[
  {
    "path": ".gitignore",
    "content": "# IDE\n.idea/\n.vscode/\n\n# Python cache/bytecode\n__pycache__/\n*.py[cod]\n*$py.class\n\n# Python packaging/build\nbuild/\ndist/\n*.egg-info/\n.eggs/\npip-wheel-metadata/\n\n# Virtual environments\n.venv/\nvenv/\nenv/\nENV/\n\n# Test and type-check caches\n.pytest_cache/\n.mypy_cache/\n.ruff_cache/\n.tox/\n.nox/\n.coverage\n.coverage.*\nhtmlcov/\n\n# Notebook checkpoints\n.ipynb_checkpoints/\n\n# OS files\n.DS_Store\nThumbs.db\n"
  },
  {
    "path": "LICENSE",
    "content": "                    GNU AFFERO GENERAL PUBLIC LICENSE\n                       Version 3, 19 November 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The GNU Affero General Public License is a free, copyleft license for\nsoftware and other kinds of works, specifically designed to ensure\ncooperation with the community in the case of network server software.\n\n  The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works.  By contrast,\nour General Public Licenses are intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.\n\n  When we speak of free software, we are referring to freedom, not\nprice.  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If your rights have been terminated and not permanently\nreinstated, you do not qualify to receive new licenses for the same\nmaterial under section 10.\n\n  9. Acceptance Not Required for Having Copies.\n\n  You are not required to accept this License in order to receive or\nrun a copy of the Program.  Ancillary propagation of a covered work\noccurring solely as a consequence of using peer-to-peer transmission\nto receive a copy likewise does not require acceptance.  However,\nnothing other than this License grants you permission to propagate or\nmodify any covered work.  These actions infringe copyright if you do\nnot accept this License.  Therefore, by modifying or propagating a\ncovered work, you indicate your acceptance of this License to do so.\n\n  10. Automatic Licensing of Downstream Recipients.\n\n  Each time you convey a covered work, the recipient automatically\nreceives a license from the original licensors, to run, modify and\npropagate that work, subject to this License.  You are not responsible\nfor enforcing compliance by third parties with this License.\n\n  An \"entity transaction\" is a transaction transferring control of an\norganization, or substantially all assets of one, or subdividing an\norganization, or merging organizations.  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Patents.\n\n  A \"contributor\" is a copyright holder who authorizes use under this\nLicense of the Program or a work on which the Program is based.  The\nwork thus licensed is called the contributor's \"contributor version\".\n\n  A contributor's \"essential patent claims\" are all patent claims\nowned or controlled by the contributor, whether already acquired or\nhereafter acquired, that would be infringed by some manner, permitted\nby this License, of making, using, or selling its contributor version,\nbut do not include claims that would be infringed only as a\nconsequence of further modification of the contributor version.  For\npurposes of this definition, \"control\" includes the right to grant\npatent sublicenses in a manner consistent with the requirements of\nthis License.\n\n  Each contributor grants you a non-exclusive, worldwide, royalty-free\npatent license under the contributor's essential patent claims, to\nmake, use, sell, offer for sale, import and otherwise run, modify and\npropagate the contents of its contributor version.\n\n  In the following three paragraphs, a \"patent license\" is any express\nagreement or commitment, however denominated, not to enforce a patent\n(such as an express permission to practice a patent or covenant not to\nsue for patent infringement).  To \"grant\" such a patent license to a\nparty means to make such an agreement or commitment not to enforce a\npatent against the party.\n\n  If you convey a covered work, knowingly relying on a patent license,\nand the Corresponding Source of the work is not available for anyone\nto copy, free of charge and under the terms of this License, through a\npublicly available network server or other readily accessible means,\nthen you must either (1) cause the Corresponding Source to be so\navailable, or (2) arrange to deprive yourself of the benefit of the\npatent license for this particular work, or (3) arrange, in a manner\nconsistent with the requirements of this License, to extend the patent\nlicense to downstream recipients.  \"Knowingly relying\" means you have\nactual knowledge that, but for the patent license, your conveying the\ncovered work in a country, or your recipient's use of the covered work\nin a country, would infringe one or more identifiable patents in that\ncountry that you have reason to believe are valid.\n\n  If, pursuant to or in connection with a single transaction or\narrangement, you convey, or propagate by procuring conveyance of, a\ncovered work, and grant a patent license to some of the parties\nreceiving the covered work authorizing them to use, propagate, modify\nor convey a specific copy of the covered work, then the patent license\nyou grant is automatically extended to all recipients of the covered\nwork and works based on it.\n\n  A patent license is \"discriminatory\" if it does not include within\nthe scope of its coverage, prohibits the exercise of, or is\nconditioned on the non-exercise of one or more of the rights that are\nspecifically granted under this License.  You may not convey a covered\nwork if you are a party to an arrangement with a third party that is\nin the business of distributing software, under which you make payment\nto the third party based on the extent of your activity of conveying\nthe work, and under which the third party grants, to any of the\nparties who would receive the covered work from you, a discriminatory\npatent license (a) in connection with copies of the covered work\nconveyed by you (or copies made from those copies), or (b) primarily\nfor and in connection with specific products or compilations that\ncontain the covered work, unless you entered into that arrangement,\nor that patent license was granted, prior to 28 March 2007.\n\n  Nothing in this License shall be construed as excluding or limiting\nany implied license or other defenses to infringement that may\notherwise be available to you under applicable patent law.\n\n  12. No Surrender of Others' Freedom.\n\n  If conditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License.  If you cannot convey a\ncovered work so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you may\nnot convey it at all.  For example, if you agree to terms that obligate you\nto collect a royalty for further conveying from those to whom you convey\nthe Program, the only way you could satisfy both those terms and this\nLicense would be to refrain entirely from conveying the Program.\n\n  13. Remote Network Interaction; Use with the GNU General Public License.\n\n  Notwithstanding any other provision of this License, if you modify the\nProgram, your modified version must prominently offer all users\ninteracting with it remotely through a computer network (if your version\nsupports such interaction) an opportunity to receive the Corresponding\nSource of your version by providing access to the Corresponding Source\nfrom a network server at no charge, through some standard or customary\nmeans of facilitating copying of software.  This Corresponding Source\nshall include the Corresponding Source for any work covered by version 3\nof the GNU General Public License that is incorporated pursuant to the\nfollowing paragraph.\n\n  Notwithstanding any other provision of this License, you have\npermission to link or combine any covered work with a work licensed\nunder version 3 of the GNU General Public License into a single\ncombined work, and to convey the resulting work.  The terms of this\nLicense will continue to apply to the part which is the covered work,\nbut the work with which it is combined will remain governed by version\n3 of the GNU General Public License.\n\n  14. Revised Versions of this License.\n\n  The Free Software Foundation may publish revised and/or new versions of\nthe GNU Affero General Public License from time to time.  Such new versions\nwill be similar in spirit to the present version, but may differ in detail to\naddress new problems or concerns.\n\n  Each version is given a distinguishing version number.  If the\nProgram specifies that a certain numbered version of the GNU Affero General\nPublic License \"or any later version\" applies to it, you have the\noption of following the terms and conditions either of that numbered\nversion or of any later version published by the Free Software\nFoundation.  If the Program does not specify a version number of the\nGNU Affero General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n  If the Program specifies that a proxy can decide which future\nversions of the GNU Affero General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n  Later license versions may give you additional or different\npermissions.  However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n  15. Disclaimer of Warranty.\n\n  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY\nAPPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT\nHOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY\nOF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,\nTHE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\nPURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM\nIS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF\nALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n  16. Limitation of Liability.\n\n  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING\nWILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS\nTHE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY\nGENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE\nUSE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF\nDATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD\nPARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),\nEVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF\nSUCH DAMAGES.\n\n  17. Interpretation of Sections 15 and 16.\n\n  If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n    <one line to give the program's name and a brief idea of what it does.>\n    Copyright (C) <year>  <name of author>\n\n    This program is free software: you can redistribute it and/or modify\n    it under the terms of the GNU Affero General Public License as published\n    by the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n\n    This program is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU Affero General Public License for more details.\n\n    You should have received a copy of the GNU Affero General Public License\n    along with this program.  If not, see <http://www.gnu.org/licenses/>.\n\nAlso add information on how to contact you by electronic and paper mail.\n\n  If your software can interact with users remotely through a computer\nnetwork, you should also make sure that it provides a way for users to\nget its source.  For example, if your program is a web application, its\ninterface could display a \"Source\" link that leads users to an archive\nof the code.  There are many ways you could offer source, and different\nsolutions will be better for different programs; see section 13 for the\nspecific requirements.\n\n  You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU AGPL, see\n<http://www.gnu.org/licenses/>.\n"
  },
  {
    "path": "README.rst",
    "content": "Anomaly Detection Learning Resources\n====================================\n\n.. image:: https://img.shields.io/github/stars/yzhao062/anomaly-detection-resources.svg\n   :target: https://github.com/yzhao062/anomaly-detection-resources/stargazers\n   :alt: GitHub stars\n\n\n.. image:: https://img.shields.io/github/forks/yzhao062/anomaly-detection-resources.svg?color=blue\n   :target: https://github.com/yzhao062/anomaly-detection-resources/network\n   :alt: GitHub forks\n\n\n.. image:: https://img.shields.io/github/license/yzhao062/anomaly-detection-resources.svg?color=blue\n   :target: https://github.com/yzhao062/anomaly-detection-resources/blob/master/LICENSE\n   :alt: License\n\n\n.. image:: https://awesome.re/badge-flat2.svg\n   :target: https://awesome.re/badge-flat2.svg\n   :alt: Awesome\n\n\n.. image:: https://img.shields.io/badge/ADBench-benchmark_results-pink\n   :target: https://github.com/Minqi824/ADBench\n   :alt: Benchmark\n\n\n----\n\n`Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_\n(also known as *Anomaly Detection*) is an exciting yet challenging field,\nwhich aims to identify outlying objects that are deviant from the general data distribution.\nOutlier detection has been proven critical in many fields, such as credit card\nfraud analytics, network intrusion detection, and mechanical unit defect detection.\n\n**This repository collects**:\n\n\n#. Books & Academic Papers \n#. Online Courses and Videos\n#. Outlier Datasets\n#. Open-source and Commercial Libraries/Toolkits\n#. Key Conferences & Journals\n\n\n**More items will be added to the repository**.\nPlease feel free to suggest other key resources by opening an issue report,\nsubmitting a pull request, or dropping me an email @ (yzhao010@usc.edu).\nEnjoy reading!\n\nBTW, you may find my `[GitHub] <https://github.com/yzhao062>`_, `[USC FORTIS Lab] <https://github.com/USC-FORTIS>`_, and\n`[Google Scholar] <https://scholar.google.com/citations?user=zoGDYsoAAAAJ&hl=en>`_ relevant,\nespecially `PyOD library <https://github.com/yzhao062/pyod>`_, `ADBench benchmark <https://github.com/Minqi824/ADBench>`_, and `NLP-ADBench: NLP Anomaly Detection Benchmark  <https://github.com/USC-FORTIS/NLP-ADBench>`_,.\n\n----\n\nTable of Contents\n-----------------\n\n\n* `1. Books & Tutorials & Benchmarks <#1-books--tutorials--benchmarks>`_\n\n  * `1.1. Benchmarks <#13-benchmarks>`_\n  * `1.2. Tutorials <#12-tutorials>`_\n  * `1.3. Books <#11-books>`_\n\n* `2. Courses/Seminars/Videos <#2-coursesseminarsvideos>`_\n* `3. Toolbox & Datasets <#3-toolbox--datasets>`_\n\n  * `3.1. Multivariate data outlier detection <#31-multivariate-data>`_\n  * `3.2. Time series outlier detection <#32-time-series-outlier-detection>`_\n  * `3.3. Graph Outlier Detection <#33-graph-outlier-detection>`_\n  * `3.4. Real-time Elasticsearch <#34-real-time-elasticsearch>`_\n  * `3.5. Datasets <#35-datasets>`_\n\n* `4. Papers <#4-papers>`_\n\n  * `4.1. LLM and LLM Agents for Anomaly Detection <#41-llm-and-llm-agents-for-anomaly-detection>`_\n  * `4.2. Emerging and Interesting Topics <#42-emerging-and-interesting-topics>`_\n  * `4.3. Weakly-supervised Methods <#43-weakly-supervised-methods>`_\n  * `4.4. Machine Learning Systems for Outlier Detection <#44-machine-learning-systems-for-outlier-detection>`_\n  * `4.5. Automated Outlier Detection <#45-automated-outlier-detection>`_\n  * `4.6. Outlier Detection with Neural Networks <#46-outlier-detection-with-neural-networks>`_\n  * `4.7. Interpretability <#47-interpretability>`_\n  * `4.8. Representation Learning in Outlier Detection <#48-representation-learning-in-outlier-detection>`_\n  * `4.9. Outlier Detection in Evolving Data <#49-outlier-detection-in-evolving-data>`_\n  * `4.10. Outlier Ensembles <#410-outlier-ensembles>`_\n  * `4.11. High-dimensional & Subspace Outliers <#411-high-dimensional--subspace-outliers>`_\n  * `4.12. Feature Selection in Outlier Detection <#412-feature-selection-in-outlier-detection>`_\n  * `4.13. Time Series Outlier Detection <#413-time-series-outlier-detection>`_\n  * `4.14. Graph & Network Outlier Detection <#414-graph--network-outlier-detection>`_\n  * `4.15. Key Algorithms <#415-key-algorithms>`_\n  * `4.16. Overview & Survey Papers <#416-overview--survey-papers>`_\n  * `4.17. Isolation-based Methods <#417-isolation-based-methods>`_\n  * `4.18. Fairness and Bias in Outlier Detection <#418-fairness-and-bias-in-outlier-detection>`_\n  * `4.19. Outlier Detection Applications <#419-outlier-detection-applications>`_\n  * `4.20. Outlier Detection in Other fields <#420-outlier-detection-in-other-fields>`_\n  * `4.21. Interactive Outlier Detection <#421-interactive-outlier-detection>`_\n  * `4.22. Active Anomaly Detection <#422-active-anomaly-detection>`_\n\n\n* `5. Key Conferences/Workshops/Journals <#5-key-conferencesworkshopsjournals>`_\n\n  * `5.1. Conferences & Workshops <#51-conferences--workshops>`_\n  * `5.2. Journals <#52-journals>`_\n\n\n----\n\n\n1. Books & Tutorials & Benchmarks\n---------------------------------\n\n1.1. Benchmarks\n^^^^^^^^^^^^^^^\n\n**News**: We have two new works on NLP-based and LLM-based anomaly detection:\n\n- NLP-ADBench: NLP Anomaly Detection Benchmark\n- AD-LLM: Benchmarking Large Language Models for Anomaly Detection\n\n=============  =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nData Types     Paper Title                                                                                        Venue                         Year   Ref                           Materials\n=============  =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nNLP            NLP-ADBench: NLP Anomaly Detection Benchmark                                                       Preprint                      2024   [#Li2024NLPADBench]_          `[PDF] <https://arxiv.org/abs/2412.04784>`_, `[Code] <https://github.com/USC-FORTIS/NLP-ADBench>`_\nNLP            AD-LLM: Benchmarking Large Language Models for Anomaly Detection                                   Preprint                      2024   [#Yang2024ADLLM]_             `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_\nTime-series    The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark               NeurIPS D&B                   2024   [#Liu2024Elephant]_           `[Homepage] <https://nips.cc/virtual/2024/poster/97690>`_, `[PDF] <https://papers.nips.cc/paper_files/paper/2024/file/c3f3c690b7a99fba16d0efd35cb83b2c-Paper-Datasets_and_Benchmarks_Track.pdf>`_, `[Code] <https://github.com/TheDatumOrg/TSB-AD>`_\nGraph          GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection                           NeurIPS                       2023   [#Tang2023GADBench]_          `[PDF] <https://arxiv.org/abs/2306.12251>`_, `[Code] <https://github.com/squareRoot3/GADBench>`_\nTabular        ADGym: Design Choices for Deep Anomaly Detection                                                   NeurIPS                       2023   [#Jiang2023adgym]_            `[PDF] <https://arxiv.org/abs/2309.15376>`_, `[Code] <https://github.com/Minqi824/ADGym>`_\nGraph          Benchmarking Node Outlier Detection on Graphs                                                      NeurIPS                       2022   [#Liu2022Benchmarking]_       `[PDF] <https://arxiv.org/abs/2206.10071>`_, `[Code] <https://github.com/pygod-team/pygod/tree/main/benchmark>`_\nTabular        ADBench: Anomaly Detection Benchmark                                                               NeurIPS                       2022   [#Han2022Adbench]_            `[PDF] <https://arxiv.org/abs/2206.09426>`_, `[Code] <https://github.com/Minqi824/ADBench>`_\nTime-series    Revisiting Time Series Outlier Detection: Definitions and Benchmarks                               NeurIPS                       2021   [#Lai2021Revisiting]_         `[PDF] <https://openreview.net/pdf?id=r8IvOsnHchr>`_, `[Code] <https://github.com/datamllab/tods/tree/benchmark>`_\n=============  =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n1.2. Tutorials\n^^^^^^^^^^^^^^\n\n===================================================== ============================================  =====  ============================  ==========================================================================================================================================================================\nTutorial Title                                        Venue                                         Year   Ref                           Materials\n===================================================== ============================================  =====  ============================  ==========================================================================================================================================================================\nTrustworthy Anomaly Detection                         SDM                                           2024   [#Yuan2024Trustworthy]_       `[HTML] <https://yuan.shuhan.org/talks/SDM24/>`_\nRecent Advances in Anomaly Detection                  CVPR                                          2023   [#Pang2023recent]_            `[HTML] <https://sites.google.com/view/cvpr2023-tutorial-on-ad/>`_, `[Video] <https://www.youtube.com/watch?v=dXxrzWeybBo&feature=youtu.be>`_\nDeep Learning for Anomaly Detection                   WSDM                                          2021   [#Pang2021Deep]_              `[HTML] <https://sites.google.com/site/gspangsite/wsdm21_tutorial>`_\nToward Explainable Deep Anomaly Detection             KDD                                           2021   [#Pang2021Toward]_            `[HTML] <https://sites.google.com/site/gspangsite/kdd21_tutorial>`_\nDeep Learning for Anomaly Detection                   KDD                                           2020   [#Wang2020Deep]_              `[HTML] <https://sites.google.com/view/kdd2020deepeye/home>`_, `[Video] <https://www.youtube.com/watch?v=Fn0qDbKL3UI&list=PLn0nrSd4xjja7AD3aY9Jxmr820gx59EQC&index=66>`_\nWhich Outlier Detector Should I use?                  