.02018,793.8131308,1259.755564,26978.779639999997,908182.0,1.0,120000000.0,1259.755564,26978.779639999997,908182.0,1.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,761304,0,793.8131308,0.0,32.0,32.0,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,32.00033627,32.0,0.0,0,0,0,0,0,0,95163,3045216,0,0,-1,-1,95162,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-LOIC-UDP\n1337,80,6,23/02/2018 02:07:11,951,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2103.0494216614,951.0,0.0,951.0,951.0,951.0,951.0,0.0,951.0,951.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,2103.0494216614,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2053,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -XSS\n6443007,53,17,20/02/2018 09:58:59,25306,1,1,29.0,77.0,29.0,29.0,29.0,0.0,77.0,77.0,77.0,0.0,4188.729945467479,79.03264048051845,25306.0,0.0,25306.0,25306.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,39.516320240259226,39.516320240259226,29.0,77.0,45.0,27.71281292110204,768.0,0,0,0,0,0,0,0,0,1.0,67.5,29.0,77.0,0,0,0,0,0,0,1,29,1,77,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n5749510,52773,6,20/02/2018 04:35:58,0,2,0,31.0,0.0,31.0,0.0,15.5,21.92031021678297,0.0,0.0,0.0,0.0,inf,inf,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,40,0,0.0,0.0,0.0,31.0,20.666666666666664,17.8978583448784,320.33333333333337,0,1,0,0,1,0,0,0,0.0,31.0,15.5,0.0,0,0,0,0,0,0,2,31,0,0,946,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n143939,53,17,15/02/2018 03:25:13,248,1,1,44.0,108.0,44.0,44.0,44.0,0.0,108.0,108.0,108.0,0.0,612903.225806452,8064.516129032299,248.0,0.0,248.0,248.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,4032.2580645160997,4032.2580645160997,44.0,108.0,65.3333333333,36.9504172281,1365.3333333333,0,0,0,0,0,0,0,0,1.0,98.0,44.0,108.0,0,0,0,0,0,0,1,44,1,108,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n52017,80,6,15/02/2018 09:56:35,12631636,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1583326182,12631636.0,0.0,12631636.0,12631636.0,12631636.0,12631636.0,0.0,12631636.0,12631636.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,0.1583326182,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,226,-1,0,32,0.0,0.0,0.0,0.0,12631636.0,0.0,12631636.0,12631636.0,DoS attacks-GoldenEye\n1241737,53,17,20/02/2018 11:23:02,768,1,1,30.0,132.0,30.0,30.0,30.0,0.0,132.0,132.0,132.0,0.0,210937.5,2604.1666666666665,768.0,0.0,768.0,768.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,1302.0833333333333,1302.0833333333333,30.0,132.0,64.0,58.88972745734182,3468.0,0,0,0,0,0,0,0,0,1.0,96.0,30.0,132.0,0,0,0,0,0,0,1,30,1,132,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n444883,443,6,28/02/2018 11:43:25,23283526,5,1,123.0,46.0,46.0,0.0,24.6,23.27659769,46.0,46.0,46.0,0.0,7.2583508189999995,0.257692928,4656705.2,10200000.0,23000000.0,24.0,23300000.0,5820881.5,11400000.0,23000000.0,24.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,100,20,0.214744107,0.042948821,0.0,46.0,30.71428571,21.68497395,470.2380952,0,1,0,0,1,0,0,0,0.0,35.83333333,24.6,46.0,0,0,0,0,0,0,5,123,1,46,258,196,2,20,325972.0,0.0,325972.0,325972.0,23000000.0,0.0,23000000.0,23000000.0,Infilteration\n5342704,53,17,20/02/2018 09:47:14,494,1,1,44.0,108.0,44.0,44.0,44.0,0.0,108.0,108.0,108.0,0.0,307692.3076923077,4048.582995951417,494.0,0.0,494.0,494.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,2024.2914979757086,2024.2914979757086,44.0,108.0,65.33333333333333,36.95041722813605,1365.3333333333333,0,0,0,0,0,0,0,0,1.0,98.0,44.0,108.0,0,0,0,0,0,0,1,44,1,108,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n3576,80,17,21/02/2018 10:41:22,97016388,108832,0,3482624.0,0.0,32.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,35897.27542,1121.789857,891.4407476,18864.27151,597581.0,0.0,97000000.0,891.4407476,18864.27151,597581.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,870656,0,1121.789857,0.0,32.0,32.0,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,32.00029403,32.0,0.0,0,0,0,0,0,0,108832,3482624,0,0,-1,-1,108831,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-LOIC-UDP\n28735,80,6,15/02/2018 11:00:12,100009363,5,4,246.0,0.0,230.0,0.0,49.2,101.1493944619,0.0,0.0,0.0,0.0,2.4597696918,0.0899915741,12501170.375,35354591.9589834,99999321.0,2.0,100009361.0,25002340.25,49997988.7003813,99999323.0,64.0,100009360.0,33336453.333333302,57731738.8080534,99999323.0,365.0,0,0,0,0,168,136,0.049995318899999995,0.0399962552,0.0,230.0,24.6,72.2467992371,5219.6,0,0,0,1,0,0,0,0,0.0,27.3333333333,49.2,0.0,0,0,0,0,0,0,5,246,4,0,26883,219,3,32,10040.0,0.0,10040.0,10040.0,99999321.0,0.0,99999321.0,99999321.0,DoS attacks-Slowloris\n431581,80,6,21/02/2018 02:20:08,1139,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1755.926251,1139.0,0.0,1139.0,1139.0,1139.0,1139.0,0.0,1139.0,1139.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,1755.926251,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,32738,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-HOIC\n42418,80,6,15/02/2018 11:32:46,108293626,15,3,2530.0,0.0,230.0,0.0,168.6666666667,105.2796728899,0.0,0.0,0.0,0.0,23.362409159699997,0.1662147687,6370213.29411765,13528277.5027673,53223538.0,19.0,108293607.0,7735257.64285714,14625075.647072699,53223538.0,206.0,107267936.0,53633968.0,32009234.8174338,76267915.0,31000021.0,0,0,0,0,496,100,0.1385123073,0.027702461499999997,0.0,230.0,133.15789473680002,116.6691729054,13611.6959064327,0,0,0,1,0,0,0,0,0.0,140.5555555556,168.6666666667,0.0,0,0,0,0,0,0,15,2530,3,0,26883,0,11,32,6047385.5,2705449.23468257,7960427.0,4134344.0,24049709.0,20577353.671217103,53223538.0,6655578.0,DoS attacks-Slowloris\n5563677,53,17,20/02/2018 02:11:33,1624,1,1,42.0,155.0,42.0,42.0,42.0,0.0,155.0,155.0,155.0,0.0,121305.41871921186,1231.5270935960589,1624.0,0.0,1624.0,1624.