{"id":13400134,"url":"https://github.com/yzhao062/anomaly-detection-resources","last_synced_at":"2025-10-07T08:32:16.621Z","repository":{"id":37445331,"uuid":"133717148","full_name":"yzhao062/anomaly-detection-resources","owner":"yzhao062","description":"Anomaly detection related books, papers, videos, and 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Applications","Papers","异常检测","Anomaly Detection","Others","Table of Contents","3.2 Github Repositories","acknowledgments","📝 More Awesome Lists","Thanks","Awesome Computer Vision","📚 Learning \u0026 Resources"],"sub_categories":["Windows Manager","Uncategorized","Anomaly Detection","Outiler","3.2.2 *Paper list*","secret sharing","Miscellaneous"],"readme":"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 \u003chttps://en.wikipedia.org/wiki/Anomaly_detection\u003e`_\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 \u0026 Academic Papers \n#. Online Courses and Videos\n#. Outlier Datasets\n#. Open-source and Commercial Libraries/Toolkits\n#. Key Conferences \u0026 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] \u003chttps://github.com/yzhao062\u003e`_ and\n`[outlier detection papers] \u003chttps://scholar.google.com/citations?user=zoGDYsoAAAAJ\u0026hl=en\u003e`_ useful,\nespecially `PyOD library \u003chttps://github.com/yzhao062/pyod\u003e`_, `ADBench benchmark \u003chttps://github.com/Minqi824/ADBench\u003e`_,\nand `AD-LLM: benchmarking LLMs for AD \u003chttps://github.com/USC-FORTIS/AD-LLM\u003e`_.\n\n----\n\nTable of Contents\n-----------------\n\n\n* `1. Books \u0026 Tutorials \u0026 Benchmarks \u003c#1-books--tutorials--benchmarks\u003e`_\n\n * `1.1. Books \u003c#11-books\u003e`_\n * `1.2. Tutorials \u003c#12-tutorials\u003e`_\n * `1.3. Benchmarks \u003c#13-benchmarks\u003e`_\n\n* `2. Courses/Seminars/Videos \u003c#2-coursesseminarsvideos\u003e`_\n* `3. Toolbox \u0026 Datasets \u003c#3-toolbox--datasets\u003e`_\n\n * `3.1. Multivariate data outlier detection \u003c#31-multivariate-data\u003e`_\n * `3.2. Time series outlier detection \u003c#32-time-series-outlier-detection\u003e`_\n * `3.3. Graph Outlier Detection \u003c#33-graph-outlier-detection\u003e`_\n * `3.4. Real-time Elasticsearch \u003c#34-real-time-elasticsearch\u003e`_\n * `3.5. Datasets \u003c#35-datasets\u003e`_\n\n* `4. Papers \u003c#4-papers\u003e`_\n\n * `4.1. Overview \u0026 Survey Papers \u003c#41-overview--survey-papers\u003e`_\n * `4.2. Key Algorithms \u003c#42-key-algorithms\u003e`_\n * `4.3. Graph \u0026 Network Outlier Detection \u003c#43-graph--network-outlier-detection\u003e`_\n * `4.4. Time Series Outlier Detection \u003c#44-time-series-outlier-detection\u003e`_\n * `4.5. Feature Selection in Outlier Detection \u003c#45-feature-selection-in-outlier-detection\u003e`_\n * `4.6. High-dimensional \u0026 Subspace Outliers \u003c#46-high-dimensional--subspace-outliers\u003e`_\n * `4.7. Outlier Ensembles \u003c#47-outlier-ensembles\u003e`_\n * `4.8. Outlier Detection in Evolving Data \u003c#48-outlier-detection-in-evolving-data\u003e`_\n * `4.9. Representation Learning in Outlier Detection \u003c#49-representation-learning-in-outlier-detection\u003e`_\n * `4.10. Interpretability \u003c#410-interpretability\u003e`_\n * `4.11. Outlier Detection with Neural Networks \u003c#411-outlier-detection-with-neural-networks\u003e`_\n * `4.12. Active Anomaly Detection \u003c#412-active-anomaly-detection\u003e`_\n * `4.13. Interactive Outlier Detection \u003c#413-interactive-outlier-detection\u003e`_\n * `4.14. Outlier Detection in Other fields \u003c#414-outlier-detection-in-other-fields\u003e`_\n * `4.15. Outlier Detection Applications \u003c#415-outlier-detection-applications\u003e`_\n * `4.16. Automated Outlier Detection \u003c#416-automated-outlier-detection\u003e`_\n * `4.17. Machine Learning Systems for Outlier Detection \u003c#417-machine-learning-systems-for-outlier-detection\u003e`_\n * `4.18. Fairness and Bias in Outlier Detection \u003c#418-fairness-and-bias-in-outlier-detection\u003e`_\n * `4.19. Isolation-based Methods \u003c#419-isolation-based-methods\u003e`_\n * `4.20. Weakly-supervised Methods \u003c#420-weakly-supervised-methods\u003e`_\n * `4.21. Emerging and Interesting Topics \u003c#420-emerging-and-interesting-topics\u003e`_\n\n* `5. Key Conferences/Workshops/Journals \u003c#5-key-conferencesworkshopsjournals\u003e`_\n\n * `5.1. Conferences \u0026 Workshops \u003c#51-conferences--workshops\u003e`_\n * `5.2. Journals \u003c#52-journals\u003e`_\n\n\n----\n\n\n1. Books \u0026 Tutorials \u0026 Benchmarks\n---------------------------------\n\n1.1. Books\n^^^^^^^^^^\n\n`Outlier Analysis \u003chttps://link.springer.com/book/10.1007/978-3-319-47578-3\u003e`_ \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] \u003chttp://charuaggarwal.net/outlierbook.pdf\u003e`_\n\n`Outlier Ensembles: An Introduction \u003chttps://www.springer.com/gp/book/9783319547640\u003e`_ \nby Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier analysis.