ICDM                                          2018   [#Ting2018Which]_             `[PDF] <https://ieeexplore.ieee.org/document/8594824>`_\nOutlier detection techniques                          ACM SIGKDD                                    2010   [#Kriegel2010Outlier]_        `[PDF] <https://imada.sdu.dk/~zimek/publications/KDD2010/kdd10-outlier-tutorial.pdf>`_\nData mining for anomaly detection                     PKDD                                          2008   [#Lazarevic2008Data]_         `[Video] <http://videolectures.net/ecmlpkdd08_lazarevic_dmfa/>`_\n===================================================== ============================================  =====  ============================  ==========================================================================================================================================================================\n\n\n1.3. Books\n^^^^^^^^^^\n\n`Outlier Analysis <https://link.springer.com/book/10.1007/978-3-319-47578-3>`_ \nby Charu Aggarwal: Classical text book covering most of the outlier analysis techniques. \nA **must-read** for people in the field of outlier detection. `[Preview.pdf] <http://charuaggarwal.net/outlierbook.pdf>`_\n\n`Outlier Ensembles: An Introduction <https://www.springer.com/gp/book/9783319547640>`_ \nby Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier analysis.\n\n`Data Mining: Concepts and Techniques (3rd) <https://www.elsevier.com/books/data-mining-concepts-and-techniques/han/978-0-12-381479-1>`_ \nby Jiawei Han and Micheline Kamber and Jian Pei: Chapter 12 discusses outlier detection with many key points. `[Google Search] <https://www.google.ca/search?&q=data+mining+jiawei+han&oq=data+ming+jiawei>`_\n\n\n----\n\n2. Courses/Seminars/Videos\n--------------------------\n\n**Coursera Introduction to Anomaly Detection (by IBM)**\\ :\n`[See Video] <https://www.coursera.org/learn/ai/lecture/ASPv0/introduction-to-anomaly-detection>`_\n\n**Get started with the Anomaly Detection API (by IBM)**\\ :\n`[See Website] <https://developer.ibm.com/learningpaths/get-started-anomaly-detection-api/>`_\n\n**Practical Anomaly Detection by appliedAI Institute**\\:\n`[See Website] <https://transferlab.ai/trainings/practical-anomaly-detection/>`_, `[See Video] <https://www.youtube.com/watch?v=sEoMIDARpJ0&list=PLz6xKPm1Bnd6cDDgct3MDhNWJuPXzsmyW>`_, `[See GitHub] <https://github.com/aai-institute/tfl-training-practical-anomaly-detection>`_\n\n**Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic**\\ :\n`[See Video] <https://www.coursera.org/learn/real-time-cyber-threat-detection>`_\n\n**Coursera Machine Learning by Andrew Ng also partly covers the topic**\\ :\n\n\n* `Anomaly Detection vs. Supervised Learning <https://www.coursera.org/learn/machine-learning/lecture/Rkc5x/anomaly-detection-vs-supervised-learning>`_\n* `Developing and Evaluating an Anomaly Detection System <https://www.coursera.org/learn/machine-learning/lecture/Mwrni/developing-and-evaluating-an-anomaly-detection-system>`_\n\n**Udemy Outlier Detection Algorithms in Data Mining and Data Science**\\ :\n`[See Video] <https://www.udemy.com/outlier-detection-techniques/>`_\n\n**Stanford Data Mining for Cyber Security** also covers part of anomaly detection techniques\\ :\n`[See Video] <http://web.stanford.edu/class/cs259d/>`_\n\n----\n\n3. Toolbox & Datasets\n---------------------\n\n[**Python+LLM Agent**] `OpenAD <https://github.com/USC-FORTIS/AD-AGENT>`_: AD-AGENT is a multi-agent framework designed to automate anomaly detection across diverse data modalities, including tabular, graph, time series, and more. It integrates modular agents, model selection strategies, and configurable pipelines to support extensible and interpretable detection workflows. The framework is under active development and aims to support both academic research and practical deployment.\n\n3.1. Multivariate Data\n^^^^^^^^^^^^^^^^^^^^^^\n\n[**Python**] `Python Outlier Detection (PyOD) <https://github.com/yzhao062/pyod>`_\\ : PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. It contains more than 20 detection algorithms, including emerging deep learning models and outlier ensembles.\n\n[**Python**, **GPU**] `TOD: Tensor-based Outlier Detection (PyTOD) <https://github.com/yzhao062/pytod>`_: A general GPU-accelerated framework for outlier detection.\n\n[**Python**] `Python Streaming Anomaly Detection (PySAD) <https://github.com/selimfirat/pysad>`_\\ : PySAD is a streaming anomaly detection framework in Python, which provides a complete set of tools for anomaly detection experiments. It currently contains more than 15 online anomaly detection algorithms and 2 different methods to integrate PyOD detectors to the streaming setting.\n\n[**Python**] `Scikit-learn Novelty and Outlier Detection <http://scikit-learn.org/stable/modules/outlier_detection.html>`_. It supports some popular algorithms like LOF, Isolation Forest, and One-class SVM.\n\n[**Python**] `Scalable Unsupervised Outlier Detection (SUOD) <https://github.com/yzhao062/suod>`_\\ : SUOD (Scalable Unsupervised Outlier Detection) is an acceleration framework for large-scale unsupervised outlier detector training and prediction, on top of PyOD.\n\n[**Julia**] `OutlierDetection.jl <https://github.com/OutlierDetectionJL/OutlierDetection.jl>`_\\ : OutlierDetection.jl is a Julia toolkit for detecting outlying objects, also known as anomalies.\n\n[**Java**] `ELKI: Environment for Developing KDD-Applications Supported by Index-Structures <https://elki-project.github.io/>`_\\ :\nELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. \n\n[**Java**] `RapidMiner Anomaly Detection Extension <https://github.com/Markus-Go/rapidminer-anomalydetection>`_\\ : The Anomaly Detection Extension for RapidMiner comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets. It allows you to find data, which is significantly different from the normal, without the need for the data being labeled.\n\n[**R**] `CRAN Task View: Anomaly Detection with R <https://github.com/pridiltal/ctv-AnomalyDetection>`_\\ : This CRAN task view contains a list of packages that can be used for anomaly detection with R.\n\n[**R**] `outliers package <https://cran.r-project.org/web/packages/outliers/index.html>`_\\ : A collection of some tests commonly used for identifying outliers in R.\n\n[**Matlab**] `Anomaly Detection Toolbox - Beta <http://dsmi-lab-ntust.github.io/AnomalyDetectionToolbox/>`_\\ : A collection of popular outlier detection algorithms in Matlab.\n\n\n3.2. Time Series Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n[**Python**] `TODS <https://github.com/datamllab/tods>`_\\ : TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.\n\n[**Python**] `skyline <https://github.com/earthgecko/skyline>`_\\ : Skyline is a near real time anomaly detection system.\n\n[**Python**] `banpei <https://github.com/tsurubee/banpei>`_\\ : Banpei is a Python package of the anomaly detection.\n\n[**Python**] `telemanom <https://github.com/khundman/telemanom>`_\\ : A framework for using LSTMs to detect anomalies in multivariate time series data.\n\n[**Python**] `DeepADoTS <https://github.com/KDD-OpenSource/DeepADoTS>`_\\ : A benchmarking pipeline for anomaly detection on time series data for multiple state-of-the-art deep learning methods.\n\n[**Python**] `NAB: The Numenta Anomaly Benchmark <https://github.com/numenta/NAB>`_\\ : NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications.\n\n[**Python**] `CueObserve <https://github.com/cuebook/CueObserve>`_\\ : Anomaly detection on SQL data warehouses and databases.\n\n[**Python**] `Chaos Genius <https://github.com/chaos-genius/chaos_genius>`_\\ : ML powered analytics engine for outlier/anomaly detection and root cause analysis.\n\n[**R**] `CRAN Task View: Anomaly Detection with R <https://github.com/pridiltal/ctv-AnomalyDetection>`_\\ : This CRAN task view contains a list of packages that can be used for anomaly detection with R.\n\n[**R**] `AnomalyDetection <https://github.com/twitter/AnomalyDetection>`_\\ : AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend.\n\n[**R**] `anomalize <https://cran.r-project.org/web/packages/anomalize/>`_\\ : The 'anomalize' package enables a \"tidy\" workflow for detecting anomalies in data.\n\n\n3.3. Graph Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n[**Python**] `Python Graph Outlier Detection (PyGOD) <https://github.com/pygod-team/pygod/>`_\\ : PyGOD is a Python library for graph outlier detection (anomaly detection). It includes more than 10 latest graph-based detection algorithms\n\n\n3.4. Real-time Elasticsearch\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n[**Open Distro**] `Real Time Anomaly Detection in Open Distro for Elasticsearch by Amazon <https://github.com/aws/random-cut-forest-by-aws>`_\\ : A machine learning-based anomaly detection plugins for Open Distro for Elasticsearch. See `Real Time Anomaly Detection in Open Distro for Elasticsearch <https://opendistro.github.io/for-elasticsearch/blog/odfe-updates/2019/11/real-time-anomaly-detection-in-open-distro-for-elasticsearch/>`_.\n\n[**Python**] `datastream.io <https://github.com/MentatInnovations/datastream.io>`_\\ : An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.\n\n\n3.5. Datasets\n^^^^^^^^^^^^^\n\n**NLP-ADBench**: NLP Anomaly Detection Benchmark and Datasets: https://github.com/USC-FORTIS/NLP-ADBench\n\n**ELKI Outlier Datasets**\\ : https://elki-project.github.io/datasets/outlier\n\n**Outlier Detection DataSets (ODDS)**\\ : http://odds.cs.stonybrook.edu/#table1\n\n**Unsupervised Anomaly Detection Dataverse**\\ : https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF\n\n**Anomaly Detection Meta-Analysis Benchmarks**\\ : https://ir.library.oregonstate.edu/concern/datasets/47429f155\n\n**Skoltech Anomaly Benchmark (SKAB)**\\ : https://github.com/waico/skab\n\n\n----\n\n\n4. Papers\n---------\n\nRecommended reading order (latest-first):\n\n* `4.1. LLM and LLM Agents for Anomaly Detection <#41-llm-and-llm-agents-for-anomaly-detection>`_\n* `4.2. Emerging and Interesting Topics <#42-emerging-and-interesting-topics>`_\n* `4.3. Weakly-supervised Methods <#43-weakly-supervised-methods>`_\n* `4.4. Machine Learning Systems for Outlier Detection <#44-machine-learning-systems-for-outlier-detection>`_\n* `4.5. Automated Outlier Detection <#45-automated-outlier-detection>`_\n\n4.1. LLM and LLM Agents for Anomaly Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==============================================================================================================  ============================  =====  ============================  =====================================================================================================================================================================================\nPaper Title                                                                                                     Venue                         Year   Ref                           Materials\n==============================================================================================================  ============================  =====  ============================  =====================================================================================================================================================================================\nAD-LLM: Benchmarking Large Language Models for Anomaly Detection                                                ACL 2025 Findings             2024   [#Yang2024ADLLM]_             `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_\nNLP-ADBench: NLP Anomaly Detection Benchmark                                                                    EMNLP 2025 Findings           2024   [#Li2024NLPADBench]_          `[PDF] <https://arxiv.org/abs/2412.04784>`_, `[Code] <https://github.com/USC-FORTIS/NLP-ADBench>`_\nAD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection                                              Findings of IJCNLP-AACL       2025   [#Yang2025ADAGENT]_           `[PDF] <https://arxiv.org/abs/2505.12594>`_, `[Code] <https://github.com/USC-FORTIS/AD-AGENT>`_\nLogSAD: Training-free Anomaly Detection with Vision & Language Foundation Models                                CVPR 2025                     2025   [#Zhang2025LogSAD]_           `[PDF] <https://arxiv.org/abs/2503.18325>`_, `[Code] <https://github.com/zhang0jhon/LogSAD>`_\nMMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection            ICLR 2025                     2025   [#Jiang2025MMAD]_             `[PDF] <https://arxiv.org/abs/2410.09453>`_, `[Code] <https://github.com/jam-cc/MMAD>`_\nDelving into Large Language Models for Effective Time-Series Anomaly Detection                                  NeurIPS 2025                  2025   [#Park2025LLMTSAD]_           `[PDF] <https://openreview.net/pdf?id=6rpy7X1Of8>`_, `[Code] <https://github.com/junwoopark92/LLM-TSAD>`_\n==============================================================================================================  ============================  =====  ============================  =====================================================================================================================================================================================\n\n\n\n4.2. Emerging and Interesting Topics\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nClustering with Outlier Removal                                                                    TKDE                          2019   [#Liu2018Clustering]_         `[PDF] <https://arxiv.org/pdf/1801.01899.pdf>`_\nReal-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning          IEEE Trans. Ind. Informat.    2020   [#Castellani2020Siamese]_     `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9179030>`_\nSSD: A Unified Framework for Self-Supervised Outlier Detection                                     ICLR                          2021   [#Sehwag2021SSD]_             `[PDF] <https://openreview.net/pdf?id=v5gjXpmR8J>`_, `[Code] <https://github.com/inspire-group/SSD>`_\nAD-LLM: Benchmarking Large Language Models for Anomaly Detection                                   Preprint                      2024   [#Yang2024ADLLM]_             `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.3. Weakly-Supervised Methods\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                            Materials\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\nXGBOD: improving supervised outlier detection with unsupervised representation learning            IJCNN                         2018   [#Zhao2018Xgbod]_              `[PDF] <https://arxiv.org/abs/1912.00290>`_\nFeature Encoding With Autoencoders for Weakly Supervised Anomaly Detection                         TNNLS                         2021   [#Zhou2021Feature]_            `[PDF] <https://arxiv.org/pdf/2105.10500.pdf>`_, `[Code] <https://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection>`_\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\n\n\n\n4.4. Machine Learning Systems for Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nThis section summarizes a list of systems for outlier detection, which may\noverlap with the section of tools and libraries.\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPyOD: A Python Toolbox for Scalable Outlier Detection                                              JMLR                          2019   [#Zhao2019PYOD]_              `[PDF] <https://www.jmlr.org/papers/volume20/19-011/19-011.pdf>`_, `[Code] <https://github.com/yzhao062/pyod>`_\nSUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection                        MLSys                         2021   [#Zhao2021SUOD]_              `[PDF] <https://arxiv.org/pdf/2003.05731.pdf>`_, `[Code] <https://github.com/yzhao062/suod>`_\nTOD: Tensor-based Outlier Detection                                                                Preprint                      2021   [#Zhao2021TOD]_               `[PDF] <https://arxiv.org/pdf/2110.14007.pdf>`_, `[Code] <https://github.com/yzhao062/pytod>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n\n4.5. Automated Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nAutoML: state of the art with a focus on anomaly detection, challenges, and research directions    Int J Data Sci Anal           2022   [#Bahri2022automl]_           `[PDF] <https://www.researchgate.net/publication/358364044_AutoML_state_of_the_art_with_a_focus_on_anomaly_detection_challenges_and_research_directions>`_\nAutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning        ICDE                          2020   [#Li2020AutoOD]_              `[PDF] <https://arxiv.org/pdf/2006.11321.pdf>`_\nAutomatic Unsupervised Outlier Model Selection                                                     NeurIPS                       2021   [#Zhao2020Automating]_        `[PDF] <https://openreview.net/forum?id=KCd-3Pz8VjM>`_, `[Code] <https://github.com/yzhao062/MetaOD>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.6. Outlier Detection with Neural Networks\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                         Venue                         Year   Ref                           Materials\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nDetecting spacecraft anomalies using lstms and nonparametric dynamic thresholding                   KDD                           2018   [#Hundman2018Detecting]_      `[PDF] <https://arxiv.org/pdf/1802.04431.pdf>`_, `[Code] <https://github.com/khundman/telemanom>`_\nMAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks   ICANN                         2019   [#Li2019MAD]_                 `[PDF] <https://arxiv.org/pdf/1901.04997.pdf>`_, `[Code] <https://github.com/LiDan456/MAD-GANs>`_\nGenerative Adversarial Active Learning for Unsupervised Outlier Detection                           TKDE                          2019   [#Liu2019Generative]_         `[PDF] <https://arxiv.org/pdf/1809.10816.pdf>`_, `[Code] <https://github.com/leibinghe/GAAL-based-outlier-detection>`_\nDeep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection                         ICLR                          2018   [#Zong2018Deep]_              `[PDF] <http://www.cs.ucsb.edu/~bzong/doc/iclr18-dagmm.pdf>`_, `[Code] <https://github.com/danieltan07/dagmm>`_\nDeep Anomaly Detection with Outlier Exposure                                                        ICLR                          2019   [#Hendrycks2019Deep]_         `[PDF] <https://arxiv.org/pdf/1812.04606.pdf>`_, `[Code] <https://github.com/hendrycks/outlier-exposure>`_\nUnsupervised Anomaly Detection With LSTM Neural Networks                                            TNNLS                         2019   [#Ergen2019Unsupervised]_     `[PDF] <https://arxiv.org/pdf/1710.09207.pdf>`_, `[IEEE] <https://ieeexplore.ieee.org/document/8836638>`_,\nEffective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network   NeurIPS                       2019   [#Wang2019Effective]_         `[PDF] <https://papers.nips.cc/paper/8830-effective-end-to-end-unsupervised-outlier-detection-via-inlier-priority-of-discriminative-network.pdf>`_ `[Code] <https://github.com/demonzyj56/E3Outlier>`_\nFascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning  ICML                          2023   [#Xu2023Fascinating]_         `[PDF] <https://arxiv.org/abs/2305.16114>`_, `[Code] <https://github.com/xuhongzuo/scale-learning>`_ \n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.7. Interpretability\n^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nExplaining Anomalies in Groups with Characterizing Subspace Rules                                  DMKD                          2018   [#Macha2018Explaining]_       `[PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-journal-xpacs.pdf>`_\nBeyond Outlier Detection: LookOut for Pictorial Explanation                                        ECML-PKDD                     2018   [#Gupta2018Beyond]_           `[PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-lookout.pdf>`_\nContextual outlier interpretation                                                                  IJCAI                         2018   [#Liu2018Contextual]_         `[PDF] <https://arxiv.org/pdf/1711.10589.pdf>`_\nMining multidimensional contextual outliers from categorical relational data                       IDA                           2015   [#Tang2015Mining]_            `[PDF] <http://www.cs.sfu.ca/~jpei/publications/Contextual%20outliers.pdf>`_\nDiscriminative features for identifying and interpreting outliers                                  ICDE                          2014   [#Dang2014Discriminative]_    `[PDF] <https://ieeexplore.ieee.org/abstract/document/6816642>`_\nSequential Feature Explanations for Anomaly Detection                                              TKDD                          2019   [#Siddiqui2019Sequential]_    `[HTML] <https://dl.acm.org/citation.cfm?id=3230666>`_\nA Survey on Explainable Anomaly Detection                                                          TKDD                          2023   [#Li2023XAD]_                 `[HTML] <https://dl.acm.org/doi/10.1145/3609333>`_\nExplainable Contextual Anomaly Detection Using Quantile Regression Forests                         DMKD                          2023   [#Li2023QCAD]_                `[HTML] <https://link.springer.com/article/10.1007/s10618-023-00967-z>`_\nBeyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network     WWW                           2021   [#Xu2021Beyond]_              `[PDF] <https://jiansonglei.github.io/files/21WWW.pdf>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.8. Representation Learning in Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                         Venue                         Year   Ref                           Materials\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nLearning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection  SIGKDD                        2018   [#Pang2018Learning]_          `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_\nLearning representations for outlier detection on a budget                                          Preprint                      2015   [#Micenkova2015Learning]_     `[PDF] <https://arxiv.org/pdf/1507.08104.pdf>`_\nXGBOD: improving supervised outlier detection with unsupervised representation learning             IJCNN                         2018   [#Zhao2018Xgbod]_             `[PDF] <https://arxiv.org/abs/1912.00290>`_\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.9. Outlier Detection in Evolving Data\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                         Venue                         Year   Ref                           Materials\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nA Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]   SIGKDD Explorations           2018   [#Salehi2018A]_               `[PDF] <http://www.kdd.org/exploration_files/20-1-Article2.pdf>`_\nUnsupervised real-time anomaly detection for streaming data                                         Neurocomputing                2017   [#Ahmad2017Unsupervised]_     `[PDF] <https://www.researchgate.net/publication/317325599_Unsupervised_real-time_anomaly_detection_for_streaming_data>`_\nOutlier Detection in Feature-Evolving Data Streams                                                  SIGKDD                        2018   [#Manzoor2018Outlier]_        `[PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-kdd-xstream.pdf>`_, `[Github] <https://cmuxstream.github.io/>`_\nEvaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark                    ICMLA                         2015   [#Lavin2015Evaluating]_       `[PDF] <https://arxiv.org/pdf/1510.03336.pdf>`_, `[Github] <https://github.com/numenta/NAB>`_\nMIDAS: Microcluster-Based Detector of Anomalies in Edge Streams                                     AAAI                          2020   [#Bhatia2020MIDAS]_           `[PDF] <https://www.comp.nus.edu.sg/~sbhatia/assets/pdf/midas.pdf>`_, `[Github] <https://github.com/bhatiasiddharth/MIDAS>`_\nNETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing                  VLDB                          2019   [#Yoon2019NETS]_              `[PDF] <http://www.vldb.org/pvldb/vol12/p1303-yoon.pdf>`_, `[Github] <https://github.com/kaist-dmlab/NETS>`_, `[Slide] <https://drive.google.com/file/d/1wqKJZhEE4nTWe0zODu21ejgPDsDA_xaF/view?usp=sharing>`_\nUltrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping                KDD                           2020   [#Yoon2020STARE]_             `[PDF] <https://dl.acm.org/doi/pdf/10.1145/3394486.3403171>`_, `[Github] <https://github.com/kaist-dmlab/STARE>`_, `[Slide] <https://drive.google.com/file/d/11y7Gs703SKJBkPZ4nKKgua__dHXXMbkV/view?usp=sharing>`_\nMultiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries     SIGMOD                        2021   [#Yoon2021MDUAL]_             `[PDF] <https://dl.acm.org/doi/pdf/10.1145/3448016.3452810>`_, `[Github] <https://github.com/kaist-dmlab/MDUAL>`_, `[Slide] <https://drive.google.com/file/d/1wmkkKCAcF9Dk8Wg49WnJF4U--lbtWy9J/view>`_\nAdaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream        KDD                           2022   [#Yoon2022ARCUS]_             `[PDF] <https://dl.acm.org/doi/pdf/10.1145/3534678.3539348>`_, `[Github] <https://github.com/kaist-dmlab/ARCUS>`_, `[Slide] <https://drive.google.com/file/d/1JhrnEj1vScqGy69cfNUpfTjQYZh-vj_D/view>`_\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.10. Outlier Ensembles\n^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nOutlier ensembles: position paper                                                                  SIGKDD Explorations           2013   [#Aggarwal2013Outlier]_       `[PDF] <https://pdfs.semanticscholar.org/841e/ce7c3812bbf799c99c84c064bbcf77916ba9.pdf>`_\nEnsembles for unsupervised outlier detection: challenges and research questions a position paper   SIGKDD Explorations           2014   [#Zimek2014Ensembles]_        `[PDF] <http://www.kdd.org/exploration_files/V15-01-02-Zimek.pdf>`_\nAn Unsupervised Boosting Strategy for Outlier Detection Ensembles                                  PAKDD                         2018   [#Campos2018An]_              `[HTML] <https://link.springer.com/chapter/10.1007/978-3-319-93034-3_45>`_\nLSCP: Locally selective combination in parallel outlier ensembles                                  SDM                           2019   [#Zhao2019LSCP]_              `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.66>`_\nAdaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream       KDD                           2022   [#Yoon2022ARCUS]_             `[PDF] <https://dl.acm.org/doi/pdf/10.1145/3534678.3539348>`_, `[Github] <https://github.com/kaist-dmlab/ARCUS>`_, `[Slide] <https://drive.google.com/file/d/1JhrnEj1vScqGy69cfNUpfTjQYZh-vj_D/view>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n4.11. High-dimensional & Subspace Outliers\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==================================================================================================  ============================  =====  ============================  =======================================================================================================================================================================================================\nPaper Title                                                                                         Venue                         Year   Ref                           Materials\n==================================================================================================  ============================  =====  ============================  =======================================================================================================================================================================================================\nA survey on unsupervised outlier detection in high-dimensional numerical data                       Stat Anal Data Min            2012   [#Zimek2012A]_                `[HTML] <https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.11161>`_\nLearning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection  SIGKDD                        2018   [#Pang2018Learning]_          `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_\nReverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection                          TKDE                          2015   [#Radovanovic2015Reverse]_    `[PDF] <https://ieeexplore.ieee.org/document/6948273>`_, `[SLIDES] <https://pdfs.semanticscholar.org/c8aa/832362422418287ff56793c780b425afa93f.pdf>`_\nOutlier detection for high-dimensional data                                                         Biometrika                    2015   [#Ro2015Outlier]_             `[PDF] <http://web.hku.hk/~gyin/materials/2015RoZouWangYinBiometrika.pdf>`_\n==================================================================================================  ============================  =====  ============================  =======================================================================================================================================================================================================\n\n\n4.12. Feature Selection in Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                                       Venue                         Year   Ref                           Materials\n================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nUnsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings            ICDM                          2016   [#Pang2016Unsupervised]_      `[PDF] <https://opus.lib.uts.edu.au/bitstream/10453/107356/4/DSFS_ICDM2016.pdf>`_\nLearning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection  IJCAI                         2017   [#Pang2017Learning]_          `[PDF] <https://www.ijcai.org/proceedings/2017/0360.pdf>`_\n================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.13. Time Series Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=====================================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                                                            Venue                         Year   Ref                           Materials\n=====================================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nOutlier detection for temporal data: A survey                                                                                          TKDE                          2014   [#Gupta2014Outlier]_          `[PDF] <https://www.microsoft.com/en-us/research/wp-content/uploads/2014/01/gupta14_tkde.pdf>`_\nDetecting spacecraft anomalies using lstms and nonparametric dynamic thresholding                                                      KDD                           2018   [#Hundman2018Detecting]_      `[PDF] <https://arxiv.org/pdf/1802.04431.pdf>`_, `[Code] <https://github.com/khundman/telemanom>`_\nTime-Series Anomaly Detection Service at Microsoft                                                                                     KDD                           2019   [#Ren2019Time]_               `[PDF] <https://arxiv.org/pdf/1906.03821.pdf>`_\nRevisiting Time Series Outlier Detection: Definitions and Benchmarks                                                                   NeurIPS                       2021   [#Lai2021Revisiting]_         `[PDF] <https://openreview.net/pdf?id=r8IvOsnHchr>`_, `[Code] <https://github.com/datamllab/tods/tree/benchmark>`_\nGraph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series                                                        ICLR                          2022   [#Dai2022Graph]_              `[PDF] <https://openreview.net/pdf?id=45L_dgP48Vd>`_, `[Code] <https://github.com/EnyanDai/GANF>`_\nDrift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection      NeurIPS                       2023   [#Wang2023Drift]_             `[PDF] <https://openreview.net/pdf?id=aW5bSuduF1>`_, `[Code] <https://github.com/ForestsKing/D3R>`_\n=====================================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.14. Graph & Network Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  =============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                          Year   Ref                           Materials\n=================================================================================================  =============================  =====  ============================  ==========================================================================================================================================================================\nGraph based anomaly detection and description: a survey                                            DMKD                           2015   [#Akoglu2015Graph]_           `[PDF] <https://arxiv.org/pdf/1404.4679.pdf>`_\nAnomaly detection in dynamic networks: a survey                                                    WIREs Computational Statistic  2015   [#Ranshous2015Anomaly]_       `[PDF] <https://onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1347>`_\nOutlier detection in graphs: On the impact of multiple graph models                                ComSIS                         2019   [#Campos2019Outlier]_         `[PDF] <http://www.comsis.org/pdf.php?id=wims-8671>`_\nA Comprehensive Survey on Graph Anomaly Detection with Deep Learning                               TKDE                           2021   [#Ma2021A]_                   `[PDF] <https://arxiv.org/pdf/2106.07178.pdf>`_\n=================================================================================================  =============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.15. Key Algorithms\n^^^^^^^^^^^^^^^^^^^\n\nAll these algorithms are available in `Python Outlier Detection (PyOD) <https://github.com/yzhao062/pyod>`_.\n\n====================  =================================================================================================  =================================  =====  ===========================  ==============================================================================================================================================================================================\nAbbreviation          Paper Title                                                                                        Venue                              Year   Ref                          Materials\n====================  =================================================================================================  =================================  =====  ===========================  ==============================================================================================================================================================================================\nkNN                   Efficient algorithms for mining outliers from large data sets                                      ACM SIGMOD Record                  2000   [#Ramaswamy2000Efficient]_   `[PDF] <https://webdocs.cs.ualberta.ca/~zaiane/pub/check/ramaswamy.pdf>`_\nKNN                   Fast outlier detection in high dimensional spaces                                                  PKDD                               2002   [#Angiulli2002Fast]_         `[PDF] <https://www.researchgate.net/profile/Clara_Pizzuti/publication/220699183_Fast_Outlier_Detection_in_High_Dimensional_Spaces/links/542ea6a60cf27e39fa9635c6.pdf>`_\nLOF                   LOF: identifying density-based local outliers                                                      ACM SIGMOD Record                  2000   [#Breunig2000LOF]_           `[PDF] <http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf>`_\nIForest               Isolation forest                                                                                   ICDM                               2008   [#Liu2008Isolation]_         `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_\nOCSVM                 Estimating the support of a high-dimensional distribution                                          Neural Computation                 2001   [#Scholkopf2001Estimating]_  `[PDF] <http://users.cecs.anu.edu.au/~williams/papers/P132.pdf>`_\nAutoEncoder Ensemble  Outlier detection with autoencoder ensembles                                                       SDM                                2017   [#Chen2017Outlier]_          `[PDF] <http://saketsathe.net/downloads/autoencode.pdf>`_\nCOPOD                 COPOD: Copula-Based Outlier Detection                                                              ICDM                               2020   [#Li2020COPOD]_              `[PDF] <https://arxiv.org/abs/2009.09463>`_\nECOD                  Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions                   TKDE                               2022   [#Li2021ECOD]_               `[PDF] <https://arxiv.org/abs/2201.00382>`_\n====================  =================================================================================================  =================================  =====  ===========================  ==============================================================================================================================================================================================\n\n4.16. Overview & Survey Papers\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nPapers are sorted by the publication year.\n\n======================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                                             Venue                         Year   Ref                           Materials\n======================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nA survey of outlier detection methodologies                                                                             ARTIF INTELL REV              2004   [#Hodge2004A]_                `[PDF] <https://www-users.cs.york.ac.uk/vicky/myPapers/Hodge+Austin_OutlierDetection_AIRE381.pdf>`_\nAnomaly detection: A survey                                                                                             CSUR                          2009   [#Chandola2009Anomaly]_       `[PDF] <https://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf>`_\nA meta-analysis of the anomaly detection problem                                                                        Preprint                      2015   [#Emmott2015A]_               `[PDF] <https://arxiv.org/pdf/1503.01158.pdf>`_\nOn the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study                         DMKD                          2016   [#Campos2016On]_              `[HTML] <https://link.springer.com/article/10.1007/s10618-015-0444-8>`_, `[SLIDES] <https://imada.sdu.dk/~zimek/InvitedTalks/TUVienna-2016-05-18-outlier-evaluation.pdf>`_\nA comparative evaluation of unsupervised anomaly detection algorithms for multivariate data                             PLOS ONE                      2016   [#Goldstein2016A]_            `[PDF] <http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0152173&type=printable>`_\nA comparative evaluation of outlier detection algorithms: Experiments and analyses                                      Pattern Recognition           2018   [#Domingues2018A]_            `[PDF] <https://www.researchgate.net/publication/320025854_A_comparative_evaluation_of_outlier_detection_algorithms_Experiments_and_analyses>`_\nResearch Issues in Outlier Detection                                                                                    Book Chapter                  2019   [#Suri2019Research]_          `[HTML] <https://link.springer.com/chapter/10.1007/978-3-030-05127-3_3>`_\nQuantitative comparison of unsupervised anomaly detection algorithms for intrusion detection                            SAC                           2019   [#Falcao2019Quantitative]_    `[HTML] <https://dl.acm.org/citation.cfm?id=3297314>`_\nProgress in Outlier Detection Techniques: A Survey                                                                      IEEE Access                   2019   [#Wang2019Progress]_          `[PDF] <https://ieeexplore.ieee.org/iel7/6287639/8600701/08786096.pdf>`_\nDeep learning for anomaly detection: A survey                                                                           Preprint                      2019   [#Chalapathy2019Deep]_        `[PDF] <https://arxiv.org/pdf/1901.03407.pdf>`_\nAnomalous Instance Detection in Deep Learning: A Survey                                                                 Tech Report                   2020   [#Bulusu2020Deep]_            `[PDF] <https://arxiv.org/pdf/2003.06979.pdf>`_\nAnomaly detection in univariate time-series: A survey on the state-of-the-art                                           Preprint                      2020   [#Braei2020Anomaly]_          `[PDF] <https://arxiv.org/pdf/2004.00433.pdf>`_\nDeep Learning for Anomaly Detection: A Review                                                                           CSUR                          2021   [#Pang2020Deep]_              `[PDF] <https://arxiv.org/pdf/2007.02500.pdf>`_\nA Comprehensive Survey on Graph Anomaly Detection with Deep Learning                                                    TKDE                          2021   [#Ma2021A]_                   `[PDF] <https://arxiv.org/pdf/2106.07178.pdf>`_\nRevisiting Time Series Outlier Detection: Definitions and Benchmarks                                                    NeurIPS                       2021   [#Lai2021Revisiting]_         `[PDF] <https://openreview.net/pdf?id=r8IvOsnHchr>`_, `[Code] <https://github.com/datamllab/tods/tree/benchmark>`_\nA Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges      Preprint                      2021   [#Salehi2021A]_               `[PDF] <https://arxiv.org/pdf/2110.14051.pdf>`_\nSelf-Supervised Anomaly Detection: A Survey and Outlook                                                                 Preprint                      2022   [#Hojjati2022Self]_           `[PDF] <https://arxiv.org/pdf/2205.05173.pdf>`_\nWeakly supervised anomaly detection: A survey                                                                           Preprint                      2023   [#Jiang2023weakly]_           `[PDF] <https://arxiv.org/abs/2302.04549>`_, `[PDF] <https://github.com/yzhao062/wsad>`_\nAD-LLM: Benchmarking Large Language Models for Anomaly Detection                                                        Preprint                      2024   [#Yang2024ADLLM]_             `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_\nLarge Language Models for Anomaly and Out-of-Distribution Detection: A Survey                                           Preprint                      2024   [#Xu2024LLMsurvey]_           `[PDF] <https://arxiv.org/abs/2409.01980>`_\n======================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n4.17. Isolation-Based Methods\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                            Materials\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\nIsolation forest                                                                                   ICDM                          2008   [#Liu2008Isolation]_           `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_\nIsolation‐based anomaly detection using nearest‐neighbor ensembles                                  Computational Intelligence    2018   [#Bandaragoda2018Isolation]_   `[PDF] <https://www.researchgate.net/publication/322359651_Isolation-based_anomaly_detection_using_nearest-neighbor_ensembles_iNNE>`_, `[Code] <https://github.com/zhuye88/iNNE>`_\nExtended Isolation Forest                                                                          TKDE                          2019   [#Hariri2019Extended]_         `[PDF] <https://arxiv.org/pdf/1811.02141.pdf>`_, `[Code] <https://github.com/sahandha/eif>`_\nIsolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection                     KDD                           2020   [#Ting2020Isolation]_          `[PDF] <https://arxiv.org/pdf/2009.12196.pdf>`_, `[Code] <https://github.com/IsolationKernel/Codes/tree/main/IDK>`_\nDeep Isolation Forest for Anomaly Detection                                                        TKDE                          2023   [#Xu2023Deep]_                 `[PDF] <https://arxiv.org/abs/2206.06602>`_, `[Code] <https://github.com/xuhongzuo/deep-iforest>`_\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\n\n\n4.18. Fairness and Bias in Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nA Framework for Determining the Fairness of Outlier Detection                                      ECAI                          2020   [#Davidson2020A]_             `[PDF] <https://web.cs.ucdavis.edu/~davidson/Publications/TR.pdf>`_\nFAIROD: Fairness-aware Outlier Detection                                                           AIES                          2021   [#Shekhar2021FAIROD]_         `[PDF] <https://arxiv.org/pdf/2012.03063.pdf>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n\n4.19. Outlier Detection Applications\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n========================    =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nField                       Paper Title                                                                                        Venue                         Year   Ref                           Materials\n========================    =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n**Security**                A survey of distance and similarity measures used within network intrusion anomaly detection       IEEE Commun. Surv. Tutor.     2015   [#WellerFahy2015A]_           `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6853338>`_\n**Security**                Anomaly-based network intrusion detection: Techniques, systems and challenges                      Computers & Security          2009   [#GarciaTeodoro2009Anomaly]_  `[PDF] <https://www2.cs.uh.edu/~acl/cs6397/Doc/2009-Elsevier-Anomaly-based%20network%20intrusion%20detection.pdf>`_\n**Finance**                 A survey of anomaly detection techniques in financial domain                                       Future Gener Comput Syst      2016   [#Ahmed2016A]_                `[PDF] <https://www.sciencedirect.com/science/article/abs/pii/S0167739X15000023>`_\n**Traffic**                 Outlier Detection in Urban Traffic Data                                                            WIMS                          2018   [#Djenouri2018Outlier]_       `[PDF] <http://dss.sdu.dk/assets/fpd-lof/outlier-detection-urban.pdf>`_\n**Social Media**            A survey on social media anomaly detection                                                         SIGKDD Explorations           2016   [#Yu2016A]_                   `[PDF] <https://arxiv.org/pdf/1601.01102.pdf>`_\n**Social Media**            GLAD: group anomaly detection in social media analysis                                             TKDD                          2015   [#Yu2015Glad]_                `[PDF] <https://arxiv.org/pdf/1410.1940.pdf>`_\n**Machine Failure**         Detecting the Onset of Machine Failure Using Anomaly Detection Methods                             DAWAK                         2019   [#Riazi2019Detecting]_        `[PDF] <https://webdocs.cs.ualberta.ca/~zaiane/postscript/DAWAK19.pdf>`_\n**Video Surveillance**      AnomalyNet: An anomaly detection network for video surveillance                                    TIFS                          2019   [#Zhou2019AnomalyNet]_        `[IEEE] <https://ieeexplore.ieee.org/document/8649753>`_, `Code <https://github.com/joeyzhouty/AnomalyNet>`_\n========================    =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.20. Outlier Detection in Other fields\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n============== =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nField          Paper Title                                                                                        Venue                         Year   Ref                           Materials\n============== =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n**Text**       Outlier detection for text data                                                                    SDM                           2017   [#Kannan2017Outlier]_         `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611974973.55>`_\n============== =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.21. Interactive Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nLearning On-the-Job to Re-rank Anomalies from Top-1 Feedback                                       SDM                           2019   [#Lamba2019Learning]_         `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.69>`_\nInteractive anomaly detection on attributed networks                                               WSDM                          2019   [#Ding2019Interactive]_       `[PDF] <http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf>`_\neX2: a framework for interactive anomaly detection                                                 IUI Workshop                  2019   [#Arnaldo2019ex2]_            `[PDF] <http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf>`_\nTripartite Active Learning for Interactive Anomaly Discovery                                       IEEE Access                   2019   [#Zhu2019Tripartite]_         `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.22. Active Anomaly Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                         Venue                         Year   Ref                           Materials\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nActive learning for anomaly and rare-category detection                                             NeurIPS                       2005   [#Pelleg2005Active]_          `[PDF] <http://papers.nips.cc/paper/2554-active-learning-for-anomaly-and-rare-category-detection.pdf>`_\nOutlier detection by active learning                                                                SIGKDD                        2006   [#Abe2006Outlier]_            `[PDF] <https://www.researchgate.net/profile/Naoki_Abe2/publication/221653343_Outlier_detection_by_active_learning/links/5441464a0cf2e6f0c0f60abb.pdf>`_\nActive Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability                  Preprint                      2019   [#Das2019Active]_             `[PDF] <https://arxiv.org/pdf/1901.08930.pdf>`_\nMeta-AAD: Active Anomaly Detection with Deep Reinforcement Learning                                 ICDM                          2020   [#Zha2020Meta]_               `[PDF] <https://arxiv.org/pdf/2009.07415.pdf>`_\nA3: Activation Anomaly Analysis                                                                     ECML-PKDD                     2020   [#Sperl2021A3]_               `[PDF] <https://arxiv.org/pdf/2003.01801>`_, `[Code] <https://github.com/Fraunhofer-AISEC/A3>`_\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n----\n\n5. Key Conferences/Workshops/Journals\n-------------------------------------\n\n5.1. Conferences & Workshops\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nKey data mining conference **deadlines**, **historical acceptance rates**, and more\ncan be found `data-mining-conferences <https://github.com/yzhao062/data-mining-conferences>`_.\n\n\n`ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) <http://www.kdd.org/conferences>`_. **Note**: SIGKDD usually has an Outlier Detection Workshop (ODD), see `ODD 2021 <https://oddworkshop.github.io/>`_.\n\n`ACM International Conference on Management of Data (SIGMOD) <https://sigmod.org/>`_\n\n`The Web Conference (WWW) <https://www2018.thewebconf.org/>`_\n\n`IEEE International Conference on Data Mining (ICDM) <https://icdm2024.org//>`_\n\n`SIAM International Conference on Data Mining (SDM) <https://www.siam.org/Conferences/CM/Main/sdm19>`_\n\n`IEEE International Conference on Data Engineering (ICDE) <https://icde2018.org/>`_\n\n`ACM InternationalConference on Information and Knowledge Management (CIKM) <http://www.cikmconference.org/>`_\n\n`ACM International Conference on Web Search and Data Mining (WSDM) <http://www.wsdm-conference.org/2018/>`_\n\n`The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) <http://www.ecmlpkdd2018.org/>`_\n\n`The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) <http://pakdd2019.medmeeting.org>`_\n\n5.2. Journals\n^^^^^^^^^^^^^\n\n`ACM Transactions on Knowledge Discovery from Data (TKDD) <https://tkdd.acm.org/>`_\n\n`IEEE Transactions on Knowledge and Data Engineering (TKDE) <https://www.computer.org/web/tkde>`_\n\n`ACM SIGKDD Explorations Newsletter <http://www.kdd.org/explorations>`_\n\n`Data Mining and Knowledge Discovery <https://link.springer.com/journal/10618>`_\n\n`Knowledge and Information Systems (KAIS) <https://link.springer.com/journal/10115>`_\n\n----\n\nReferences\n----------\n\n.. [#Abe2006Outlier] Abe, N., Zadrozny, B. and Langford, J., 2006, August. Outlier detection by active learning. In *Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining*, pp. 504-509, ACM.\n\n.. [#Aggarwal2013Outlier] Aggarwal, C.C., 2013. Outlier ensembles: position paper. *ACM SIGKDD Explorations Newsletter*\\ , 14(2), pp.49-58.\n\n.. [#Ahmed2016A] Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. 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[#Lai2021Revisiting] Lai, K.H., Zha, D., Xu, J., Zhao, Y., Wang, G. and Hu, X., 2021. Revisiting Time Series Outlier Detection: Definitions and Benchmarks. *NeurIPS*, Datasets and Benchmarks Track.\n\n.. [#Lamba2019Learning] Lamba, H. and Akoglu, L., 2019, May. Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback. In *Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)*, pp. 612-620. Society for Industrial and Applied Mathematics.\n\n.. [#Lavin2015Evaluating] Lavin, A. and Ahmad, S., 2015, December. Evaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark. In *2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)* (pp. 38-44). IEEE.\n\n.. [#Lazarevic2008Data] Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V. and Srivastava, J., 2008, September. Data mining for anomaly detection. *Tutorial at ECML PKDD 2008*.\n\n.. [#Li2019MAD] Li, D., Chen, D., Jin, B., Shi, L., Goh, J. and Ng, S.K., 2019, September. MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In *International Conference on Artificial Neural Networks* (pp. 703-716). Springer, Cham.\n\n.. [#Li2020COPOD] Li, Z., Zhao, Y., Botta, N., Ionescu, C. and Hu, X. COPOD: Copula-Based Outlier Detection. *IEEE International Conference on Data Mining (ICDM)*, 2020.\n\n.. [#Li2021ECOD] Li, Z., Zhao, Y., Hu, X., Botta, N., Ionescu, C. and Chen, H. G. ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions. *IEEE Transactions on Knowledge and Data Engineering (TKDE)*, 2022.\n\n.. [#Li2023XAD] Li, Z., Zhu, Y. and Van Leeuwen, M., 2023. A survey on explainable anomaly detection. *ACM Transactions on Knowledge Discovery from Data*, 18(1), pp.1-54.\n\n.. [#Li2023QCAD] Li, Z. and Van Leeuwen, M., 2023. Explainable contextual anomaly detection using quantile regression forests. *Data Mining and Knowledge Discovery*, 37(6), pp.2517-2563.\n\n.. [#Li2024NLPADBench] Li, Y., Li, J., Xiao, Z., Yang, T., Nian, Y., Hu, X. and Zhao, Y. \"NLP-ADBench: NLP Anomaly Detection Benchmark,\" arXiv preprint arXiv:2412.04784.\n\n.. [#Liu2008Isolation] Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In *International Conference on Data Mining*\\ , pp. 413-422. IEEE.\n\n.. [#Liu2018Clustering] Liu, H., Li, J., Wu, Y. and Fu, Y., 2019. Clustering with outlier removal. *IEEE transactions on knowledge and data engineering*.\n\n.. [#Liu2018Contextual] Liu, N., Shin, D. and Hu, X., 2017. Contextual outlier interpretation. In *International Joint Conference on Artificial Intelligence (IJCAI-18)*, pp.2461-2467.\n\n.. [#Liu2019Generative] Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M. and He, X., 2019. Generative Adversarial Active Learning for Unsupervised Outlier Detection. *IEEE transactions on knowledge and data engineering*.\n\n.. [#Li2020AutoOD] Li, Y., Chen, Z., Zha, D., Zhou, K., Jin, H., Chen, H. and Hu, X., 2020. AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning. *ICDE*.\n\n.. [#Liu2022Benchmarking] Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K., Sun, L., Li, J., Chen, G.H., Jia, Z., and Yu, P.S. 2022. Benchmarking Node Outlier Detection on Graphs. arXiv preprint arXiv:2206.10071.\n.. [#Liu2024Elephant] Liu, Q. and Paparrizos, J., 2024. The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark. *NeurIPS 2024 Datasets and Benchmarks Track*.\n\n.. [#Ma2021A] Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q.Z., Xiong, H. and Akoglu, L., 2021. A comprehensive survey on graph anomaly detection with deep learning. *IEEE Transactions on Knowledge and Data Engineering*.\n\n.. [#Macha2018Explaining] Macha, M. and Akoglu, L., 2018. Explaining anomalies in groups with characterizing subspace rules. Data Mining and Knowledge Discovery, 32(5), pp.1444-1480.\n\n.. [#Manzoor2018Outlier] Manzoor, E., Lamba, H. and Akoglu, L. Outlier Detection in Feature-Evolving Data Streams. In *24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD)*. 2018.\n\n.. [#Mendiratta2017Anomaly] Mendiratta, B.V., 2017. Anomaly Detection in Networks. *Tutorial at ACM SIGKDD 2017*.\n\n.. [#Micenkova2015Learning] Micenková, B., McWilliams, B. and Assent, I., 2015. Learning representations for outlier detection on a budget. arXiv preprint arXiv:1507.08104.\n\n.. [#Gupta2018Beyond] Gupta, N., Eswaran, D., Shah, N., Akoglu, L. and Faloutsos, C., Beyond Outlier Detection: LookOut for Pictorial Explanation. *ECML PKDD 2018*.\n\n.. [#Han2022Adbench] Han, S., Hu, X., Huang, H., Jiang, M. and Zhao, Y., 2022. ADBench: Anomaly Detection Benchmark. arXiv preprint arXiv:2206.09426.\n\n.. [#Pang2016Unsupervised] Pang, G., Cao, L., Chen, L. and Liu, H., 2016, December. Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings. In Data Mining (ICDM), 2016 IEEE 16th International Conference on (pp. 410-419). IEEE.\n\n.. [#Pang2017Learning] Pang, G., Cao, L., Chen, L. and Liu, H., 2017, August. Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 2585-2591). AAAI Press.\n\n.. [#Pang2018Learning] Pang, G., Cao, L., Chen, L. and Liu, H., 2018. Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection. In *24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD)*. 2018.\n\n.. [#Pang2020Deep] Pang, G., Shen, C., Cao, L. and Hengel, A.V.D., 2021. Deep Learning for Anomaly Detection: A Review. ACM Computing Surveys (CSUR), 54(2), pp.1-38.\n\n.. [#Pang2021Deep] Pang, G., Cao, L. and Aggarwal, C., 2021. Deep Learning for Anomaly Detection. *Tutorial at WSDM 2021*.\n\n.. [#Pang2021Toward] Pang, G. and Aggarwal, C., 2021, August. Toward explainable deep anomaly detection. In *KDD* (pp. 4056-4057).\n\n.. [#Pang2023recent] Guansong Pang, Joey Tianyi Zhou, Radu Tudor Ionescu, Yu Tian, and Kihyuk Sohn. \"Recent Advances in Anomaly Detection\". In: *CVPR'23*. Vancouver, Canada.\n\n.. [#Park2025LLMTSAD] Park, J., Jung, K., Lee, D., Lee, H., Gwak, D., Park, C., Choo, J. and Cho, J., 2025. Delving into Large Language Models for Effective Time-Series Anomaly Detection. In *NeurIPS 2025*.\n\n.. [#Pelleg2005Active] Pelleg, D. and Moore, A.W., 2005. Active learning for anomaly and rare-category detection. In *Advances in neural information processing systems*\\, pp. 1073-1080.\n\n.. [#Radovanovic2015Reverse] Radovanović, M., Nanopoulos, A. and Ivanović, M., 2015. Reverse nearest neighbors in unsupervised distance-based outlier detection. *IEEE transactions on knowledge and data engineering*, 27(5), pp.1369-1382.\n\n.. [#Ramaswamy2000Efficient] Ramaswamy, S., Rastogi, R. and Shim, K., 2000, May. Efficient algorithms for mining outliers from large data sets. *ACM SIGMOD Record*\\ , 29(2), pp. 427-438.\n\n.. [#Ranshous2015Anomaly] Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C. and Samatova, N.F., 2015. Anomaly detection in dynamic networks: a survey. Wiley Interdisciplinary Reviews: Computational Statistics, 7(3), pp.223-247.\n\n.. [#Ren2019Time] Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Xing, T., Yang, M., Tong, J. and Zhang, Q., 2019. Time-Series Anomaly Detection Service at Microsoft. In *Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*. ACM.\n\n.. [#Riazi2019Detecting] Riazi, M., Zaiane, O., Takeuchi, T., Maltais, A., Günther, J. and Lipsett, M., Detecting the Onset of Machine Failure Using Anomaly Detection Methods.\n\n.. [#Ro2015Outlier] Ro, K., Zou, C., Wang, Z. and Yin, G., 2015. Outlier detection for high-dimensional data. *Biometrika*, 102(3), pp.589-599.\n\n.. [#Salehi2018A] Salehi, Mahsa & Rashidi, Lida. (2018). A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]. *ACM SIGKDD Explorations Newsletter*. 20. 13-23.\n\n.. [#Salehi2021A] Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M.H., Sabokrou, M., 2021. A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges. arXiv preprint arXiv:2110.14051.\n\n.. [#Scholkopf2001Estimating] Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J. and Williamson, R.C., 2001. Estimating the support of a high-dimensional distribution. *Neural Computation*, 13(7), pp.1443-1471.\n\n.. [#Sehwag2021SSD] Sehwag, V., Chiang, M., Mittal, P., 2021. SSD: A Unified Framework for Self-Supervised Outlier Detection. *International Conference on Learning Representations (ICLR)*.\n\n.. [#Shekhar2021FAIROD] Shekhar, S., Shah, N. and Akoglu, L., 2021. FAIROD: Fairness-aware Outlier Detection. AAAI/ACM Conference on AI, Ethics, and Society (AIES).\n\n.. [#Siddiqui2019Sequential] Siddiqui, M.A., Fern, A., Dietterich, T.G. and Wong, W.K., 2019. Sequential Feature Explanations for Anomaly Detection. *ACM Transactions on Knowledge Discovery from Data (TKDD)*, 13(1), p.1.\n\n.. [#Sperl2021A3] Sperl, P., Schulze, J.-P., and Böttinger, K., 2021. Activation Anomaly Analysis. *European Conference on Machine Learning and Data Mining (ECML-PKDD) 2020*.\n\n.. [#Suri2019Research] Suri, N.R. and Athithan, G., 2019. Research Issues in Outlier Detection. In *Outlier Detection: Techniques and Applications*, pp. 29-51. Springer, Cham.\n\n.. [#Tang2015Mining] Tang, G., Pei, J., Bailey, J. and Dong, G., 2015. Mining multidimensional contextual outliers from categorical relational data. *Intelligent Data Analysis*, 19(5), pp.1171-1192.