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,615.7635467980297,615.7635467980297,42.0,155.0,79.66666666666666,65.24058041842771,4256.333333333333,0,0,0,0,0,0,0,0,1.0,119.5,42.0,155.0,0,0,0,0,0,0,1,42,1,155,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n249017,80,6,20/02/2018 10:23:45,34581519,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.057834359,34600000.0,0.0,34600000.0,34600000.0,34600000.0,34600000.0,0.0,34600000.0,34600000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,0.057834359,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2049,-1,0,20,0.0,0.0,0.0,0.0,34600000.0,0.0,34600000.0,34600000.0,DDoS attacks-LOIC-HTTP\n36216,21,6,14/02/2018 10:51:34,1,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2000000.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,1000000.0,1000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,FTP-BruteForce\n40928,80,6,15/02/2018 11:28:57,107628972,15,3,2530.0,0.0,230.0,0.0,168.6666666667,105.2796728899,0.0,0.0,0.0,0.0,23.5066818254,0.16724121460000002,6331116.0,13482491.1827535,53245224.0,14.0,107628958.0,7687782.71428571,14577943.1828405,53245224.0,335.0,106601584.0,53300792.0,31539905.4193433,75602873.0,30998711.0,0,0,0,0,496,100,0.1393676788,0.0278735358,0.0,230.0,133.15789473680002,116.6691729054,13611.6959064327,0,0,0,1,0,0,0,0,0.0,140.5555555556,168.6666666667,0.0,0,0,0,0,0,0,15,2530,3,0,26883,0,11,32,6040579.0,2462944.8427542998,7782144.0,4299014.0,23886950.0,20599427.883054603,53245224.0,6644696.0,DoS attacks-Slowloris\n1567,80,6,23/02/2018 03:15:31,5010187,5,3,696.0,1907.0,696.0,0.0,139.2,311.26066246799996,1907.0,0.0,635.6666666666999,1101.0069633446,519.5414861761,1.5967467881,715741.0,1890980.11395564,5004080.0,19.0,5010187.0,1252546.75,2501022.43341537,5004080.0,409.0,5705.0,2852.5,381.13055505949995,3122.0,2583.0,0,0,0,0,112,72,0.9979667426000001,0.5987800455,0.0,1907.0,289.2222222222,648.8662762422,421027.444444444,0,0,1,1,0,0,0,1,0.0,325.375,139.2,635.6666666666999,0,0,0,0,0,0,5,696,3,1907,8192,221,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SQL Injection\n891186,80,6,21/02/2018 02:29:48,31638,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,63.21512106,31638.0,0.0,31638.0,31638.0,31638.0,31638.0,0.0,31638.0,31638.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,63.21512106,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,32738,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-HOIC\n570,80,6,22/02/2018 10:58:12,19,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,105263.15789473699,19.0,0.0,19.0,19.0,19.0,19.0,0.0,19.0,19.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,105263.15789473699,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2051,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -Web\n4035900,55146,6,20/02/2018 04:44:03,0,2,0,31.0,0.0,31.0,0.0,15.5,21.92031021678297,0.0,0.0,0.0,0.0,inf,inf,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,40,0,0.0,0.0,0.0,31.0,20.666666666666664,17.8978583448784,320.33333333333337,0,1,0,0,1,0,0,0,0.0,31.0,15.5,0.0,0,0,0,0,0,0,2,31,0,0,946,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n1516,80,17,21/02/2018 10:21:08,119656002,135881,0,4348192.0,0.0,32.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,36339.104830000004,1135.597026,880.6005446,17789.137440000002,571156.0,0.0,120000000.0,880.6005446,17789.137440000002,571156.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1087048,0,1135.597026,0.0,32.0,32.0,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,32.000235499999995,32.0,0.0,0,0,0,0,0,0,135881,4348192,0,0,-1,-1,135880,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-LOIC-UDP\n172188,8080,6,02/03/2018 11:16:13,10899,3,4,326.0,129.0,326.0,0.0,108.66666670000001,188.2161878,112.0,0.0,32.25,53.7672453,41746.94926,642.2607579,1816.5,4017.530921,10008.0,17.0,574.0,287.0,329.51176,520.0,54.0,10402.0,3467.333333,5667.2427450000005,10008.0,17.0,0,0,0,0,72,92,275.2546105,367.0061474,0.0,326.0,56.875,115.4066568,13318.69643,0,0,1,1,0,0,0,1,1.0,65.0,108.66666670000001,32.25,0,0,0,0,0,0,3,326,4,129,8192,219,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bot\n128048,53,17,01/03/2018 02:27:20,1832,1,1,34.0,64.0,34.0,34.0,34.0,0.0,64.0,64.0,64.0,0.0,53493.44978,1091.703057,1832.0,0.0,1832.0,1832.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,545.8515284,545.8515284,34.0,64.0,44.0,17.32050808,300.0,0,0,0,0,0,0,0,0,1.0,66.0,34.0,64.0,0,0,0,0,0,0,1,34,1,64,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Infilteration\n5712426,53,17,20/02/2018 02:07:45,18056,1,1,39.0,89.0,39.0,39.0,39.0,0.0,89.0,89.0,89.0,0.0,7089.056269384138,110.76650420912716,18056.0,0.0,18056.0,18056.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,55.38325210456358,55.38325210456358,39.0,89.0,55.66666666666667,28.867513459481287,833.3333333333335,0,0,0,0,0,0,0,0,1.0,83.5,39.0,89.0,0,0,0,0,0,0,1,39,1,89,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n36791,80,6,15/02/2018 09:54:08,1479831,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.3515056787000002,1479831.0,0.0,1479831.0,1479831.0,1479831.0,1479831.0,0.0,1479831.0,1479831.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,1.3515056787000002,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,226,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-GoldenEye\n3525944,0,0,20/02/2018 02:02:38,119278857,52,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4359532050177175,2338801.1176470593,4719753.084021228,31393771.0,562064.0,119278857.0,2338801.1176470593,4719753.084021228,31393771.0,562064.