\n\n`Data Mining: Concepts and Techniques (3rd) \u003chttps://www.elsevier.com/books/data-mining-concepts-and-techniques/han/978-0-12-381479-1\u003e`_ \nby Jiawei Han and Micheline Kamber and Jian Pei: Chapter 12 discusses outlier detection with many key points. `[Google Search] \u003chttps://www.google.ca/search?\u0026q=data+mining+jiawei+han\u0026oq=data+ming+jiawei\u003e`_\n\n1.2. Tutorials\n^^^^^^^^^^^^^^\n\n===================================================== ============================================ ===== ============================ ==========================================================================================================================================================================\nTutorial Title Venue Year Ref Materials\n===================================================== ============================================ ===== ============================ ==========================================================================================================================================================================\nData mining for anomaly detection PKDD 2008 [#Lazarevic2008Data]_ `[Video] \u003chttp://videolectures.net/ecmlpkdd08_lazarevic_dmfa/\u003e`_\nOutlier detection techniques ACM SIGKDD 2010 [#Kriegel2010Outlier]_ `[PDF] \u003chttps://imada.sdu.dk/~zimek/publications/KDD2010/kdd10-outlier-tutorial.pdf\u003e`_\nWhich Outlier Detector Should I use? ICDM 2018 [#Ting2018Which]_ `[PDF] \u003chttps://ieeexplore.ieee.org/document/8594824\u003e`_\nDeep Learning for Anomaly Detection KDD 2020 [#Wang2020Deep]_ `[HTML] \u003chttps://sites.google.com/view/kdd2020deepeye/home\u003e`_, `[Video] \u003chttps://www.youtube.com/watch?v=Fn0qDbKL3UI\u0026list=PLn0nrSd4xjja7AD3aY9Jxmr820gx59EQC\u0026index=66\u003e`_\nDeep Learning for Anomaly Detection WSDM 2021 [#Pang2021Deep]_ `[HTML] \u003chttps://sites.google.com/site/gspangsite/wsdm21_tutorial\u003e`_\nToward Explainable Deep Anomaly Detection KDD 2021 [#Pang2021Toward]_ `[HTML] \u003chttps://sites.google.com/site/gspangsite/kdd21_tutorial\u003e`_\nRecent Advances in Anomaly Detection CVPR 2023 [#Pang2023recent]_ `[HTML] \u003chttps://sites.google.com/view/cvpr2023-tutorial-on-ad/\u003e`_, `[Video] \u003chttps://www.youtube.com/watch?v=dXxrzWeybBo\u0026feature=youtu.be\u003e`_\nTrustworthy Anomaly Detection SDM 2024 [#Yuan2024Trustworthy]_ `[HTML] \u003chttps://yuan.shuhan.org/talks/SDM24/\u003e`_\n===================================================== ============================================ ===== ============================ ==========================================================================================================================================================================\n\n\n1.3. 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============= ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\nTime-series Revisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [#Lai2021Revisiting]_ `[PDF] \u003chttps://openreview.net/pdf?id=r8IvOsnHchr\u003e`_, `[Code] \u003chttps://github.com/datamllab/tods/tree/benchmark\u003e`_\nGraph Benchmarking Node Outlier Detection on Graphs NeurIPS 2022 [#Liu2022Benchmarking]_ `[PDF] \u003chttps://arxiv.org/abs/2206.10071\u003e`_, `[Code] \u003chttps://github.com/pygod-team/pygod/tree/main/benchmark\u003e`_\nGraph GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection NeurIPS 2023 [#Tang2023GADBench]_ `[PDF] \u003chttps://arxiv.org/abs/2306.12251\u003e`_, `[Code] \u003chttps://github.com/squareRoot3/GADBench\u003e`_\nTabular ADBench: Anomaly Detection Benchmark NeurIPS 2022 [#Han2022Adbench]_ `[PDF] \u003chttps://arxiv.org/abs/2206.09426\u003e`_, `[Code] \u003chttps://github.com/Minqi824/ADBench\u003e`_\nTabular ADGym: Design Choices for Deep Anomaly Detection