\n\n.. [#Tang2023GADBench] Tang, J., Hua, F., Gao, Z., Zhao, P. and Li, J., 2023. GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection. *NeurIPS*, Datasets and Benchmarks Track.\n\n.. [#Ting2018Which] Ting, KM., Aryal, S. and Washio, T., 2018, Which Anomaly Detector should I use? *Tutorial at ICDM 2018*.\n\n.. [#Ting2020Isolation] Ting, Kai Ming, Bi-Cun Xu, Takashi Washio, and Zhi-Hua Zhou. \"Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection.\" In *Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*, pp. 198-206. 2020.\n\n.. [#Wang2019Effective] Wang, S., Zeng, Y., Liu, X., Zhu, E., Yin, J., Xu, C. and Kloft, M., 2019. Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network. In *33rd Conference on Neural Information Processing Systems*.\n\n.. [#Wang2019Progress] Wang, H., Bah, M.J. and Hammad, M., 2019. Progress in Outlier Detection Techniques: A Survey. *IEEE Access*, 7, pp.107964-108000.\n\n.. [#Wang2020Deep] Wang, R., Nie, K., Chang, Y. J., Gong, X., Wang, T., Yang, Y., Long, B.,  2020. Deep Learning for Anomaly Detection. *Tutorial at KDD 2020*.\n\n.. [#WellerFahy2015A] Weller-Fahy, D.J., Borghetti, B.J. and Sodemann, A.A., 2015. A survey of distance and similarity measures used within network intrusion anomaly detection. *IEEE Communications Surveys & Tutorials*\\ , 17(1), pp.70-91.\n\n.. [#Xu2021Beyond] Xu, H., Wang, Y., Jian, S., Huang, Z., Wang, Y., Liu, N. and Li, F., 2021, April. Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network. In *Proceedings of the Web Conference* 2021 (pp. 1328-1339).\n\n.. [#Xu2023Deep] Xu, H., Pang, G., Wang, Y., Wang, Y., 2023. Deep Isolation Forest for Anomaly Detection. *IEEE Transactions on Knowledge and Data Engineering*. \n\n.. [#Xu2023Fascinating] Xu, H., Wang, Y., Wei, J., Jian, S., Li, Y., Liu, N., 2023. Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning. *International Conference on Machine Learning (ICML)*.\n\n.. [#Xu2024LLMsurvey] Xu, R. and Ding, K., 2024. Large language models for anomaly and out-of-distribution detection: A survey. arXiv preprint arXiv:2409.01980.\n\n.. [#Yang2024ADLLM] Yang, T., Nian, Y., Li, S., Xu, R., Li, Y., Li, J., Xiao, Z., Hu, X., Rossi, R., Ding, K., Hu, X. and Zhao, Y. \"AD-LLM: Benchmarking Large Language Models for Anomaly Detection.\" Findings of ACL, 2025.\n\n.. [#Yang2025ADAGENT] Yang, T., Liu, J., Siu, W., Wang, J., Qian, Z., Song, C., Cheng, C., Hu, X., and Zhao, Y. \"AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection.\" Findings of IJCNLP-AACL, 2025.\n\n.. [#Yoon2019NETS] Yoon, S., Lee, J. G., & Lee, B. S., 2019. NETS: extremely fast outlier detection from a data stream via set-based processing. Proceedings of the VLDB Endowment, 12(11), 1303-1315.\n\n.. [#Yoon2020STARE] Yoon, S., Lee, J. G., & Lee, B. S., 2020. Ultrafast local outlier detection from a data stream with stationary region skipping. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1181-1191)\n\n.. [#Yoon2021MDUAL] Yoon, S., Shin, Y., Lee, J. G., & Lee, B. S. (2021, June). Multiple dynamic outlier-detection from a data stream by exploiting duality of data and queries. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD).\n\n.. [#Yoon2022ARCUS] Yoon, S., Lee, Y., Lee, J.G. and Lee, B.S., 2022, August. Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2347-2357).\n\n.. [#Yu2015Glad] Yu, R., He, X. and Liu, Y., 2015. GLAD: group anomaly detection in social media analysis. *ACM Transactions on Knowledge Discovery from Data (TKDD)*\\ , 10(2), p.18.\n\n.. [#Yu2016A] Yu, R., Qiu, H., Wen, Z., Lin, C. and Liu, Y., 2016. A survey on social media anomaly detection. *ACM SIGKDD Explorations Newsletter*\\ , 18(1), pp.1-14.\n\n.. [#Yuan2024Trustworthy] Yuan, S., Xu, D. and Wu, X., 2024  Trustworthy Anomaly Detection. *Tutorial at SDM 2024*.\n\n.. [#Zha2020Meta] Zha, D., Lai, K.H., Wan, M. and Hu, X., 2020. Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning. *ICDM*.\n\n.. [#Zhao2018Xgbod] Zhao, Y. and Hryniewicki, M.K., 2018, July. XGBOD: improving supervised outlier detection with unsupervised representation learning. In *2018 International Joint Conference on Neural Networks (IJCNN)*. IEEE.\n\n.. [#Zhao2019LSCP] Zhao, Y., Nasrullah, Z., Hryniewicki, M.K. and Li, Z., 2019, May. LSCP: Locally selective combination in parallel outlier ensembles. In *Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)*, pp. 585-593. Society for Industrial and Applied Mathematics.\n\n.. [#Zhao2019PYOD] Zhao, Y., Nasrullah, Z. and Li, Z., PyOD: A Python Toolbox for Scalable Outlier Detection. *Journal of Machine Learning Research*, 20, pp.1-7.\n\n.. [#Zhao2020Automating] Zhao, Y., Rossi, R.A. and Akoglu, L., 2021. Automatic Unsupervised Outlier Model Selection. *Advances in Neural Information Processing Systems*.\n\n.. [#Zhao2021SUOD] Zhao, Y., Hu, X., Cheng, C., Wang, C., Wan, C., Wang, W., Yang, J., Bai, H., Li, Z., Xiao, C. and Wang, Y., 2021. SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection. *Proceedings of Machine Learning and Systems (MLSys)*.\n\n.. [#Zhao2021TOD] Zhao, Y., Chen, G.H. and Jia, Z., 2021. TOD: Tensor-based Outlier Detection. arXiv preprint arXiv:2110.14007.\n\n.. [#Zhang2025LogSAD] Zhang, J., Wang, G., Jin, Y. and Huang, D., 2025. Towards Training-free Anomaly Detection with Vision and Language Foundation Models. In *CVPR 2025*.\n\n.. [#Zhou2019AnomalyNet] Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y. and Goh, R.S.M., 2019. AnomalyNet: An anomaly detection network for video surveillance. *IEEE Transactions on Information Forensics and Security*.\n\n.. [#Zhou2021Feature] Zhou, Y., Song, X., Zhang, Y., Liu, F., Zhu, C., & Liu, L. (2021). Feature encoding with autoencoders for weakly supervised anomaly detection. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2454-2465.\n\n.. [#Zhu2019Tripartite] Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. *IEEE Access*.\n\n.. [#Zimek2012A] Zimek, A., Schubert, E. and Kriegel, H.P., 2012. A survey on unsupervised outlier detection in high‐dimensional numerical data. *Statistical Analysis and Data Mining: The ASA Data Science Journal*\\ , 5(5), pp.363-387.\n\n.. [#Zimek2014Ensembles] Zimek, A., Campello, R.J. and Sander, J., 2014. Ensembles for unsupervised outlier detection: challenges and research questions a position paper. *ACM Sigkdd Explorations Newsletter*\\ , 15(1), pp.11-22.\n\n.. [#Zong2018Deep] Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D. and Chen, H., 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. International Conference on Learning Representations (ICLR).\n\n.. [#Wang2023Drift] Wang, C., Zhuang, Z., Qi, Q., Wang, J., Wang, X., Sun, H., & Liao, J. (2023). Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection. Advances in Neural Information Processing Systems, 36.\n\n\n"
  },
  {
    "path": "README_CN.rst",
    "content": "异常检测学习资源\n====================================\n\n.. image:: https://img.shields.io/github/stars/yzhao062/anomaly-detection-resources.svg\n   :target: https://github.com/yzhao062/anomaly-detection-resources/stargazers\n   :alt: GitHub stars\n\n\n.. image:: https://img.shields.io/github/forks/yzhao062/anomaly-detection-resources.svg?color=blue\n   :target: https://github.com/yzhao062/anomaly-detection-resources/network\n   :alt: GitHub forks\n\n\n.. image:: https://img.shields.io/github/license/yzhao062/anomaly-detection-resources.svg?color=blue\n   :target: https://github.com/yzhao062/anomaly-detection-resources/blob/master/LICENSE\n   :alt: License\n\n\n.. image:: https://awesome.re/badge-flat2.svg\n   :target: https://awesome.re/badge-flat2.svg\n   :alt: Awesome\n\n\n.. image:: https://img.shields.io/badge/ADBench-benchmark_results-pink\n   :target: https://github.com/Minqi824/ADBench\n   :alt: Benchmark\n\n\n----\n\n`Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>`_\n（也称 *Anomaly Detection*）是一个既重要又有挑战性的研究方向，\n目标是在数据中识别偏离整体分布的异常样本。\n异常检测已在多个场景被证明十分关键，例如信用卡欺诈分析、\n网络入侵检测和工业设备缺陷检测。\n\n**本仓库收集了以下资源**:\n\n\n#. 书籍与学术论文 \n#. 在线课程与视频\n#. 异常检测数据集\n#. 开源与商业库/工具包\n#. 重要会议与期刊\n\n\n**后续会持续补充更多资源**。\n欢迎通过提交 issue、pull request，或邮件联系 (yzhao010@usc.edu) 推荐更多关键资料。\n祝阅读愉快！\n\n另外，你也可以查看我的 `[GitHub] <https://github.com/yzhao062>`_、`[USC FORTIS Lab] <https://github.com/USC-FORTIS>`_ 和\n`[Google Scholar] <https://scholar.google.com/citations?user=zoGDYsoAAAAJ&hl=en>`_，\n以及相关项目：`PyOD library <https://github.com/yzhao062/pyod>`_、`ADBench benchmark <https://github.com/Minqi824/ADBench>`_、\n`NLP-ADBench: NLP Anomaly Detection Benchmark  <https://github.com/USC-FORTIS/NLP-ADBench>`_。\n\n----\n\n目录\n-----------------\n\n\n* `1. 书籍、教程与基准测试 <#1-books--tutorials--benchmarks>`_\n\n  * `1.1. 基准测试 <#13-benchmarks>`_\n  * `1.2. 教程 <#12-tutorials>`_\n  * `1.3. 书籍 <#11-books>`_\n\n* `2. 课程 / 研讨会 / 视频 <#2-coursesseminarsvideos>`_\n\n* `3. 工具库与数据集 <#3-toolbox--datasets>`_\n\n  * `3.1. 多变量数据异常检测 <#31-multivariate-data>`_\n  * `3.2. 时间序列异常检测 <#32-time-series-outlier-detection>`_\n  * `3.3. 图异常检测 <#33-graph-outlier-detection>`_\n  * `3.4. 实时 Elasticsearch <#34-real-time-elasticsearch>`_\n  * `3.5. 数据集 <#35-datasets>`_\n\n* `4. 论文 <#4-papers>`_\n\n  * `4.1. 用于异常检测的 LLM 与 LLM Agent <#41-llm-and-llm-agents-for-anomaly-detection>`_\n  * `4.2. 新兴与有趣方向 <#42-emerging-and-interesting-topics>`_\n  * `4.3. 弱监督方法 <#43-weakly-supervised-methods>`_\n  * `4.4. 异常检测机器学习系统 <#44-machine-learning-systems-for-outlier-detection>`_\n  * `4.5. 自动化异常检测 <#45-automated-outlier-detection>`_\n  * `4.6. 神经网络异常检测 <#46-outlier-detection-with-neural-networks>`_\n  * `4.7. 可解释性 <#47-interpretability>`_\n  * `4.8. 异常检测中的表征学习 <#48-representation-learning-in-outlier-detection>`_\n  * `4.9. 演化数据中的异常检测 <#49-outlier-detection-in-evolving-data>`_\n  * `4.10. 异常检测集成方法 <#410-outlier-ensembles>`_\n  * `4.11. 高维与子空间异常检测 <#411-high-dimensional--subspace-outliers>`_\n  * `4.12. 异常检测中的特征选择 <#412-feature-selection-in-outlier-detection>`_\n  * `4.13. 时间序列异常检测 <#413-time-series-outlier-detection>`_\n  * `4.14. 图与网络异常检测 <#414-graph--network-outlier-detection>`_\n  * `4.15. 关键算法 <#415-key-algorithms>`_\n  * `4.16. 综述与调查论文 <#416-overview--survey-papers>`_\n  * `4.17. 基于 Isolation 的方法 <#417-isolation-based-methods>`_\n  * `4.18. 异常检测中的公平性与偏差 <#418-fairness-and-bias-in-outlier-detection>`_\n  * `4.19. 异常检测应用 <#419-outlier-detection-applications>`_\n  * `4.20. 其他领域中的异常检测 <#420-outlier-detection-in-other-fields>`_\n  * `4.21. 交互式异常检测 <#421-interactive-outlier-detection>`_\n  * `4.22. 主动异常检测 <#422-active-anomaly-detection>`_\n\n* `5. 重要会议 / Workshop / 期刊 <#5-key-conferencesworkshopsjournals>`_\n\n  * `5.1. 会议与 Workshop <#51-conferences--workshops>`_\n  * `5.2. 期刊 <#52-journals>`_\n\n\n----\n\n\n.. _1-books--tutorials--benchmarks:\n\n1. 书籍、教程与基准测试\n---------------------------------\n\n.. _13-benchmarks:\n\n1.1. 基准测试\n^^^^^^^^^^^^^^\n\n**News**: We have two new works on NLP-based and LLM-based anomaly detection:\n\n- NLP-ADBench: NLP Anomaly Detection Benchmark\n- AD-LLM: Benchmarking Large Language Models for Anomaly Detection\n\n=============  =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nData Types     Paper Title                                                                                        Venue                         Year   Ref                           Materials\n=============  =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nNLP            NLP-ADBench: NLP Anomaly Detection Benchmark                                                       Preprint                      2024   [#Li2024NLPADBench]_          `[PDF] <https://arxiv.org/abs/2412.04784>`_, `[Code] <https://github.com/USC-FORTIS/NLP-ADBench>`_\nNLP            AD-LLM: Benchmarking Large Language Models for Anomaly Detection                                   Preprint                      2024   [#Yang2024ADLLM]_             `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_\nTime-series    The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark               NeurIPS D&B                   2024   [#Liu2024Elephant]_           `[Homepage] <https://nips.cc/virtual/2024/poster/97690>`_, `[PDF] <https://papers.nips.cc/paper_files/paper/2024/file/c3f3c690b7a99fba16d0efd35cb83b2c-Paper-Datasets_and_Benchmarks_Track.pdf>`_, `[Code] <https://github.com/TheDatumOrg/TSB-AD>`_\nGraph          GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection                           NeurIPS                       2023   [#Tang2023GADBench]_          `[PDF] <https://arxiv.org/abs/2306.12251>`_, `[Code] <https://github.com/squareRoot3/GADBench>`_\nTabular        ADGym: Design Choices for Deep Anomaly Detection                                                   NeurIPS                       2023   [#Jiang2023adgym]_            `[PDF] <https://arxiv.org/abs/2309.15376>`_, `[Code] <https://github.com/Minqi824/ADGym>`_\nGraph          Benchmarking Node Outlier Detection on Graphs                                                      NeurIPS                       2022   [#Liu2022Benchmarking]_       `[PDF] <https://arxiv.org/abs/2206.10071>`_, `[Code] <https://github.com/pygod-team/pygod/tree/main/benchmark>`_\nTabular        ADBench: Anomaly Detection Benchmark                                                               NeurIPS                       2022   [#Han2022Adbench]_            `[PDF] <https://arxiv.org/abs/2206.09426>`_, `[Code] <https://github.com/Minqi824/ADBench>`_\nTime-series    Revisiting Time Series Outlier Detection: Definitions and Benchmarks                               NeurIPS                       2021   [#Lai2021Revisiting]_         `[PDF] <https://openreview.net/pdf?id=r8IvOsnHchr>`_, `[Code] <https://github.com/datamllab/tods/tree/benchmark>`_\n=============  =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n.. _12-tutorials:\n\n1.2. 教程\n^^^^^^^^^^^^^^\n\n===================================================== ============================================  =====  ============================  ==========================================================================================================================================================================\nTutorial Title                                        Venue                                         Year   Ref                           Materials\n===================================================== ============================================  =====  ============================  ==========================================================================================================================================================================\nTrustworthy Anomaly Detection                         SDM                                           2024   [#Yuan2024Trustworthy]_       `[HTML] <https://yuan.shuhan.org/talks/SDM24/>`_\nRecent Advances in Anomaly Detection                  CVPR                                          2023   [#Pang2023recent]_            `[HTML] <https://sites.google.com/view/cvpr2023-tutorial-on-ad/>`_, `[Video] <https://www.youtube.com/watch?v=dXxrzWeybBo&feature=youtu.be>`_\nDeep Learning for Anomaly Detection                   WSDM                                          2021   [#Pang2021Deep]_              `[HTML] <https://sites.google.com/site/gspangsite/wsdm21_tutorial>`_\nToward Explainable Deep Anomaly Detection             KDD                                           2021   [#Pang2021Toward]_            `[HTML] <https://sites.google.com/site/gspangsite/kdd21_tutorial>`_\nDeep Learning for Anomaly Detection                   KDD                                           2020   [#Wang2020Deep]_              `[HTML] <https://sites.google.com/view/kdd2020deepeye/home>`_, `[Video] <https://www.youtube.com/watch?v=Fn0qDbKL3UI&list=PLn0nrSd4xjja7AD3aY9Jxmr820gx59EQC&index=66>`_\nWhich Outlier Detector Should I use?                  ICDM                                          2018   [#Ting2018Which]_             `[PDF] <https://ieeexplore.ieee.org/document/8594824>`_\nOutlier detection techniques                          ACM SIGKDD                                    2010   [#Kriegel2010Outlier]_        `[PDF] <https://imada.sdu.dk/~zimek/publications/KDD2010/kdd10-outlier-tutorial.pdf>`_\nData mining for anomaly detection                     PKDD                                          2008   [#Lazarevic2008Data]_         `[Video] <http://videolectures.net/ecmlpkdd08_lazarevic_dmfa/>`_\n===================================================== ============================================  =====  ============================  ==========================================================================================================================================================================\n\n\n.. _11-books:\n\n1.3. 书籍\n^^^^^^^^^^\n\n`Outlier Analysis <https://link.springer.com/book/10.1007/978-3-319-47578-3>`_ \nby Charu Aggarwal: Classical text book covering most of the outlier analysis techniques. \nA **must-read** for people in the field of outlier detection. `[Preview.pdf] <http://charuaggarwal.net/outlierbook.pdf>`_\n\n`Outlier Ensembles: An Introduction <https://www.springer.com/gp/book/9783319547640>`_ \nby Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier analysis.\n\n`Data Mining: Concepts and Techniques (3rd) <https://www.elsevier.com/books/data-mining-concepts-and-techniques/han/978-0-12-381479-1>`_ \nby Jiawei Han and Micheline Kamber and Jian Pei: Chapter 12 discusses outlier detection with many key points. `[Google Search] <https://www.google.ca/search?&q=data+mining+jiawei+han&oq=data+ming+jiawei>`_\n\n\n----\n\n.. _2-coursesseminarsvideos:\n\n2. 课程 / 研讨会 / 视频\n--------------------------\n\n**Coursera Introduction to Anomaly Detection (by IBM)**\\ :\n`[See Video] <https://www.coursera.org/learn/ai/lecture/ASPv0/introduction-to-anomaly-detection>`_\n\n**Get started with the Anomaly Detection API (by IBM)**\\ :\n`[See Website] <https://developer.ibm.com/learningpaths/get-started-anomaly-detection-api/>`_\n\n**Practical Anomaly Detection by appliedAI Institute**\\:\n`[See Website] <https://transferlab.ai/trainings/practical-anomaly-detection/>`_, `[See Video] <https://www.youtube.com/watch?v=sEoMIDARpJ0&list=PLz6xKPm1Bnd6cDDgct3MDhNWJuPXzsmyW>`_, `[See GitHub] <https://github.com/aai-institute/tfl-training-practical-anomaly-detection>`_\n\n**Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic**\\ :\n`[See Video] <https://www.coursera.org/learn/real-time-cyber-threat-detection>`_\n\n**Coursera Machine Learning by Andrew Ng also partly covers the topic**\\ :\n\n\n* `Anomaly Detection vs. Supervised Learning <https://www.coursera.org/learn/machine-learning/lecture/Rkc5x/anomaly-detection-vs-supervised-learning>`_\n* `Developing and Evaluating an Anomaly Detection System <https://www.coursera.org/learn/machine-learning/lecture/Mwrni/developing-and-evaluating-an-anomaly-detection-system>`_\n\n**Udemy Outlier Detection Algorithms in Data Mining and Data Science**\\ :\n`[See Video] <https://www.udemy.com/outlier-detection-techniques/>`_\n\n**Stanford Data Mining for Cyber Security** also covers part of anomaly detection techniques\\ :\n`[See Video] <http://web.stanford.edu/class/cs259d/>`_\n\n----\n\n.. _3-toolbox--datasets:\n\n3. 工具库与数据集\n---------------------\n\n[**Python+LLM Agent**] `OpenAD <https://github.com/USC-FORTIS/AD-AGENT>`_: AD-AGENT is a multi-agent framework designed to automate anomaly detection across diverse data modalities, including tabular, graph, time series, and more. It integrates modular agents, model selection strategies, and configurable pipelines to support extensible and interpretable detection workflows. The framework is under active development and aims to support both academic research and practical deployment.\n\n.. _31-multivariate-data:\n\n3.1. 多变量数据异常检测\n^^^^^^^^^^^^^^^^^^^^^^\n\n[**Python**] `Python Outlier Detection (PyOD) <https://github.com/yzhao062/pyod>`_\\ : PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. It contains more than 20 detection algorithms, including emerging deep learning models and outlier ensembles.\n\n[**Python**, **GPU**] `TOD: Tensor-based Outlier Detection (PyTOD) <https://github.com/yzhao062/pytod>`_: A general GPU-accelerated framework for outlier detection.\n\n[**Python**] `Python Streaming Anomaly Detection (PySAD) <https://github.com/selimfirat/pysad>`_\\ : PySAD is a streaming anomaly detection framework in Python, which provides a complete set of tools for anomaly detection experiments. It currently contains more than 15 online anomaly detection algorithms and 2 different methods to integrate PyOD detectors to the streaming setting.\n\n[**Python**] `Scikit-learn Novelty and Outlier Detection <http://scikit-learn.org/stable/modules/outlier_detection.html>`_. It supports some popular algorithms like LOF, Isolation Forest, and One-class SVM.\n\n[**Python**] `Scalable Unsupervised Outlier Detection (SUOD) <https://github.com/yzhao062/suod>`_\\ : SUOD (Scalable Unsupervised Outlier Detection) is an acceleration framework for large-scale unsupervised outlier detector training and prediction, on top of PyOD.