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.4359532050177175,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,52,0,0,0,-1,-1,0,0,9044847.333333334,6812474.093130947,15291631.0,1781036.0,17679801.333333332,12545214.055505086,31393771.0,6782065.0,Benign\n22463,80,6,15/02/2018 09:54:59,11409511,4,4,345.0,972.0,345.0,0.0,86.25,172.5,972.0,0.0,243.0,486.0,115.4300127324,0.7011694016,1629930.14285714,2812590.38617413,6404416.0,4.0,6405098.0,2135032.66666667,3697684.54593849,6404751.0,44.0,11409507.0,3803169.0,3366877.15838565,6404416.0,347.0,0,0,0,0,136,136,0.3505847008,0.3505847008,0.0,972.0,146.3333333333,329.97878719700003,108886.0,0,0,0,1,0,0,0,0,1.0,164.625,86.25,243.0,0,0,0,0,0,0,4,345,4,972,26883,219,1,32,351.0,0.0,351.0,351.0,6404416.0,0.0,6404416.0,6404416.0,DoS attacks-GoldenEye\n1164,80,6,22/02/2018 01:57:11,57162533,203,104,56083.0,189899.0,680.0,0.0,276.2709359606,291.6989844299,1935.0,0.0,1825.9519230769001,320.4033677328,4303.2032887696005,5.3706507373,186805.663398693,363776.72702577,1022364.0,18.0,57160035.0,282970.47029703,416486.085039135,1022364.0,471.0,57162510.0,554975.825242719,438546.71386744,1024909.0,43.0,0,0,0,0,4072,2092,3.5512771975999997,1.8193735397,0.0,1935.0,798.6428571429,794.0134778314999,630457.40297813,0,0,1,1,0,0,0,1,0.0,801.2442996742999,276.2709359606,1825.9519230769001,0,0,0,0,0,0,203,56083,104,189899,8192,1281,101,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -XSS\n1296,80,6,23/02/2018 01:57:33,58011873,203,104,56330.0,189999.0,680.0,0.0,277.4876847291,292.86878012569997,1935.0,0.0,1826.9134615385,320.4473544929,4246.182501295901,5.2920201353,189581.284313725,363372.27904146,1024732.0,18.0,58009684.0,287176.653465346,414963.541730537,1024732.0,347.0,58011848.0,563221.825242719,430448.60595421796,1027310.0,38.0,0,0,0,0,4072,2092,3.4992836725,1.7927364628,0.0,1935.0,799.7694805195,794.194887907,630745.51997758,0,0,1,1,0,0,0,1,0.0,802.3745928339,277.4876847291,1826.9134615385,0,0,0,0,0,0,203,56330,104,189999,8192,1284,101,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -XSS\n2679202,49991,6,20/02/2018 09:01:06,8,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,250000.0,8.0,0.0,8.0,8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,20,20,125000.0,125000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,1,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,180,254,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n2658,80,17,21/02/2018 10:32:54,119894231,130509,0,4176288.0,0.0,32.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,34833.10219,1088.534443,918.6734223,17996.77876,1050025.0,0.0,120000000.0,918.6734223,17996.77876,1050025.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1044072,0,1088.534443,0.0,32.0,32.0,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,32.00024519,32.0,0.0,0,0,0,0,0,0,130509,4176288,0,0,-1,-1,130508,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-LOIC-UDP\n634313,32044,6,23/02/2018 04:29:19,42,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,47619.0476190476,42.0,0.0,42.0,42.0,42.0,42.0,0.0,42.0,42.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,47619.0476190476,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,256,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n1606,80,6,22/02/2018 04:14:45,12903217,16,11,3208.0,13898.0,598.0,0.0,200.5,238.7517539203,2920.0,0.0,1263.4545454546,1197.3863506517998,1325.7159048011,2.0925014281,496277.57692307705,1223028.58442014,5004723.0,4.0,7898494.0,526566.2666666671,1020932.84161506,3081526.0,296.0,12903213.0,1290321.3,1746539.38575783,5005173.0,40.0,0,0,0,0,332,232,1.2400008462999998,0.8525005818000001,0.0,2920.0,610.9285714286,921.8445248752,849797.328042328,0,0,1,1,0,0,0,1,0.0,633.5555555556,200.5,1263.4545454546,0,0,0,0,0,0,16,3208,11,13898,8192,272,7,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SQL Injection\n293602,53,17,01/03/2018 10:54:39,1123,1,1,45.0,73.0,45.0,45.0,45.0,0.0,73.0,73.0,73.0,0.0,105075.6901,1780.9439,1123.0,0.0,1123.0,1123.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,890.4719501000001,890.4719501000001,45.0,73.0,54.33333333,16.16580754,261.3333333,0,0,0,0,0,0,0,0,1.0,81.5,45.0,73.0,0,0,0,0,0,0,1,45,1,73,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Infilteration\n22737,80,6,20/02/2018 10:18:55,1479252,3,4,20.0,964.0,20.0,0.0,6.666666667,11.54700538,964.0,0.0,241.0,482.0,665.2010611000001,4.732121369,246542.0,603683.131,1478805.0,6.0,404.0,202.0,230.51681069999998,365.0,39.0,1479246.0,493082.0,853661.179,1478805.0,36.0,0,0,0,0,72,92,2.028052015,2.7040693539999996,0.0,964.0,123.0,339.8873763,115523.4286,0,0,1,1,0,0,0,1,1.0,140.5714286,6.666666667,241.0,0,0,0,0,0,0,3,20,4,964,8192,211,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDoS attacks-LOIC-HTTP\n248314,0,0,12/01/1970 09:30:26,-642052000000,163,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0002538735,-3963283950.61729,438678710140.367,968434000000.0,-936885000000.0,-642052000000.0,-3963283950.61729,438678710140.367,968434000000.0,-936885000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,163,0,0,0,-1,-1,0,0,0.0,0.0,0.0,0.0,356032864197.531,234764228535.501,968434000000.0,2054000000.0,Benign\n4448727,54438,6,20/02/2018 03:03:36,0,2,0,31.0,0.0,31.0,0.0,15.5,21.92031021678297,0.0,0.0,0.0,0.0,inf,inf,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,40,0,0.0,0.0,0.0,31.0,20.666666666666664,17.8978583448784,320.33333333333337,0,1,0,0,1,0,0,0,0.0,31.0,15.5,0.0,0,0,0,0,0,0,2,31,0,0,131,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n15945,21,6,16/02/2018 10:20:10,2,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1000000.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,500000.0,500000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-SlowHTTPTest\n246715,0,0,12/01/1970 09:30:03,-110116000000,79,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0007174252999999999,-1411743589.74359,456514436704.295,863748000000.0,-947405000000.0,-110116000000.0,-1411743589.74359,456514436704.295,863748000000.0,-947405000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,79,0,0,0,-1,-1,0,0,0.0,0.0,0.0,0.0,355338609756.09705,254701346240.345,863748000000.0,15069000000.0,Benign\n444,80,6,22/02/2018 10:36:43,56756889,153,104,54995.0,72496.0,646.0,0.0,359.4444444444,271.4171844443,1068.0,0.0,697.0769230769,368.319740061,2246.2647662031,4.5280846877,221706.59765625,415956.238303236,1103271.0,5.0,56755074.0,373388.644736842,485031.87018105295,1103271.0,305.0,56756884.0,551037.708737865,557655.623315575,1164289.0,33.0,0,0,0,0,3072,2092,2.6957080047,1.8323766828999999,0.0,1068.0,494.15116279069997,355.46571143010004,126355.872002534,0,0,1,1,0,0,0,1,0.0,496.0739299611,359.4444444444,697.0769230769,0,0,0,0,0,0,153,54995,104,72496,8192,1228,101,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -Web\n556948,52992,6,23/02/2018 05:01:03,0,2,0,31.0,0.0,31.0,0.0,15.5,21.920310216799997,0.0,0.0,0.0,0.0,inf,inf,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,40,0,0.0,0.0,0.0,31.0,20.6666666667,17.8978583449,320.3333333333,0,1,0,0,1,0,0,0,0.0,31.0,15.5,0.0,0,0,0,0,0,0,2,31,0,0,946,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n61496,21,6,14/02/2018 11:04:15,1,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2000000.