\n\n[**Julia**] `OutlierDetection.jl <https://github.com/OutlierDetectionJL/OutlierDetection.jl>`_\\ : OutlierDetection.jl is a Julia toolkit for detecting outlying objects, also known as anomalies.\n\n[**Java**] `ELKI: Environment for Developing KDD-Applications Supported by Index-Structures <https://elki-project.github.io/>`_\\ :\nELKI is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. \n\n[**Java**] `RapidMiner Anomaly Detection Extension <https://github.com/Markus-Go/rapidminer-anomalydetection>`_\\ : The Anomaly Detection Extension for RapidMiner comprises the most well know unsupervised anomaly detection algorithms, assigning individual anomaly scores to data rows of example sets. It allows you to find data, which is significantly different from the normal, without the need for the data being labeled.\n\n[**R**] `CRAN Task View: Anomaly Detection with R <https://github.com/pridiltal/ctv-AnomalyDetection>`_\\ : This CRAN task view contains a list of packages that can be used for anomaly detection with R.\n\n[**R**] `outliers package <https://cran.r-project.org/web/packages/outliers/index.html>`_\\ : A collection of some tests commonly used for identifying outliers in R.\n\n[**Matlab**] `Anomaly Detection Toolbox - Beta <http://dsmi-lab-ntust.github.io/AnomalyDetectionToolbox/>`_\\ : A collection of popular outlier detection algorithms in Matlab.\n\n\n.. _32-time-series-outlier-detection:\n\n3.2. 时间序列异常检测\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n[**Python**] `TODS <https://github.com/datamllab/tods>`_\\ : TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.\n\n[**Python**] `skyline <https://github.com/earthgecko/skyline>`_\\ : Skyline is a near real time anomaly detection system.\n\n[**Python**] `banpei <https://github.com/tsurubee/banpei>`_\\ : Banpei is a Python package of the anomaly detection.\n\n[**Python**] `telemanom <https://github.com/khundman/telemanom>`_\\ : A framework for using LSTMs to detect anomalies in multivariate time series data.\n\n[**Python**] `DeepADoTS <https://github.com/KDD-OpenSource/DeepADoTS>`_\\ : A benchmarking pipeline for anomaly detection on time series data for multiple state-of-the-art deep learning methods.\n\n[**Python**] `NAB: The Numenta Anomaly Benchmark <https://github.com/numenta/NAB>`_\\ : NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications.\n\n[**Python**] `CueObserve <https://github.com/cuebook/CueObserve>`_\\ : Anomaly detection on SQL data warehouses and databases.\n\n[**Python**] `Chaos Genius <https://github.com/chaos-genius/chaos_genius>`_\\ : ML powered analytics engine for outlier/anomaly detection and root cause analysis.\n\n[**R**] `CRAN Task View: Anomaly Detection with R <https://github.com/pridiltal/ctv-AnomalyDetection>`_\\ : This CRAN task view contains a list of packages that can be used for anomaly detection with R.\n\n[**R**] `AnomalyDetection <https://github.com/twitter/AnomalyDetection>`_\\ : AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend.\n\n[**R**] `anomalize <https://cran.r-project.org/web/packages/anomalize/>`_\\ : The 'anomalize' package enables a \"tidy\" workflow for detecting anomalies in data.\n\n\n.. _33-graph-outlier-detection:\n\n3.3. 图异常检测\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n[**Python**] `Python Graph Outlier Detection (PyGOD) <https://github.com/pygod-team/pygod/>`_\\ : PyGOD is a Python library for graph outlier detection (anomaly detection). It includes more than 10 latest graph-based detection algorithms\n\n\n.. _34-real-time-elasticsearch:\n\n3.4. 实时 Elasticsearch\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n[**Open Distro**] `Real Time Anomaly Detection in Open Distro for Elasticsearch by Amazon <https://github.com/aws/random-cut-forest-by-aws>`_\\ : A machine learning-based anomaly detection plugins for Open Distro for Elasticsearch. See `Real Time Anomaly Detection in Open Distro for Elasticsearch <https://opendistro.github.io/for-elasticsearch/blog/odfe-updates/2019/11/real-time-anomaly-detection-in-open-distro-for-elasticsearch/>`_.\n\n[**Python**] `datastream.io <https://github.com/MentatInnovations/datastream.io>`_\\ : An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.\n\n\n.. _35-datasets:\n\n3.5. 数据集\n^^^^^^^^^^^^^\n\n**NLP-ADBench**: NLP Anomaly Detection Benchmark and Datasets: https://github.com/USC-FORTIS/NLP-ADBench\n\n**ELKI Outlier Datasets**\\ : https://elki-project.github.io/datasets/outlier\n\n**Outlier Detection DataSets (ODDS)**\\ : http://odds.cs.stonybrook.edu/#table1\n\n**Unsupervised Anomaly Detection Dataverse**\\ : https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF\n\n**Anomaly Detection Meta-Analysis Benchmarks**\\ : https://ir.library.oregonstate.edu/concern/datasets/47429f155\n\n**Skoltech Anomaly Benchmark (SKAB)**\\ : https://github.com/waico/skab\n\n\n----\n\n\n.. _4-papers:\n\n4. 论文\n---------\n\n推荐阅读顺序（前沿优先）:\n\n* `4.1. 用于异常检测的 LLM 与 LLM Agent <#41-llm-and-llm-agents-for-anomaly-detection>`_\n* `4.2. 新兴与有趣方向 <#42-emerging-and-interesting-topics>`_\n* `4.3. 弱监督方法 <#43-weakly-supervised-methods>`_\n* `4.4. 异常检测机器学习系统 <#44-machine-learning-systems-for-outlier-detection>`_\n* `4.5. 自动化异常检测 <#45-automated-outlier-detection>`_\n\n4.1. LLM and LLM Agents for Anomaly Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==============================================================================================================  ============================  =====  ============================  =====================================================================================================================================================================================\nPaper Title                                                                                                     Venue                         Year   Ref                           Materials\n==============================================================================================================  ============================  =====  ============================  =====================================================================================================================================================================================\nAD-LLM: Benchmarking Large Language Models for Anomaly Detection                                                ACL 2025 Findings             2024   [#Yang2024ADLLM]_             `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_\nNLP-ADBench: NLP Anomaly Detection Benchmark                                                                    EMNLP 2025 Findings           2024   [#Li2024NLPADBench]_          `[PDF] <https://arxiv.org/abs/2412.04784>`_, `[Code] <https://github.com/USC-FORTIS/NLP-ADBench>`_\nAD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection                                              Findings of IJCNLP-AACL       2025   [#Yang2025ADAGENT]_           `[PDF] <https://arxiv.org/abs/2505.12594>`_, `[Code] <https://github.com/USC-FORTIS/AD-AGENT>`_\nLogSAD: Training-free Anomaly Detection with Vision & Language Foundation Models                                CVPR 2025                     2025   [#Zhang2025LogSAD]_           `[PDF] <https://arxiv.org/abs/2503.18325>`_, `[Code] <https://github.com/zhang0jhon/LogSAD>`_\nMMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection            ICLR 2025                     2025   [#Jiang2025MMAD]_             `[PDF] <https://arxiv.org/abs/2410.09453>`_, `[Code] <https://github.com/jam-cc/MMAD>`_\nDelving into Large Language Models for Effective Time-Series Anomaly Detection                                  NeurIPS 2025                  2025   [#Park2025LLMTSAD]_           `[PDF] <https://openreview.net/pdf?id=6rpy7X1Of8>`_, `[Code] <https://github.com/junwoopark92/LLM-TSAD>`_\n==============================================================================================================  ============================  =====  ============================  =====================================================================================================================================================================================\n\n\n\n4.2. Emerging and Interesting Topics\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nClustering with Outlier Removal                                                                    TKDE                          2019   [#Liu2018Clustering]_         `[PDF] <https://arxiv.org/pdf/1801.01899.pdf>`_\nReal-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning          IEEE Trans. Ind. Informat.    2020   [#Castellani2020Siamese]_     `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9179030>`_\nSSD: A Unified Framework for Self-Supervised Outlier Detection                                     ICLR                          2021   [#Sehwag2021SSD]_             `[PDF] <https://openreview.net/pdf?id=v5gjXpmR8J>`_, `[Code] <https://github.com/inspire-group/SSD>`_\nAD-LLM: Benchmarking Large Language Models for Anomaly Detection                                   Preprint                      2024   [#Yang2024ADLLM]_             `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.3. Weakly-Supervised Methods\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                            Materials\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\nXGBOD: improving supervised outlier detection with unsupervised representation learning            IJCNN                         2018   [#Zhao2018Xgbod]_              `[PDF] <https://arxiv.org/abs/1912.00290>`_\nFeature Encoding With Autoencoders for Weakly Supervised Anomaly Detection                         TNNLS                         2021   [#Zhou2021Feature]_            `[PDF] <https://arxiv.org/pdf/2105.10500.pdf>`_, `[Code] <https://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection>`_\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\n\n\n\n4.4. Machine Learning Systems for Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nThis section summarizes a list of systems for outlier detection, which may\noverlap with the section of tools and libraries.\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPyOD: A Python Toolbox for Scalable Outlier Detection                                              JMLR                          2019   [#Zhao2019PYOD]_              `[PDF] <https://www.jmlr.org/papers/volume20/19-011/19-011.pdf>`_, `[Code] <https://github.com/yzhao062/pyod>`_\nSUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection                        MLSys                         2021   [#Zhao2021SUOD]_              `[PDF] <https://arxiv.org/pdf/2003.05731.pdf>`_, `[Code] <https://github.com/yzhao062/suod>`_\nTOD: Tensor-based Outlier Detection                                                                Preprint                      2021   [#Zhao2021TOD]_               `[PDF] <https://arxiv.org/pdf/2110.14007.pdf>`_, `[Code] <https://github.com/yzhao062/pytod>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n\n4.5. Automated Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nAutoML: state of the art with a focus on anomaly detection, challenges, and research directions    Int J Data Sci Anal           2022   [#Bahri2022automl]_           `[PDF] <https://www.researchgate.net/publication/358364044_AutoML_state_of_the_art_with_a_focus_on_anomaly_detection_challenges_and_research_directions>`_\nAutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning        ICDE                          2020   [#Li2020AutoOD]_              `[PDF] <https://arxiv.org/pdf/2006.11321.pdf>`_\nAutomatic Unsupervised Outlier Model Selection                                                     NeurIPS                       2021   [#Zhao2020Automating]_        `[PDF] <https://openreview.net/forum?id=KCd-3Pz8VjM>`_, `[Code] <https://github.com/yzhao062/MetaOD>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.6. Outlier Detection with Neural Networks\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                         Venue                         Year   Ref                           Materials\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nDetecting spacecraft anomalies using lstms and nonparametric dynamic thresholding                   KDD                           2018   [#Hundman2018Detecting]_      `[PDF] <https://arxiv.org/pdf/1802.04431.pdf>`_, `[Code] <https://github.com/khundman/telemanom>`_\nMAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks   ICANN                         2019   [#Li2019MAD]_                 `[PDF] <https://arxiv.org/pdf/1901.04997.pdf>`_, `[Code] <https://github.com/LiDan456/MAD-GANs>`_\nGenerative Adversarial Active Learning for Unsupervised Outlier Detection                           TKDE                          2019   [#Liu2019Generative]_         `[PDF] <https://arxiv.org/pdf/1809.10816.pdf>`_, `[Code] <https://github.com/leibinghe/GAAL-based-outlier-detection>`_\nDeep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection                         ICLR                          2018   [#Zong2018Deep]_              `[PDF] <http://www.cs.ucsb.edu/~bzong/doc/iclr18-dagmm.pdf>`_, `[Code] <https://github.com/danieltan07/dagmm>`_\nDeep Anomaly Detection with Outlier Exposure                                                        ICLR                          2019   [#Hendrycks2019Deep]_         `[PDF] <https://arxiv.org/pdf/1812.04606.pdf>`_, `[Code] <https://github.com/hendrycks/outlier-exposure>`_\nUnsupervised Anomaly Detection With LSTM Neural Networks                                            TNNLS                         2019   [#Ergen2019Unsupervised]_     `[PDF] <https://arxiv.org/pdf/1710.09207.pdf>`_, `[IEEE] <https://ieeexplore.ieee.org/document/8836638>`_,\nEffective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network   NeurIPS                       2019   [#Wang2019Effective]_         `[PDF] <https://papers.nips.cc/paper/8830-effective-end-to-end-unsupervised-outlier-detection-via-inlier-priority-of-discriminative-network.pdf>`_ `[Code] <https://github.com/demonzyj56/E3Outlier>`_\nFascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning  ICML                          2023   [#Xu2023Fascinating]_         `[PDF] <https://arxiv.org/abs/2305.16114>`_, `[Code] <https://github.com/xuhongzuo/scale-learning>`_ \n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.7. Interpretability\n^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nExplaining Anomalies in Groups with Characterizing Subspace Rules                                  DMKD                          2018   [#Macha2018Explaining]_       `[PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-journal-xpacs.pdf>`_\nBeyond Outlier Detection: LookOut for Pictorial Explanation                                        ECML-PKDD                     2018   [#Gupta2018Beyond]_           `[PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-lookout.pdf>`_\nContextual outlier interpretation                                                                  IJCAI                         2018   [#Liu2018Contextual]_         `[PDF] <https://arxiv.org/pdf/1711.10589.pdf>`_\nMining multidimensional contextual outliers from categorical relational data                       IDA                           2015   [#Tang2015Mining]_            `[PDF] <http://www.cs.sfu.ca/~jpei/publications/Contextual%20outliers.pdf>`_\nDiscriminative features for identifying and interpreting outliers                                  ICDE                          2014   [#Dang2014Discriminative]_    `[PDF] <https://ieeexplore.ieee.org/abstract/document/6816642>`_\nSequential Feature Explanations for Anomaly Detection                                              TKDD                          2019   [#Siddiqui2019Sequential]_    `[HTML] <https://dl.acm.org/citation.cfm?id=3230666>`_\nA Survey on Explainable Anomaly Detection                                                          TKDD                          2023   [#Li2023XAD]_                 `[HTML] <https://dl.acm.org/doi/10.1145/3609333>`_\nExplainable Contextual Anomaly Detection Using Quantile Regression Forests                         DMKD                          2023   [#Li2023QCAD]_                `[HTML] <https://link.springer.com/article/10.1007/s10618-023-00967-z>`_\nBeyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network     WWW                           2021   [#Xu2021Beyond]_              `[PDF] <https://jiansonglei.github.io/files/21WWW.pdf>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.8. Representation Learning in Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                         Venue                         Year   Ref                           Materials\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nLearning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection  SIGKDD                        2018   [#Pang2018Learning]_          `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_\nLearning representations for outlier detection on a budget                                          Preprint                      2015   [#Micenkova2015Learning]_     `[PDF] <https://arxiv.org/pdf/1507.08104.pdf>`_\nXGBOD: improving supervised outlier detection with unsupervised representation learning             IJCNN                         2018   [#Zhao2018Xgbod]_             `[PDF] <https://arxiv.org/abs/1912.00290>`_\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.9. Outlier Detection in Evolving Data\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                         Venue                         Year   Ref                           Materials\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nA Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]   SIGKDD Explorations           2018   [#Salehi2018A]_               `[PDF] <http://www.kdd.org/exploration_files/20-1-Article2.pdf>`_\nUnsupervised real-time anomaly detection for streaming data                                         Neurocomputing                2017   [#Ahmad2017Unsupervised]_     `[PDF] <https://www.researchgate.net/publication/317325599_Unsupervised_real-time_anomaly_detection_for_streaming_data>`_\nOutlier Detection in Feature-Evolving Data Streams                                                  SIGKDD                        2018   [#Manzoor2018Outlier]_        `[PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-kdd-xstream.pdf>`_, `[Github] <https://cmuxstream.github.io/>`_\nEvaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark                    ICMLA                         2015   [#Lavin2015Evaluating]_       `[PDF] <https://arxiv.org/pdf/1510.03336.pdf>`_, `[Github] <https://github.com/numenta/NAB>`_\nMIDAS: Microcluster-Based Detector of Anomalies in Edge Streams                                     AAAI                          2020   [#Bhatia2020MIDAS]_           `[PDF] <https://www.comp.nus.edu.sg/~sbhatia/assets/pdf/midas.pdf>`_, `[Github] <https://github.com/bhatiasiddharth/MIDAS>`_\nNETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing                  VLDB                          2019   [#Yoon2019NETS]_              `[PDF] <http://www.vldb.org/pvldb/vol12/p1303-yoon.pdf>`_, `[Github] <https://github.com/kaist-dmlab/NETS>`_, `[Slide] <https://drive.google.com/file/d/1wqKJZhEE4nTWe0zODu21ejgPDsDA_xaF/view?usp=sharing>`_\nUltrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping                KDD                           2020   [#Yoon2020STARE]_             `[PDF] <https://dl.acm.org/doi/pdf/10.1145/3394486.3403171>`_, `[Github] <https://github.com/kaist-dmlab/STARE>`_, `[Slide] <https://drive.google.com/file/d/11y7Gs703SKJBkPZ4nKKgua__dHXXMbkV/view?usp=sharing>`_\nMultiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries     SIGMOD                        2021   [#Yoon2021MDUAL]_             `[PDF] <https://dl.acm.org/doi/pdf/10.1145/3448016.3452810>`_, `[Github] <https://github.com/kaist-dmlab/MDUAL>`_, `[Slide] <https://drive.google.com/file/d/1wmkkKCAcF9Dk8Wg49WnJF4U--lbtWy9J/view>`_\nAdaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream        KDD                           2022   [#Yoon2022ARCUS]_             `[PDF] <https://dl.acm.org/doi/pdf/10.1145/3534678.3539348>`_, `[Github] <https://github.com/kaist-dmlab/ARCUS>`_, `[Slide] <https://drive.google.com/file/d/1JhrnEj1vScqGy69cfNUpfTjQYZh-vj_D/view>`_\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.10. Outlier Ensembles\n^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nOutlier ensembles: position paper                                                                  SIGKDD Explorations           2013   [#Aggarwal2013Outlier]_       `[PDF] <https://pdfs.semanticscholar.org/841e/ce7c3812bbf799c99c84c064bbcf77916ba9.pdf>`_\nEnsembles for unsupervised outlier detection: challenges and research questions a position paper   SIGKDD Explorations           2014   [#Zimek2014Ensembles]_        `[PDF] <http://www.kdd.org/exploration_files/V15-01-02-Zimek.pdf>`_\nAn Unsupervised Boosting Strategy for Outlier Detection Ensembles                                  PAKDD                         2018   [#Campos2018An]_              `[HTML] <https://link.springer.com/chapter/10.1007/978-3-319-93034-3_45>`_\nLSCP: Locally selective combination in parallel outlier ensembles                                  SDM                           2019   [#Zhao2019LSCP]_              `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.66>`_\nAdaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream       KDD                           2022   [#Yoon2022ARCUS]_             `[PDF] <https://dl.acm.org/doi/pdf/10.1145/3534678.3539348>`_, `[Github] <https://github.com/kaist-dmlab/ARCUS>`_, `[Slide] <https://drive.google.com/file/d/1JhrnEj1vScqGy69cfNUpfTjQYZh-vj_D/view>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n4.11. High-dimensional & Subspace Outliers\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==================================================================================================  ============================  =====  ============================  =======================================================================================================================================================================================================\nPaper Title                                                                                         Venue                         Year   Ref                           Materials\n==================================================================================================  ============================  =====  ============================  =======================================================================================================================================================================================================\nA survey on unsupervised outlier detection in high-dimensional numerical data                       Stat Anal Data Min            2012   [#Zimek2012A]_                `[HTML] <https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.11161>`_\nLearning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection  SIGKDD                        2018   [#Pang2018Learning]_          `[PDF] <https://arxiv.org/pdf/1806.04808.pdf>`_\nReverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection                          TKDE                          2015   [#Radovanovic2015Reverse]_    `[PDF] <https://ieeexplore.ieee.org/document/6948273>`_, `[SLIDES] <https://pdfs.semanticscholar.org/c8aa/832362422418287ff56793c780b425afa93f.pdf>`_\nOutlier detection for high-dimensional data                                                         Biometrika                    2015   [#Ro2015Outlier]_             `[PDF] <http://web.hku.hk/~gyin/materials/2015RoZouWangYinBiometrika.pdf>`_\n==================================================================================================  ============================  =====  ============================  =======================================================================================================================================================================================================\n\n\n4.12. Feature Selection in Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                                       Venue                         Year   Ref                           Materials\n================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nUnsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings            ICDM                          2016   [#Pang2016Unsupervised]_      `[PDF] <https://opus.lib.uts.edu.au/bitstream/10453/107356/4/DSFS_ICDM2016.pdf>`_\nLearning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection  IJCAI                         2017   [#Pang2017Learning]_          `[PDF] <https://www.ijcai.org/proceedings/2017/0360.pdf>`_\n================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.13. Time Series Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=====================================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                                                            Venue                         Year   Ref                           Materials\n=====================================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nOutlier detection for temporal data: A survey                                                                                          TKDE                          2014   [#Gupta2014Outlier]_          `[PDF] <https://www.microsoft.com/en-us/research/wp-content/uploads/2014/01/gupta14_tkde.pdf>`_\nDetecting spacecraft anomalies using lstms and nonparametric dynamic thresholding                                                      KDD                           2018   [#Hundman2018Detecting]_      `[PDF] <https://arxiv.org/pdf/1802.04431.pdf>`_, `[Code] <https://github.com/khundman/telemanom>`_\nTime-Series Anomaly Detection Service at Microsoft                                                                                     KDD                           2019   [#Ren2019Time]_               `[PDF] <https://arxiv.org/pdf/1906.03821.pdf>`_\nRevisiting Time Series Outlier Detection: Definitions and Benchmarks                                                                   NeurIPS                       2021   [#Lai2021Revisiting]_         `[PDF] <https://openreview.net/pdf?id=r8IvOsnHchr>`_, `[Code] <https://github.com/datamllab/tods/tree/benchmark>`_\nGraph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series                                                        ICLR                          2022   [#Dai2022Graph]_              `[PDF] <https://openreview.net/pdf?id=45L_dgP48Vd>`_, `[Code] <https://github.com/EnyanDai/GANF>`_\nDrift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection      NeurIPS                       2023   [#Wang2023Drift]_             `[PDF] <https://openreview.net/pdf?id=aW5bSuduF1>`_, `[Code] <https://github.com/ForestsKing/D3R>`_\n=====================================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.14. Graph & Network Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  =============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                          Year   Ref                           Materials\n=================================================================================================  =============================  =====  ============================  ==========================================================================================================================================================================\nGraph based anomaly detection and description: a survey                                            DMKD                           2015   [#Akoglu2015Graph]_           `[PDF] <https://arxiv.org/pdf/1404.4679.pdf>`_\nAnomaly detection in dynamic networks: a survey                                                    WIREs Computational Statistic  2015   [#Ranshous2015Anomaly]_       `[PDF] <https://onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1347>`_\nOutlier detection in graphs: On the impact of multiple graph models                                ComSIS                         2019   [#Campos2019Outlier]_         `[PDF] <http://www.comsis.org/pdf.php?id=wims-8671>`_\nA Comprehensive Survey on Graph Anomaly Detection with Deep Learning                               TKDE                           2021   [#Ma2021A]_                   `[PDF] <https://arxiv.org/pdf/2106.07178.pdf>`_\n=================================================================================================  =============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.15. Key Algorithms\n^^^^^^^^^^^^^^^^^^^\n\nAll these algorithms are available in `Python Outlier Detection (PyOD) <https://github.com/yzhao062/pyod>`_.\n\n====================  =================================================================================================  =================================  =====  ===========================  ==============================================================================================================================================================================================\nAbbreviation          Paper Title                                                                                        Venue                              Year   Ref                          Materials\n====================  =================================================================================================  =================================  =====  ===========================  ==============================================================================================================================================================================================\nkNN                   Efficient algorithms for mining outliers from large data sets                                      ACM SIGMOD Record                  2000   [#Ramaswamy2000Efficient]_   `[PDF] <https://webdocs.cs.ualberta.ca/~zaiane/pub/check/ramaswamy.pdf>`_\nKNN                   Fast outlier detection in high dimensional spaces                                                  PKDD                               2002   [#Angiulli2002Fast]_         `[PDF] <https://www.researchgate.net/profile/Clara_Pizzuti/publication/220699183_Fast_Outlier_Detection_in_High_Dimensional_Spaces/links/542ea6a60cf27e39fa9635c6.pdf>`_\nLOF                   LOF: identifying density-based local outliers                                                      ACM SIGMOD Record                  2000   [#Breunig2000LOF]_           `[PDF] <http://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf>`_\nIForest               Isolation forest                                                                                   ICDM                               2008   [#Liu2008Isolation]_         `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_\nOCSVM                 Estimating the support of a high-dimensional distribution                                          Neural Computation                 2001   [#Scholkopf2001Estimating]_  `[PDF] <http://users.cecs.anu.edu.au/~williams/papers/P132.pdf>`_\nAutoEncoder Ensemble  Outlier detection with autoencoder ensembles                                                       SDM                                2017   [#Chen2017Outlier]_          `[PDF] <http://saketsathe.net/downloads/autoencode.pdf>`_\nCOPOD                 COPOD: Copula-Based Outlier Detection                                                              ICDM                               2020   [#Li2020COPOD]_              `[PDF] <https://arxiv.org/abs/2009.09463>`_\nECOD                  Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions                   TKDE                               2022   [#Li2021ECOD]_               `[PDF] <https://arxiv.org/abs/2201.00382>`_\n====================  =================================================================================================  =================================  =====  ===========================  ==============================================================================================================================================================================================\n\n4.16. Overview & Survey Papers\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nPapers are sorted by the publication year.\n\n======================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                                             Venue                         Year   Ref                           Materials\n======================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nA survey of outlier detection methodologies                                                                             ARTIF INTELL REV              2004   [#Hodge2004A]_                `[PDF] <https://www-users.cs.york.ac.uk/vicky/myPapers/Hodge+Austin_OutlierDetection_AIRE381.pdf>`_\nAnomaly detection: A survey                                                                                             CSUR                          2009   [#Chandola2009Anomaly]_       `[PDF] <https://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf>`_\nA meta-analysis of the anomaly detection problem                                                                        Preprint                      2015   [#Emmott2015A]_               `[PDF] <https://arxiv.org/pdf/1503.01158.pdf>`_\nOn the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study                         DMKD                          2016   [#Campos2016On]_              `[HTML] <https://link.springer.com/article/10.1007/s10618-015-0444-8>`_, `[SLIDES] <https://imada.sdu.dk/~zimek/InvitedTalks/TUVienna-2016-05-18-outlier-evaluation.pdf>`_\nA comparative evaluation of unsupervised anomaly detection algorithms for multivariate data                             PLOS ONE                      2016   [#Goldstein2016A]_            `[PDF] <http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0152173&type=printable>`_\nA comparative evaluation of outlier detection algorithms: Experiments and analyses                                      Pattern Recognition           2018   [#Domingues2018A]_            `[PDF] <https://www.researchgate.net/publication/320025854_A_comparative_evaluation_of_outlier_detection_algorithms_Experiments_and_analyses>`_\nResearch Issues in Outlier Detection                                                                                    Book Chapter                  2019   [#Suri2019Research]_          `[HTML] <https://link.springer.com/chapter/10.1007/978-3-030-05127-3_3>`_\nQuantitative comparison of unsupervised anomaly detection algorithms for intrusion detection                            SAC                           2019   [#Falcao2019Quantitative]_    `[HTML] <https://dl.acm.org/citation.cfm?id=3297314>`_\nProgress in Outlier Detection Techniques: A Survey                                                                      IEEE Access                   2019   [#Wang2019Progress]_          `[PDF] <https://ieeexplore.ieee.org/iel7/6287639/8600701/08786096.pdf>`_\nDeep learning for anomaly detection: A survey                                                                           Preprint                      2019   [#Chalapathy2019Deep]_        `[PDF] <https://arxiv.org/pdf/1901.03407.pdf>`_\nAnomalous Instance Detection in Deep Learning: A Survey                                                                 Tech Report                   2020   [#Bulusu2020Deep]_            `[PDF] <https://arxiv.org/pdf/2003.06979.pdf>`_\nAnomaly detection in univariate time-series: A survey on the state-of-the-art                                           Preprint                      2020   [#Braei2020Anomaly]_          `[PDF] <https://arxiv.org/pdf/2004.00433.pdf>`_\nDeep Learning for Anomaly Detection: A Review                                                                           CSUR                          2021   [#Pang2020Deep]_              `[PDF] <https://arxiv.org/pdf/2007.02500.pdf>`_\nA Comprehensive Survey on Graph Anomaly Detection with Deep Learning                                                    TKDE                          2021   [#Ma2021A]_                   `[PDF] <https://arxiv.org/pdf/2106.07178.pdf>`_\nRevisiting Time Series Outlier Detection: Definitions and Benchmarks                                                    NeurIPS                       2021   [#Lai2021Revisiting]_         `[PDF] <https://openreview.net/pdf?id=r8IvOsnHchr>`_, `[Code] <https://github.com/datamllab/tods/tree/benchmark>`_\nA Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges      Preprint                      2021   [#Salehi2021A]_               `[PDF] <https://arxiv.org/pdf/2110.14051.pdf>`_\nSelf-Supervised Anomaly Detection: A Survey and Outlook                                                                 Preprint                      2022   [#Hojjati2022Self]_           `[PDF] <https://arxiv.org/pdf/2205.05173.pdf>`_\nWeakly supervised anomaly detection: A survey                                                                           Preprint                      2023   [#Jiang2023weakly]_           `[PDF] <https://arxiv.org/abs/2302.04549>`_, `[PDF] <https://github.com/yzhao062/wsad>`_\nAD-LLM: Benchmarking Large Language Models for Anomaly Detection                                                        Preprint                      2024   [#Yang2024ADLLM]_             `[PDF] <https://arxiv.org/abs/2412.11142>`_, `[Code] <https://github.com/USC-FORTIS/AD-LLM>`_\nLarge Language Models for Anomaly and Out-of-Distribution Detection: A Survey                                           Preprint                      2024   [#Xu2024LLMsurvey]_           `[PDF] <https://arxiv.org/abs/2409.01980>`_\n======================================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n4.17. Isolation-Based Methods\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                            Materials\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\nIsolation forest                                                                                   ICDM                          2008   [#Liu2008Isolation]_           `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_\nIsolation‐based anomaly detection using nearest‐neighbor ensembles                                  Computational Intelligence    2018   [#Bandaragoda2018Isolation]_   `[PDF] <https://www.researchgate.net/publication/322359651_Isolation-based_anomaly_detection_using_nearest-neighbor_ensembles_iNNE>`_, `[Code] <https://github.com/zhuye88/iNNE>`_\nExtended Isolation Forest                                                                          TKDE                          2019   [#Hariri2019Extended]_         `[PDF] <https://arxiv.org/pdf/1811.02141.pdf>`_, `[Code] <https://github.com/sahandha/eif>`_\nIsolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection                     KDD                           2020   [#Ting2020Isolation]_          `[PDF] <https://arxiv.org/pdf/2009.12196.pdf>`_, `[Code] <https://github.com/IsolationKernel/Codes/tree/main/IDK>`_\nDeep Isolation Forest for Anomaly Detection                                                        TKDE                          2023   [#Xu2023Deep]_                 `[PDF] <https://arxiv.org/abs/2206.06602>`_, `[Code] <https://github.com/xuhongzuo/deep-iforest>`_\n=================================================================================================  ============================  =====  =============================  ==============================================================================================================================================================================================\n\n\n4.18. Fairness and Bias in Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nA Framework for Determining the Fairness of Outlier Detection                                      ECAI                          2020   [#Davidson2020A]_             `[PDF] <https://web.cs.ucdavis.edu/~davidson/Publications/TR.pdf>`_\nFAIROD: Fairness-aware Outlier Detection                                                           AIES                          2021   [#Shekhar2021FAIROD]_         `[PDF] <https://arxiv.org/pdf/2012.03063.pdf>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n\n4.19. Outlier Detection Applications\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n========================    =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nField                       Paper Title                                                                                        Venue                         Year   Ref                           Materials\n========================    =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n**Security**                A survey of distance and similarity measures used within network intrusion anomaly detection       IEEE Commun. Surv. Tutor.     