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,1000000.0,1000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,FTP-BruteForce\n30178,21,6,16/02/2018 10:27:19,22,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,90909.09091,22.0,0.0,22.0,22.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,45454.54545,45454.54545,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-SlowHTTPTest\n1286,80,6,23/02/2018 01:55:37,57467767,203,104,56330.0,190022.0,680.0,0.0,277.4876847291,292.86878012569997,1935.0,0.0,1827.1346153846,320.4924323264,4286.7856689125,5.3421250907,187803.16013071898,362939.613289066,1017490.0,20.0,57465512.0,284482.732673267,415022.922273968,1017490.0,368.0,57467740.0,557939.223300971,433933.02303845604,1020057.0,39.0,0,0,0,0,4072,2092,3.5324149623000003,1.8097101285,0.0,1935.0,799.8441558442,794.2978841266,630909.12872795,0,0,1,1,0,0,0,1,0.0,802.4495114007001,277.4876847291,1827.1346153846,0,0,0,0,0,0,203,56330,104,190022,8192,1284,101,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -XSS\n1044603,21,6,16/02/2018 10:34:34,3,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,666666.6667,3.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,333333.3333,333333.3333,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-SlowHTTPTest\n642154,80,6,16/02/2018 01:47:26,51962,3,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,57.73449829,25981.0,22924.40185,42191.0,9771.0,51962.0,25981.0,22924.40185,42191.0,9771.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,96,0,57.73449829,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,3,0,0,0,225,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Hulk\n19588,80,6,15/02/2018 09:54:03,5000905,4,4,475.0,972.0,475.0,0.0,118.75,237.5,972.0,0.0,243.0,486.0,289.3476280793,1.5997104524,714415.0,1889783.88041649,5000042.0,6.0,863.0,287.6666666667,252.26441154740002,533.0,29.0,5000899.0,1666966.33333333,2886800.13263862,5000356.0,213.0,0,0,0,0,136,136,0.7998552262,0.7998552262,0.0,972.0,160.7777777778,342.3747135003,117220.444444444,0,0,0,1,0,0,0,0,1.0,180.875,118.75,243.0,0,0,0,0,0,0,4,475,4,972,26883,219,1,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-GoldenEye\n324380,80,6,21/02/2018 02:17:54,2444,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,818.3306056,2444.0,0.0,2444.0,2444.0,2444.0,2444.0,0.0,2444.0,2444.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,818.3306056,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,32738,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-HOIC\n151935,21,6,14/02/2018 11:49:40,2,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1000000.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,500000.0,500000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,FTP-BruteForce\n306,80,17,21/02/2018 10:09:03,119827659,144743,0,4631776.0,0.0,32.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,38653.64673,1207.92646,827.8706872,17655.099280000002,635999.0,0.0,120000000.0,827.8706872,17655.099280000002,635999.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1157944,0,1207.92646,0.0,32.0,32.0,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,32.00022108,32.0,0.0,0,0,0,0,0,0,144743,4631776,0,0,-1,-1,144742,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-LOIC-UDP\n845203,50775,6,23/02/2018 10:20:35,0,3,0,77.0,0.0,46.0,0.0,25.6666666667,23.459184413200003,0.0,0.0,0.0,0.0,inf,inf,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,60,0,0.0,0.0,0.0,46.0,30.75,21.6852484422,470.25,0,1,0,0,1,0,0,0,0.0,41.0,25.6666666667,0.0,0,0,0,0,0,0,3,77,0,0,123,-1,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n18176,80,6,15/02/2018 09:30:41,1888427,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0590825062,1888427.0,0.0,1888427.0,1888427.0,1888427.0,1888427.0,0.0,1888427.0,1888427.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,1.0590825062,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,221,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-GoldenEye\n30369,80,6,15/02/2018 11:17:35,105003339,2,2,16.0,0.0,8.0,8.0,8.0,0.0,0.0,0.0,0.0,0.0,0.1523761068,0.0380940267,35001113.0,60623702.573357895,105003335.0,2.0,105003337.0,105003337.0,0.0,105003337.0,105003337.0,105003337.0,105003337.0,0.0,105003337.0,105003337.0,1,0,0,0,64,64,0.0190470134,0.0190470134,0.0,8.0,4.8,4.38178046,19.2,0,1,0,0,1,0,0,0,1.0,6.0,8.0,0.0,0,0,0,0,0,0,2,16,2,0,211,219,1,32,2.0,0.0,2.0,2.0,105003335.0,0.0,105003335.0,105003335.0,DoS attacks-Slowloris\n2828727,53,17,20/02/2018 05:11:49,261,1,1,47.0,63.0,47.0,47.0,47.0,0.0,63.0,63.0,63.0,0.0,421455.93869731796,7662.835249042146,261.0,0.0,261.0,261.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,3831.417624521073,3831.417624521073,47.0,63.0,52.33333333333334,9.237604307034012,85.33333333333333,0,0,0,0,0,0,0,0,1.0,78.5,47.0,63.0,0,0,0,0,0,0,1,47,1,63,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n336300,8080,6,02/03/2018 12:38:44,10519,3,4,326.0,129.0,326.0,0.0,108.66666670000001,188.2161878,112.0,0.0,32.25,53.7672453,43255.06227,665.4624964,1753.1666670000002,3886.7984979999997,9680.0,19.0,411.0,205.5,204.3538598,350.0,61.0,10188.0,3396.0,5446.83587,9680.0,27.0,0,0,0,0,72,92,285.1982128,380.26428369999996,0.0,326.0,56.875,115.4066568,13318.69643,0,0,1,1,0,0,0,1,1.0,65.0,108.66666670000001,32.25,0,0,0,0,0,0,3,326,4,129,8192,219,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bot\n43196,8080,6,02/03/2018 10:53:22,497,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4024.1448689999997,497.0,0.0,497.0,497.0,497.0,497.0,0.0,497.0,497.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,4024.1448689999997,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2052,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bot\n1641,80,6,22/02/2018 04:20:08,44,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,45454.5454545455,44.0,0.0,44.0,44.0,44.0,44.0,0.0,44.0,44.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,45454.5454545455,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2053,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SQL Injection\n263381,22,6,14/02/2018 02:35:23,7,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,285714.28571428603,7.0,0.0,7.0,7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,32,32,142857.14285714302,142857.14285714302,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,1,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,241,230,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SSH-Bruteforce\n653787,80,6,16/02/2018 01:47:24,3871747,4,5,346.0,935.0,346.0,0.0,86.5,173.0,935.0,0.0,187.0,418.14471180000004,330.8583954,2.324532052,483968.375,1338533.625,3796420.0,5.0,3833637.0,1277879.0,2181192.926,3796429.0,1811.0,3871738.0,967934.5,1885744.264,3796426.0,48.0,0,0,0,0,136,168,1.033125357,1.291406696,0.0,935.0,128.1,303.65274389999996,92204.98889,0,0,0,0,1,1,0,0,1.0,142.33333330000002,86.5,187.0,0,0,0,0,0,0,4,346,5,935,225,237,1,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Hulk\n45975,80,6,15/02/2018 11:25:41,107262407,15,3,2530.0,0.0,230.0,0.0,168.6666666667,105.2796728899,0.0,0.0,0.0,0.0,23.5870149735,0.1678127548,6309553.35294118,13459956.5197732,53247692.0,3.0,107262404.0,7661600.28571429,14554160.606080499,53247692.0,242.0,106239125.0,53119562.5,31284978.575483195,75241383.0,30997742.0,0,0,0,0,496,100,0.1398439623,0.0279687925,0.0,230.0,133.15789473680002,116.6691729054,13611.6959064327,0,0,0,1,0,0,0,0,0.0,140.5555555556,168.6666666667,0.0,0,0,0,0,0,0,15,2530,3,0,26883,0,11,32,6026776.0,2336700.82646838,7679073.0,4374479.0,23802213.0,20610261.677262,53247692.0,6655960.0,DoS attacks-Slowloris\n1283,80,6,23/02/2018 01:54:41,56646217,203,104,56083.0,189905.0,680.0,0.0,276.2709359606,291.6989844299,1934.0,0.0,1826.0096153846,320.3831291145,4342.5318234402,5.419602866,185118.35620915,362776.97505676706,1020289.0,18.0,56643984.0,280415.762376238,415751.72384567396,1020289.0,262.0,56646193.0,549963.038834951,440258.208973827,1022756.0,42.0,0,0,0,0,4072,2092,3.5836461948,1.8359566712,0.0,1934.0,798.6623376622999,794.0360244272999,630493.208088329,0,0,1,1,0,0,0,1,0.0,801.2638436482001,276.2709359606,1826.0096153846,0,0,0,0,0,0,203,56083,104,189905,8192,1281,101,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -XSS\n239078,80,6,16/02/2018 01:45:58,79881,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,25.0372429,79881.0,0.0,79881.0,79881.0,79881.0,79881.0,0.0,79881.0,79881.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,25.0372429,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,225,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Hulk\n411,500,17,23/02/2018 10:13:50,89479973,6,0,3000.0,0.0,500.0,500.0,500.0,0.0,0.0,0.0,0.0,0.0,33.5270552663,0.0670541105,17895994.6,15344481.2721026,41990587.0,4000458.0,89479973.0,17895994.6,15344481.2721026,41990587.0,4000458.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,48,0,0.0670541105,0.0,500.0,500.0,500.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,583.3333333333001,500.0,0.0,0,0,0,0,0,0,6,3000,0,0,-1,-1,5,8,4000458.0,0.0,4000458.0,4000458.0,21369878.75,15280215.970192801,41990587.0,7208173.0,Brute Force -Web\n1008630,21,6,16/02/2018 10:16:30,4,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,500000.0,4.0,0.0,4.0,4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,250000.0,250000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-SlowHTTPTest\n246439,0,0,12/01/1970 09:18:52,-553312000000,34,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-6.144815221791679e-05,-16767030303.0303,344555253608.681,562234000000.0,-839505000000.0,-553312000000.0,-16767030303.0303,344555253608.681,562234000000.0,-839505000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,34,0,0,0,-1,-1,0,0,0.0,0.0,0.0,0.0,252067941176.471,161378652480.924,562234000000.0,4375000000.0,Benign\n1046945,21,6,16/02/2018 10:35:44,10,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,200000.0,10.0,0.0,10.0,10.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,100000.0,100000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-SlowHTTPTest\n852569,80,6,16/02/2018 01:48:12,3081,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,649.1398896000001,3081.0,0.0,3081.0,3081.0,3081.0,3081.0,0.0,3081.0,3081.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,649.1398896000001,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,225,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Hulk\n311153,22,6,14/02/2018 03:12:00,298569,22,22,1912.0,2665.0,640.0,0.0,86.9090909091,137.68802178110002,976.0,0.0,121.13636363639999,258.64156029139997,15329.789763840201,147.3696197529,6943.465116279101,14962.9237026357,59168.0,5.0,298517.0,14215.0952380952,19236.1455752049,59168.0,435.0,298544.0,14216.380952381001,31185.5177758462,105325.0,13.0,0,0,0,0,712,712,73.68480987640001,73.68480987640001,0.0,976.0,101.71111111110001,203.73719951119998,41508.8464646465,0,0,0,1,0,0,0,0,1.0,104.02272727270001,86.9090909091,121.13636363639999,0,0,0,0,0,0,22,1912,22,2665,26883,230,16,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SSH-Bruteforce\n44128,21,6,16/02/2018 10:34:19,3,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,666666.6667,3.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,333333.3333,333333.3333,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-SlowHTTPTest\n45606,21,6,14/02/2018 10:56:17,2,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1000000.