2015   [#WellerFahy2015A]_           `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6853338>`_\n**Security**                Anomaly-based network intrusion detection: Techniques, systems and challenges                      Computers & Security          2009   [#GarciaTeodoro2009Anomaly]_  `[PDF] <https://www2.cs.uh.edu/~acl/cs6397/Doc/2009-Elsevier-Anomaly-based%20network%20intrusion%20detection.pdf>`_\n**Finance**                 A survey of anomaly detection techniques in financial domain                                       Future Gener Comput Syst      2016   [#Ahmed2016A]_                `[PDF] <https://www.sciencedirect.com/science/article/abs/pii/S0167739X15000023>`_\n**Traffic**                 Outlier Detection in Urban Traffic Data                                                            WIMS                          2018   [#Djenouri2018Outlier]_       `[PDF] <http://dss.sdu.dk/assets/fpd-lof/outlier-detection-urban.pdf>`_\n**Social Media**            A survey on social media anomaly detection                                                         SIGKDD Explorations           2016   [#Yu2016A]_                   `[PDF] <https://arxiv.org/pdf/1601.01102.pdf>`_\n**Social Media**            GLAD: group anomaly detection in social media analysis                                             TKDD                          2015   [#Yu2015Glad]_                `[PDF] <https://arxiv.org/pdf/1410.1940.pdf>`_\n**Machine Failure**         Detecting the Onset of Machine Failure Using Anomaly Detection Methods                             DAWAK                         2019   [#Riazi2019Detecting]_        `[PDF] <https://webdocs.cs.ualberta.ca/~zaiane/postscript/DAWAK19.pdf>`_\n**Video Surveillance**      AnomalyNet: An anomaly detection network for video surveillance                                    TIFS                          2019   [#Zhou2019AnomalyNet]_        `[IEEE] <https://ieeexplore.ieee.org/document/8649753>`_, `Code <https://github.com/joeyzhouty/AnomalyNet>`_\n========================    =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.20. Outlier Detection in Other fields\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n============== =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nField          Paper Title                                                                                        Venue                         Year   Ref                           Materials\n============== =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n**Text**       Outlier detection for text data                                                                    SDM                           2017   [#Kannan2017Outlier]_         `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611974973.55>`_\n============== =================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.21. Interactive Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                        Venue                         Year   Ref                           Materials\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nLearning On-the-Job to Re-rank Anomalies from Top-1 Feedback                                       SDM                           2019   [#Lamba2019Learning]_         `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.69>`_\nInteractive anomaly detection on attributed networks                                               WSDM                          2019   [#Ding2019Interactive]_       `[PDF] <http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf>`_\neX2: a framework for interactive anomaly detection                                                 IUI Workshop                  2019   [#Arnaldo2019ex2]_            `[PDF] <http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf>`_\nTripartite Active Learning for Interactive Anomaly Discovery                                       IEEE Access                   2019   [#Zhu2019Tripartite]_         `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963>`_\n=================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n4.22. Active Anomaly Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nPaper Title                                                                                         Venue                         Year   Ref                           Materials\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\nActive learning for anomaly and rare-category detection                                             NeurIPS                       2005   [#Pelleg2005Active]_          `[PDF] <http://papers.nips.cc/paper/2554-active-learning-for-anomaly-and-rare-category-detection.pdf>`_\nOutlier detection by active learning                                                                SIGKDD                        2006   [#Abe2006Outlier]_            `[PDF] <https://www.researchgate.net/profile/Naoki_Abe2/publication/221653343_Outlier_detection_by_active_learning/links/5441464a0cf2e6f0c0f60abb.pdf>`_\nActive Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability                  Preprint                      2019   [#Das2019Active]_             `[PDF] <https://arxiv.org/pdf/1901.08930.pdf>`_\nMeta-AAD: Active Anomaly Detection with Deep Reinforcement Learning                                 ICDM                          2020   [#Zha2020Meta]_               `[PDF] <https://arxiv.org/pdf/2009.07415.pdf>`_\nA3: Activation Anomaly Analysis                                                                     ECML-PKDD                     2020   [#Sperl2021A3]_               `[PDF] <https://arxiv.org/pdf/2003.01801>`_, `[Code] <https://github.com/Fraunhofer-AISEC/A3>`_\n==================================================================================================  ============================  =====  ============================  ==========================================================================================================================================================================\n\n\n----\n\n.. _5-key-conferencesworkshopsjournals:\n\n5. 重要会议 / Workshop / 期刊\n-------------------------------------\n\n.. _51-conferences--workshops:\n\n5.1. 会议与 Workshop\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nKey data mining conference **deadlines**, **historical acceptance rates**, and more\ncan be found `data-mining-conferences <https://github.com/yzhao062/data-mining-conferences>`_.\n\n\n`ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) <http://www.kdd.org/conferences>`_. **Note**: SIGKDD usually has an Outlier Detection Workshop (ODD), see `ODD 2021 <https://oddworkshop.github.io/>`_.\n\n`ACM International Conference on Management of Data (SIGMOD) <https://sigmod.org/>`_\n\n`The Web Conference (WWW) <https://www2018.thewebconf.org/>`_\n\n`IEEE International Conference on Data Mining (ICDM) <https://icdm2024.org//>`_\n\n`SIAM International Conference on Data Mining (SDM) <https://www.siam.org/Conferences/CM/Main/sdm19>`_\n\n`IEEE International Conference on Data Engineering (ICDE) <https://icde2018.org/>`_\n\n`ACM InternationalConference on Information and Knowledge Management (CIKM) <http://www.cikmconference.org/>`_\n\n`ACM International Conference on Web Search and Data Mining (WSDM) <http://www.wsdm-conference.org/2018/>`_\n\n`The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) <http://www.ecmlpkdd2018.org/>`_\n\n`The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) <http://pakdd2019.medmeeting.org>`_\n\n.. _52-journals:\n\n5.2. 期刊\n^^^^^^^^^^^^^\n\n`ACM Transactions on Knowledge Discovery from Data (TKDD) <https://tkdd.acm.org/>`_\n\n`IEEE Transactions on Knowledge and Data Engineering (TKDE) <https://www.computer.org/web/tkde>`_\n\n`ACM SIGKDD Explorations Newsletter <http://www.kdd.org/explorations>`_\n\n`Data Mining and Knowledge Discovery <https://link.springer.com/journal/10618>`_\n\n`Knowledge and Information Systems (KAIS) <https://link.springer.com/journal/10115>`_\n\n----\n\nReferences\n----------\n\n.. 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[#Wang2020Deep] Wang, R., Nie, K., Chang, Y. J., Gong, X., Wang, T., Yang, Y., Long, B.,  2020. Deep Learning for Anomaly Detection. *Tutorial at KDD 2020*.\n\n.. [#WellerFahy2015A] Weller-Fahy, D.J., Borghetti, B.J. and Sodemann, A.A., 2015. A survey of distance and similarity measures used within network intrusion anomaly detection. *IEEE Communications Surveys & Tutorials*\\ , 17(1), pp.70-91.\n\n.. [#Xu2021Beyond] Xu, H., Wang, Y., Jian, S., Huang, Z., Wang, Y., Liu, N. and Li, F., 2021, April. Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network. In *Proceedings of the Web Conference* 2021 (pp. 1328-1339).\n\n.. [#Xu2023Deep] Xu, H., Pang, G., Wang, Y., Wang, Y., 2023. Deep Isolation Forest for Anomaly Detection. *IEEE Transactions on Knowledge and Data Engineering*. \n\n.. [#Xu2023Fascinating] Xu, H., Wang, Y., Wei, J., Jian, S., Li, Y., Liu, N., 2023. Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning. *International Conference on Machine Learning (ICML)*.\n\n.. [#Xu2024LLMsurvey] Xu, R. and Ding, K., 2024. Large language models for anomaly and out-of-distribution detection: A survey. arXiv preprint arXiv:2409.01980.\n\n.. [#Yang2024ADLLM] Yang, T., Nian, Y., Li, S., Xu, R., Li, Y., Li, J., Xiao, Z., Hu, X., Rossi, R., Ding, K., Hu, X. and Zhao, Y. \"AD-LLM: Benchmarking Large Language Models for Anomaly Detection.\" Findings of ACL, 2025.\n\n.. [#Yang2025ADAGENT] Yang, T., Liu, J., Siu, W., Wang, J., Qian, Z., Song, C., Cheng, C., Hu, X., and Zhao, Y. \"AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection.\" Findings of IJCNLP-AACL, 2025.\n\n.. [#Yoon2019NETS] Yoon, S., Lee, J. G., & Lee, B. S., 2019. NETS: extremely fast outlier detection from a data stream via set-based processing. Proceedings of the VLDB Endowment, 12(11), 1303-1315.\n\n.. [#Yoon2020STARE] Yoon, S., Lee, J. G., & Lee, B. S., 2020. Ultrafast local outlier detection from a data stream with stationary region skipping. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1181-1191)\n\n.. [#Yoon2021MDUAL] Yoon, S., Shin, Y., Lee, J. G., & Lee, B. S. (2021, June). Multiple dynamic outlier-detection from a data stream by exploiting duality of data and queries. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD).\n\n.. [#Yoon2022ARCUS] Yoon, S., Lee, Y., Lee, J.G. and Lee, B.S., 2022, August. Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2347-2357).\n\n.. [#Yu2015Glad] Yu, R., He, X. and Liu, Y., 2015. GLAD: group anomaly detection in social media analysis. *ACM Transactions on Knowledge Discovery from Data (TKDD)*\\ , 10(2), p.18.\n\n.. [#Yu2016A] Yu, R., Qiu, H., Wen, Z., Lin, C. and Liu, Y., 2016. A survey on social media anomaly detection. *ACM SIGKDD Explorations Newsletter*\\ , 18(1), pp.1-14.\n\n.. [#Yuan2024Trustworthy] Yuan, S., Xu, D. and Wu, X., 2024  Trustworthy Anomaly Detection. *Tutorial at SDM 2024*.\n\n.. [#Zha2020Meta] Zha, D., Lai, K.H., Wan, M. and Hu, X., 2020. Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning. *ICDM*.\n\n.. [#Zhao2018Xgbod] Zhao, Y. and Hryniewicki, M.K., 2018, July. XGBOD: improving supervised outlier detection with unsupervised representation learning. In *2018 International Joint Conference on Neural Networks (IJCNN)*. IEEE.\n\n.. [#Zhao2019LSCP] Zhao, Y., Nasrullah, Z., Hryniewicki, M.K. and Li, Z., 2019, May. LSCP: Locally selective combination in parallel outlier ensembles. In *Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)*, pp. 585-593. Society for Industrial and Applied Mathematics.\n\n.. [#Zhao2019PYOD] Zhao, Y., Nasrullah, Z. and Li, Z., PyOD: A Python Toolbox for Scalable Outlier Detection. *Journal of Machine Learning Research*, 20, pp.1-7.\n\n.. [#Zhao2020Automating] Zhao, Y., Rossi, R.A. and Akoglu, L., 2021. Automatic Unsupervised Outlier Model Selection. *Advances in Neural Information Processing Systems*.\n\n.. [#Zhao2021SUOD] Zhao, Y., Hu, X., Cheng, C., Wang, C., Wan, C., Wang, W., Yang, J., Bai, H., Li, Z., Xiao, C. and Wang, Y., 2021. SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection. *Proceedings of Machine Learning and Systems (MLSys)*.\n\n.. [#Zhao2021TOD] Zhao, Y., Chen, G.H. and Jia, Z., 2021. TOD: Tensor-based Outlier Detection. arXiv preprint arXiv:2110.14007.\n\n.. [#Zhang2025LogSAD] Zhang, J., Wang, G., Jin, Y. and Huang, D., 2025. Towards Training-free Anomaly Detection with Vision and Language Foundation Models. In *CVPR 2025*.\n\n.. [#Zhou2019AnomalyNet] Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y. and Goh, R.S.M., 2019. AnomalyNet: An anomaly detection network for video surveillance. *IEEE Transactions on Information Forensics and Security*.\n\n.. [#Zhou2021Feature] Zhou, Y., Song, X., Zhang, Y., Liu, F., Zhu, C., & Liu, L. (2021). Feature encoding with autoencoders for weakly supervised anomaly detection. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2454-2465.\n\n.. [#Zhu2019Tripartite] Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. *IEEE Access*.\n\n.. [#Zimek2012A] Zimek, A., Schubert, E. and Kriegel, H.P., 2012. A survey on unsupervised outlier detection in high‐dimensional numerical data. *Statistical Analysis and Data Mining: The ASA Data Science Journal*\\ , 5(5), pp.363-387.\n\n.. [#Zimek2014Ensembles] Zimek, A., Campello, R.J. and Sander, J., 2014. Ensembles for unsupervised outlier detection: challenges and research questions a position paper. *ACM Sigkdd Explorations Newsletter*\\ , 15(1), pp.11-22.\n\n.. [#Zong2018Deep] Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D. and Chen, H., 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. International Conference on Learning Representations (ICLR).\n\n.. [#Wang2023Drift] Wang, C., Zhuang, Z., Qi, Q., Wang, J., Wang, X., Sun, H., & Liao, J. (2023). Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection. Advances in Neural Information Processing Systems, 36.\n\n\n"
  },
  {
    "path": "download.py",
    "content": "#!/usr/bin/python\n\n\"\"\"\n    This script will download all papers/books and rename to proper name\n    if there is no copyright issue.\n\n    TODO: download resources by item number\n    TODO: add exception handler for downloader\n\"\"\"\nimport re\nimport pathlib\nimport urllib.request\n\n# initialize the log directory if it does not exist\npathlib.Path('resources').mkdir(parents=True, exist_ok=True)\n\nf = open('resource_urls\\\\papers.txt', 'r')\nfor line in f:\n    # print(line)\n    line_splits = line.split(' | ')\n\n    # remove all special char in file name\n    file_name = re.sub(r'[\\\\/*?:\"<>|]', \"\", line_splits[0])\n    # strip filename length in case it is too long\n    if len(file_name) > 255:\n        file_name = file_name[:255]\n    url = line_splits[1]\n\n    print('Downloading', file_name, 'from', url)\n    urllib.request.urlretrieve(url, \"resources\\\\\" + file_name + '.pdf')\n\nf.close()\n"
  },
  {
    "path": "resource_urls/papers.txt",
    "content": "Anomaly detection: A survey | https://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf\nA survey of outlier detection methodologies | https://www-users.cs.york.ac.uk/vicky/myPapers/Hodge+Austin_OutlierDetection_AIRE381.pdf\nA comparative evaluation of unsupervised anomaly detection algorithms for multivariate data | http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0152173&type=printable\nOutlier detection for temporal data: A survey | https://pdfs.semanticscholar.org/18d1/714870fb989f32b4311892e8765f00f7098f.pdf\nEnsembles for unsupervised outlier detection: challenges and research questions a position paper | http://www.kdd.org/exploration_files/V15-01-02-Zimek.pdf\nOutlier ensembles: position paper | https://pdfs.semanticscholar.org/841e/ce7c3812bbf799c99c84c064bbcf77916ba9.pdf"
  },
  {
    "path": "url_checker.py",
    "content": "#!/usr/bin/env python3\n\n\"\"\"\nRobust URL checker for README-style reStructuredText documents.\n\nFeatures:\n1. Extracts and cleans HTTP/HTTPS links from text.\n2. Removes common trailing punctuation from RST/Markdown contexts.\n3. Uses retries and a browser-like User-Agent.\n4. Falls back from HEAD to GET for servers that reject HEAD.\n5. Checks links concurrently and prints a final summary.\n\nUsage:\n    python url_checker.py\n    python url_checker.py --file README_CN.rst --timeout 8 --workers 20\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport re\nfrom concurrent.futures import ThreadPoolExecutor, as_completed\nfrom dataclasses import dataclass\nfrom typing import Iterable\nfrom urllib.parse import urlsplit, urlunsplit\n\nimport requests\nfrom requests import Session\nfrom requests.adapters import HTTPAdapter\nfrom urllib3.util import Retry\n\nTRAILING_PUNCT = set('`\">),.;:_]')\nDEFAULT_TIMEOUT = 8\nDEFAULT_WORKERS = 16\n\n\n@dataclass(frozen=True)\nclass CheckResult:\n    url: str\n    ok: bool\n    status_code: int | None\n    method: str\n    detail: str\n\n\ndef parse_args() -> argparse.Namespace:\n    parser = argparse.ArgumentParser(description=\"Check URLs from an RST file.\")\n    parser.add_argument(\n        \"--file\",\n        default=\"README.rst\",\n        help=\"RST/Markdown file to parse (default: README.rst).\",\n    )\n    parser.add_argument(\n        \"--timeout\",\n        type=int,\n        default=DEFAULT_TIMEOUT,\n        help=f\"Request timeout in seconds (default: {DEFAULT_TIMEOUT}).\",\n    )\n    parser.add_argument(\n        \"--workers\",\n        type=int,\n        default=DEFAULT_WORKERS,\n        help=f\"Number of parallel workers (default: {DEFAULT_WORKERS}).\",\n    )\n    parser.add_argument(\n        \"--show-ok\",\n        action=\"store_true\",\n        help=\"Also print successful URLs.\",\n    )\n    return parser.parse_args()\n\n\ndef clean_url(raw_url: str) -> str:\n    url = raw_url.strip()\n    while url and url[-1] in TRAILING_PUNCT:\n        url = url[:-1]\n\n    # Rebuild URL to normalize casing for host and remove fragment-only noise.\n    parts = urlsplit(url)\n    netloc = parts.netloc.lower()\n    fragment = \"\"\n    cleaned = urlunsplit((parts.scheme, netloc, parts.path, parts.query, fragment))\n    return cleaned\n\n\ndef extract_urls(content: str) -> list[str]:\n    # Match until whitespace; cleanup handles RST trailing characters.\n    raw_urls = re.findall(r\"https?://\\S+\", content)\n    cleaned = set()\n    for raw_url in raw_urls:\n        normalized = clean_url(raw_url)\n        if normalized:\n            cleaned.add(normalized)\n    return sorted(cleaned)\n\n\ndef build_session() -> Session:\n    retry = Retry(\n        total=2,\n        connect=2,\n        read=2,\n        backoff_factor=0.5,\n        status_forcelist=(429, 500, 502, 503, 504),\n        allowed_methods=frozenset({\"HEAD\", \"GET\"}),\n        raise_on_status=False,\n    )\n    adapter = HTTPAdapter(max_retries=retry)\n\n    session = requests.Session()\n    session.mount(\"http://\", adapter)\n    session.mount(\"https://\", adapter)\n    session.headers.update(\n        {\n            \"User-Agent\": (\n                \"Mozilla/5.0 (compatible; URLChecker/2.0; +https://github.com/yzhao062/\"\n                \"anomaly-detection-resources)\"\n            )\n        }\n    )\n    return session\n\n\ndef should_fallback_to_get(status_code: int) -> bool:\n    return status_code in (403, 405, 406, 429, 500, 501, 502, 503)\n\n\ndef check_one_url(url: str, timeout: int) -> CheckResult:\n    session = build_session()\n    try:\n        head_resp = session.head(url, allow_redirects=True, timeout=timeout)\n        if head_resp.status_code < 400:\n            return CheckResult(url, True, head_resp.status_code, \"HEAD\", \"OK\")\n\n        if should_fallback_to_get(head_resp.status_code):\n            get_resp = session.get(url, allow_redirects=True, timeout=timeout, stream=True)\n            # Avoid downloading full body.\n            get_resp.close()\n            if get_resp.status_code < 400:\n                return CheckResult(url, True, get_resp.status_code, \"GET\", \"OK (fallback)\")\n            return CheckResult(\n                url,\n                False,\n                get_resp.status_code,\n                \"GET\",\n                f\"Fallback failed after HEAD {head_resp.status_code}\",\n            )\n\n        return CheckResult(url, False, head_resp.status_code, \"HEAD\", \"HTTP error\")\n    except requests.RequestException as exc:\n        return CheckResult(url, False, None, \"HEAD/GET\", f\"RequestException: {exc}\")\n    finally:\n        session.close()\n\n\ndef check_all(urls: Iterable[str], timeout: int, workers: int) -> list[CheckResult]:\n    results: list[CheckResult] = []\n    with ThreadPoolExecutor(max_workers=workers) as executor:\n        futures = {executor.submit(check_one_url, url, timeout): url for url in urls}\n        for future in as_completed(futures):\n            results.append(future.result())\n    return sorted(results, key=lambda r: r.url)\n\n\ndef main() -> int:\n    args = parse_args()\n    try:\n        with open(args.file, \"r\", encoding=\"utf-8\") as handle:\n            content = handle.read()\n    except FileNotFoundError:\n        print(f\"Error: file not found: {args.file}\")\n        return 2\n\n    urls = extract_urls(content)\n    if not urls:\n        print(f\"No URLs found in {args.file}.\")\n        return 0\n\n    print(f\"Found {len(urls)} unique URLs in {args.file}. Checking...\")\n    results = check_all(urls, timeout=args.timeout, workers=max(1, args.workers))\n\n    ok_count = 0\n    fail_count = 0\n    for result in results:\n        if result.ok:\n            ok_count += 1\n            if args.show_ok:\n                print(f\"[OK]   {result.url} [{result.method} {result.status_code}]\")\n            continue\n        fail_count += 1\n        status = result.status_code if result.status_code is not None else \"N/A\"\n        print(f\"[FAIL] {result.url} [{result.method} {status}] {result.detail}\")\n\n    print()\n    print(f\"Summary: total={len(results)} ok={ok_count} fail={fail_count}\")\n    return 1 if fail_count else 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n"
  }
]