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,500000.0,500000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,FTP-BruteForce\n284682,22,6,14/02/2018 02:45:39,9,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,222222.222222222,9.0,0.0,9.0,9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,32,32,111111.111111111,111111.111111111,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,1,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,241,230,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SSH-Bruteforce\n1440,80,17,21/02/2018 10:20:11,119928535,122891,0,3932512.0,0.0,32.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,32790.46142,1024.7019189999999,975.9014973,19605.95117,713740.0,1.0,120000000.0,975.9014973,19605.95117,713740.0,1.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,983128,0,1024.7019189999999,0.0,32.0,32.0,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,32.00026039,32.0,0.0,0,0,0,0,0,0,122891,3932512,0,0,-1,-1,122890,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-LOIC-UDP\n410956,0,0,05/01/1970 03:01:17,-11873000000,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-0.0001684494,-11873000000.0,0.0,-11873000000.0,-11873000000.0,-11873000000.0,-11873000000.0,0.0,-11873000000.0,-11873000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,-1,-1,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n136073,80,6,20/02/2018 10:43:30,1328937,3,4,20.0,964.0,20.0,0.0,6.666666667,11.54700538,964.0,0.0,241.0,482.0,740.4414205,5.2673678289999994,221489.5,542343.7428,1328544.0,3.0,360.0,180.0,212.1320344,330.0,30.0,1328934.0,442978.0,766922.6707,1328544.0,29.0,0,0,0,0,72,92,2.257443355,3.0099244730000003,0.0,964.0,123.0,339.8873763,115523.4286,0,0,1,1,0,0,0,1,1.0,140.5714286,6.666666667,241.0,0,0,0,0,0,0,3,20,4,964,8192,211,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDoS attacks-LOIC-HTTP\n901545,53,17,14/02/2018 05:08:11,951,1,1,44.0,125.0,44.0,44.0,44.0,0.0,125.0,125.0,125.0,0.0,177707.67613038898,2103.0494216614,951.0,0.0,951.0,951.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,1051.5247108307,1051.5247108307,44.0,125.0,71.0,46.7653718044,2187.0,0,0,0,0,0,0,0,0,1.0,106.5,44.0,125.0,0,0,0,0,0,0,1,44,1,125,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n6622,21,6,14/02/2018 10:36:42,1,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2000000.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,1000000.0,1000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,FTP-BruteForce\n1621,80,6,22/02/2018 04:17:00,61,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,32786.8852459016,61.0,0.0,61.0,61.0,61.0,61.0,0.0,61.0,61.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,32786.8852459016,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2051,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SQL Injection\n300497,22,6,14/02/2018 03:06:51,379820,22,22,1944.0,2665.0,640.0,0.0,88.36363636360001,137.8690011274,976.0,0.0,121.13636363639999,258.64156029139997,12134.695382023101,115.8443473224,8833.023255814,19425.154404593603,79620.0,3.0,379339.0,18063.7619047619,26114.5684875411,79620.0,232.0,379817.0,18086.5238095238,33814.7822447802,108389.0,10.0,0,0,0,0,712,712,57.9221736612,57.9221736612,0.0,976.0,102.42222222219999,203.7441007388,41511.6585858586,0,0,0,1,0,0,0,0,1.0,104.75,88.36363636360001,121.13636363639999,0,0,0,0,0,0,22,1944,22,2665,26883,230,16,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SSH-Bruteforce\n333027,80,6,20/02/2018 11:07:49,1139966,3,4,20.0,964.0,20.0,0.0,6.666666667,11.54700538,964.0,0.0,241.0,482.0,863.1836388,6.140534016,189994.3333,465217.2291,1139615.0,5.0,307.0,153.5,194.4543648,291.0,16.0,1139961.0,379987.0,657857.1603,1139615.0,33.0,0,0,0,0,72,92,2.631657435,3.5088765810000004,0.0,964.0,123.0,339.8873763,115523.4286,0,0,1,1,0,0,0,1,1.0,140.5714286,6.666666667,241.0,0,0,0,0,0,0,3,20,4,964,8192,211,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDoS attacks-LOIC-HTTP\n39331,80,6,15/02/2018 11:14:22,108062773,15,3,2530.0,0.0,230.0,0.0,168.6666666667,105.2796728899,0.0,0.0,0.0,0.0,23.412317949699997,0.166569851,6356633.70588235,13509372.9065988,53247750.0,3.0,108062770.0,7718769.28571429,14604684.8192841,53247750.0,424.0,107039579.0,53519789.5,31851908.508367304,76042490.0,30997089.0,0,0,0,0,496,100,0.1388082092,0.0277616418,0.0,230.0,133.15789473680002,116.6691729054,13611.6959064327,0,0,0,1,0,0,0,0,0.0,140.5555555556,168.6666666667,0.0,0,0,0,0,0,0,15,2530,3,0,26883,0,11,32,6154521.5,2201687.97898896,7711350.0,4597693.0,23938431.75,20635447.0914335,53247750.0,6655720.0,DoS attacks-Slowloris\n290795,80,6,21/02/2018 02:17:12,14615,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,136.8457065,14615.0,0.0,14615.0,14615.0,14615.0,14615.0,0.0,14615.0,14615.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,136.8457065,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,32738,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-HOIC\n32386,80,6,15/02/2018 11:39:19,116125106,2,2,16.0,0.0,8.0,8.0,8.0,0.0,0.0,0.0,0.0,0.0,0.1377824361,0.034445609,38708368.66666671,67044857.7446722,116125102.0,2.0,116125104.0,116125104.0,0.0,116125104.0,116125104.0,116125104.0,116125104.0,0.0,116125104.0,116125104.0,1,0,0,0,64,64,0.0172228045,0.0172228045,0.0,8.0,4.8,4.38178046,19.2,0,1,0,0,1,0,0,0,1.0,6.0,8.0,0.0,0,0,0,0,0,0,2,16,2,0,211,219,1,32,2.0,0.0,2.0,2.0,116125102.0,0.0,116125102.0,116125102.0,DoS attacks-Slowloris\n248163,8080,6,02/03/2018 11:16:04,578,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3460.2076119999997,578.0,0.0,578.0,578.0,578.0,578.0,0.0,578.0,578.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,3460.2076119999997,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2052,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Bot\n496170,53,17,28/02/2018 02:10:24,281,1,1,45.0,61.0,45.0,45.0,45.0,0.0,61.0,61.0,61.0,0.0,377224.1993,7117.437722,281.0,0.0,281.0,281.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,3558.718861,3558.718861,45.0,61.0,50.33333333,9.237604307,85.33333333,0,0,0,0,0,0,0,0,1.0,75.5,45.0,61.0,0,0,0,0,0,0,1,45,1,61,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Infilteration\n246436,0,0,11/01/1970 03:51:32,-828220000000,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-2.41481731906981e-06,-828220000000.0,0.0,-828220000000.0,-828220000000.0,-828220000000.0,-828220000000.0,0.0,-828220000000.0,-828220000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,-1,-1,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n7139371,53,17,20/02/2018 11:06:57,88423,2,2,90.0,122.0,45.0,45.0,45.0,0.0,61.0,61.0,61.0,0.0,2397.5662440767674,45.237098944844675,29474.333333333336,50549.619151219464,87844.0,267.0,88111.0,88111.0,0.0,88111.0,88111.0,88156.0,88156.0,0.0,88156.0,88156.0,0,0,0,0,16,16,22.618549472422327,22.618549472422327,45.0,61.0,51.4,8.763560920082657,76.8,0,0,0,0,0,0,0,0,1.0,64.25,45.0,61.0,0,0,0,0,0,0,2,90,2,122,-1,-1,1,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n1635,80,6,22/02/2018 04:18:52,5017356,5,3,566.0,1866.0,566.0,0.0,113.2,253.12289505299998,1866.0,0.0,622.0,1077.3356023078,484.7174487918,1.5944652921,716765.142857143,1890419.43453928,5003825.0,19.0,5017356.0,1254339.0,2499661.05883444,5003825.0,451.0,13120.0,6560.0,6185.7701218199,10934.0,2186.0,0,0,0,0,112,72,0.9965408075,0.5979244845,0.0,1866.0,270.2222222222,627.0099237209,393141.444444444,0,0,1,1,0,0,0,1,0.0,304.0,113.2,622.0,0,0,0,0,0,0,5,566,3,1866,8192,219,1,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,SQL Injection\n175497,40262,6,16/02/2018 01:45:39,5436346,5,3,935.0,329.0,935.0,0.0,187.0,418.14471180000004,329.0,0.0,109.66666670000001,189.9482386,232.50911549999998,1.47157668,776620.8571,2023831.9619999998,5366106.0,8.0,5436346.0,1359086.5,2671403.0919999997,5366114.0,26.0,33691.0,16845.5,18976.62469,30264.0,3427.0,0,0,0,0,168,104,0.919735425,0.551841255,0.0,935.0,140.4444444,317.2034486,100618.0278,0,0,0,0,1,0,0,0,0.0,158.0,187.0,109.66666670000001,0,0,0,0,0,0,5,935,3,329,219,211,1,32,0.0,0.0,0.0,0.0,5366106.0,0.0,5366106.0,5366106.0,Benign\n481741,80,6,20/02/2018 11:15:19,8747599,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.22863416600000003,8747599.0,0.0,8747599.0,8747599.0,8747599.0,8747599.0,0.0,8747599.0,8747599.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,0.22863416600000003,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2049,-1,0,20,0.0,0.0,0.0,0.0,8747599.0,0.0,8747599.0,8747599.0,DDoS attacks-LOIC-HTTP\n1151,80,6,22/02/2018 01:54:24,50973109,198,109,54432.0,194309.0,680.0,0.0,274.9090909091,285.46384343169996,2920.0,0.0,1782.6513761468,520.1538835011,4879.8475290177,6.0227835034,166578.787581699,346192.498466536,1014652.0,3.0,50970921.0,258735.63959390897,403160.45043370203,1014652.0,55.0,50973086.0,471973.018518519,445852.835127672,1017335.0,3.0,0,0,0,0,3972,2192,3.8844010869,2.1383824165000003,0.0,2920.0,807.6006493506,818.6339695834,670161.576155928,0,0,1,1,0,0,0,1,0.0,810.2312703583,274.9090909091,1782.6513761468,0,0,0,0,0,0,198,54432,109,194309,8192,1264,101,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -XSS\n608786,1863,6,28/02/2018 02:23:17,2,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1000000.0,2.0,0.0,2.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,24,20,500000.0,500000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,1024,0,0,24,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Infilteration\n36774,80,6,15/02/2018 09:50:19,6930247,5,3,466.0,972.0,466.0,0.0,93.2,208.401535503,972.0,0.0,324.0,561.1844616523,207.49621189549998,1.1543600105,990035.2857142859,2598091.4937557997,6881817.0,7.0,6930247.0,1732561.75,3433119.76564479,6882133.0,285.0,6882393.0,3441196.5,4865772.1740789,6881817.0,576.0,0,0,0,0,168,104,0.7214750066,0.43288500399999996,0.0,972.0,159.7777777778,341.3538405298,116522.444444444,0,0,0,1,0,0,0,0,0.0,179.75,93.2,324.0,0,0,0,0,0,0,5,466,3,972,26883,219,1,32,583.0,0.0,583.0,583.0,6881817.0,0.0,6881817.0,6881817.0,DoS attacks-GoldenEye\n137993,21,6,14/02/2018 11:42:40,1,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2000000.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,1000000.0,1000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,FTP-BruteForce\n21758,21,6,14/02/2018 10:44:19,18,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,111111.111111111,18.0,0.0,18.0,18.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,55555.5555555556,55555.5555555556,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,FTP-BruteForce\n2752,80,17,21/02/2018 10:33:28,119947712,139061,0,4449952.0,0.0,32.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,37099.09865,1159.346833,862.5608514,17523.10212,683492.0,0.0,120000000.0,862.5608514,17523.10212,683492.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1112488,0,1159.346833,0.0,32.0,32.0,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,32.00023011,32.0,0.0,0,0,0,0,0,0,139061,4449952,0,0,-1,-1,139060,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-LOIC-UDP\n2113,80,17,21/02/2018 10:27:10,119735216,138243,0,4423776.0,0.0,32.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,36946.32329,1154.5726029999998,866.1276313,17743.92191,1110331.0,0.0,120000000.0,866.1276313,17743.92191,1110331.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1105944,0,1154.5726029999998,0.0,32.0,32.0,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,32.00023148,32.0,0.0,0,0,0,0,0,0,138243,4423776,0,0,-1,-1,138242,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-LOIC-UDP\n410958,0,0,12/01/1970 07:17:56,-919011000000,81,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,-8.81382268547384e-05,-11487637500.0,383166426234.274,754847000000.0,-880263000000.0,-919011000000.0,-11487637500.0,383166426234.274,754847000000.0,-880263000000.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,81,0,0,0,-1,-1,0,0,0.0,0.0,0.0,0.0,284112452380.95197,193152417061.482,754847000000.0,12603000000.0,Benign\n48055,80,6,15/02/2018 11:17:10,3027716,3,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.990845905,1513858.0,710045.516969159,2015936.0,1011780.0,3027716.0,1513858.0,710045.516969159,2015936.0,1011780.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,120,0,0.990845905,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,3,0,0,0,26883,-1,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Slowloris\n573,500,17,23/02/2018 10:40:01,89479669,6,0,3000.0,0.0,500.0,500.0,500.0,0.0,0.0,0.0,0.0,0.0,33.5271691718,0.0670543383,17895933.8,15345212.9109094,41990474.0,4000245.0,89479669.0,17895933.8,15345212.9109094,41990474.0,4000245.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,48,0,0.0670543383,0.0,500.0,500.0,500.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,583.3333333333001,500.0,0.0,0,0,0,0,0,0,6,3000,0,0,-1,-1,5,8,4000245.0,0.0,4000245.0,4000245.0,21369856.0,15281137.916742401,41990474.0,7200363.0,Brute Force -Web\n611805,80,6,21/02/2018 02:23:53,4883,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,409.584272,4883.0,0.0,4883.0,4883.0,4883.0,4883.0,0.0,4883.0,4883.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,409.584272,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,32738,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-HOIC\n431354,80,6,20/02/2018 10:56:14,9943598,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.201134438,9943598.0,0.0,9943598.0,9943598.0,9943598.0,9943598.0,0.0,9943598.0,9943598.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,0.201134438,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2049,-1,0,20,0.0,0.0,0.0,0.0,9943598.0,0.0,9943598.0,9943598.0,DDoS attacks-LOIC-HTTP\n600340,80,6,16/02/2018 01:47:17,2235,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,894.8545861,2235.0,0.0,2235.0,2235.0,2235.0,2235.0,0.0,2235.0,2235.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,894.8545861,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,225,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Hulk\n2441431,53,17,20/02/2018 04:51:23,534,1,1,38.0,54.0,38.0,38.0,38.0,0.0,54.0,54.0,54.0,0.0,172284.64419475658,3745.3183520599255,534.0,0.0,534.0,534.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,1872.6591760299627,1872.6591760299627,38.0,54.0,43.33333333333334,9.237604307034012,85.33333333333333,0,0,0,0,0,0,0,0,1.0,65.0,38.0,54.0,0,0,0,0,0,0,1,38,1,54,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n6011191,58453,6,20/02/2018 11:57:34,36,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,55555.555555555555,36.0,0.0,36.0,36.0,36.0,36.0,0.0,36.0,36.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,55555.555555555555,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,260,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n1128,80,6,23/02/2018 01:23:00,143,3,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,20979.020979021,71.5,94.04520189780001,138.0,5.0,143.0,71.5,94.04520189780001,138.0,5.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,60,0,20979.020979021,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,3,0,0,0,2053,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -XSS\n961594,80,6,16/02/2018 01:48:35,2707,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,738.8252677999999,2707.0,0.0,2707.0,2707.0,2707.0,2707.0,0.0,2707.0,2707.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,64,0,738.8252677999999,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,225,-1,0,32,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-Hulk\n3321683,52314,6,20/02/2018 01:26:21,0,2,0,31.0,0.0,31.0,0.0,15.5,21.92031021678297,0.0,0.0,0.0,0.0,inf,inf,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0,0,0,40,0,0.0,0.0,0.0,31.0,20.666666666666664,17.8978583448784,320.33333333333337,0,1,0,0,1,0,0,0,0.0,31.0,15.5,0.0,0,0,0,0,0,0,2,31,0,0,946,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n499974,3389,6,28/02/2018 11:49:19,1390722,8,7,1148.0,1581.0,677.0,0.0,143.5,228.12966239999997,1173.0,0.0,225.8571429,430.0986044,1962.290091,10.78576452,99337.28571,252057.4802,968806.0,39.0,1390722.0,198674.5714,382963.2613,1065400.0,39.0,1327854.0,221309.0,366410.1154,968806.0,63361.0,0,0,0,0,168,148,5.752407742000001,5.033356774,0.0,1173.0,170.5625,319.43324160000003,102037.5958,0,0,0,1,0,0,0,0,0.0,181.93333330000002,143.5,225.8571429,0,0,0,0,0,0,8,1148,7,1581,8192,62852,5,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Infilteration\n2156,80,17,21/02/2018 10:27:29,119939417,124250,0,3976000.0,0.0,32.0,32.0,32.0,0.0,0.0,0.0,0.0,0.0,33150.06942,1035.9396689999999,965.3149482000001,18194.28944,701815.0,0.0,120000000.0,965.3149482000001,18194.28944,701815.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,994000,0,1035.9396689999999,0.0,32.0,32.0,32.0,0.0,0.0,0,0,0,0,0,0,0,0,0.0,32.00025755,32.0,0.0,0,0,0,0,0,0,124250,3976000,0,0,-1,-1,124249,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DDOS attack-LOIC-UDP\n2914473,123,17,20/02/2018 04:39:46,18434,1,1,48.0,48.0,48.0,48.0,48.0,0.0,48.0,48.0,48.0,0.0,5207.768254312683,108.49517196484757,18434.0,0.0,18434.0,18434.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,8,8,54.247585982423786,54.247585982423786,48.0,48.0,48.0,0.0,0.0,0,0,0,0,0,0,0,0,1.0,72.0,48.0,48.0,0,0,0,0,0,0,1,48,1,48,-1,-1,0,8,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Benign\n1193,80,6,23/02/2018 01:36:29,133,2,0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,15037.5939849624,133.0,0.0,133.0,133.0,133.0,133.0,0.0,133.0,133.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,0,15037.5939849624,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,1,0,0,0,0.0,0.0,0.0,0.0,0,0,0,0,0,0,2,0,0,0,2053,-1,0,20,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,Brute Force -XSS\n36447,21,6,16/02/2018 10:30:28,3,1,1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,666666.6667,3.0,0.0,3.0,3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0,0,0,40,20,333333.3333,333333.3333,0.0,0.0,0.0,0.0,0.0,0,0,0,1,0,0,0,0,1.0,0.0,0.0,0.0,0,0,0,0,0,0,1,0,1,0,26883,0,0,40,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,DoS attacks-SlowHTTPTest\n" }, { "path": "upload.py", "content": "" } ]