NeurIPS 2023 [#Jiang2023adgym]_ `[PDF] \u003chttps://arxiv.org/abs/2309.15376\u003e`_, `[Code] \u003chttps://github.com/Minqi824/ADGym\u003e`_\nNLP NLP-ADBench: NLP Anomaly Detection Benchmark Preprint 2024 [#Li2024NLPADBench]_ `[PDF] \u003chttps://arxiv.org/abs/2412.04784\u003e`_, `[Code] \u003chttps://github.com/USC-FORTIS/NLP-ADBench\u003e`_\nNLP AD-LLM: Benchmarking Large Language Models for Anomaly Detection Preprint 2024 [#Yang2024ADLLM]_ `[PDF] \u003chttps://arxiv.org/abs/2412.11142\u003e`_, `[Code] \u003chttps://github.com/USC-FORTIS/AD-LLM\u003e`_\n============= ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n----\n\n2. Courses/Seminars/Videos\n--------------------------\n\n**Coursera Introduction to Anomaly Detection (by IBM)**\\ :\n`[See Video] \u003chttps://www.coursera.org/learn/ai/lecture/ASPv0/introduction-to-anomaly-detection\u003e`_\n\n**Get started with the Anomaly Detection API (by IBM)**\\ :\n`[See Website] \u003chttps://developer.ibm.com/learningpaths/get-started-anomaly-detection-api/\u003e`_\n\n**Practical Anomaly Detection by appliedAI Institute**\\:\n`[See Website] \u003chttps://transferlab.ai/trainings/practical-anomaly-detection/\u003e`_, `[See Video] \u003chttps://www.youtube.com/watch?v=sEoMIDARpJ0\u0026list=PLz6xKPm1Bnd6cDDgct3MDhNWJuPXzsmyW\u003e`_, `[See GitHub] \u003chttps://github.com/aai-institute/tfl-training-practical-anomaly-detection\u003e`_\n\n**Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic**\\ :\n`[See Video] \u003chttps://www.coursera.org/learn/real-time-cyber-threat-detection\u003e`_\n\n**Coursera Machine Learning by Andrew Ng also partly covers the topic**\\ :\n\n\n* `Anomaly Detection vs. Supervised Learning \u003chttps://www.coursera.org/learn/machine-learning/lecture/Rkc5x/anomaly-detection-vs-supervised-learning\u003e`_\n* `Developing and Evaluating an Anomaly Detection System \u003chttps://www.coursera.org/learn/machine-learning/lecture/Mwrni/developing-and-evaluating-an-anomaly-detection-system\u003e`_\n\n**Udemy Outlier Detection Algorithms in Data Mining and Data Science**\\ :\n`[See Video] \u003chttps://www.udemy.com/outlier-detection-techniques/\u003e`_\n\n**Stanford Data Mining for Cyber Security** also covers part of anomaly detection techniques\\ :\n`[See Video] \u003chttp://web.stanford.edu/class/cs259d/\u003e`_\n\n----\n\n3. Toolbox \u0026 Datasets\n---------------------\n\n[**Python**] `OpenAD \u003chttps://github.com/USC-FORTIS/OpenAD\u003e`_: OpenAD 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) \u003chttps://github.com/yzhao062/pyod\u003e`_\\ : 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) \u003chttps://github.com/yzhao062/pytod\u003e`_: A general GPU-accelerated framework for outlier detection.\n\n[**Python**] `Python Streaming Anomaly Detection (PySAD) \u003chttps://github.com/selimfirat/pysad\u003e`_\\ : 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 \u003chttp://scikit-learn.org/stable/modules/outlier_detection.html\u003e`_. It supports some popular algorithms like LOF, Isolation Forest, and One-class SVM.\n\n[**Python**] `Scalable Unsupervised Outlier Detection (SUOD) \u003chttps://github.com/yzhao062/suod\u003e`_\\ : 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 \u003chttps://github.com/OutlierDetectionJL/OutlierDetection.jl\u003e`_\\ : 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 \u003chttps://elki-project.github.io/\u003e`_\\ :\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 \u003chttps://github.com/Markus-Go/rapidminer-anomalydetection\u003e`_\\ : 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 \u003chttps://github.com/pridiltal/ctv-AnomalyDetection\u003e`_\\ : This CRAN task view contains a list of packages that can be used for anomaly detection with R.\n\n[**R**] `outliers package \u003chttps://cran.r-project.org/web/packages/outliers/index.html\u003e`_\\ : A collection of some tests commonly used for identifying outliers in R.\n\n[**Matlab**] `Anomaly Detection Toolbox - Beta \u003chttp://dsmi-lab-ntust.github.io/AnomalyDetectionToolbox/\u003e`_\\ : A collection of popular outlier detection algorithms in Matlab.\n\n\n3.2. Time Series Outlier Detection\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n[**Python**] `TODS \u003chttps://github.com/datamllab/tods\u003e`_\\ : TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.\n\n[**Python**] `skyline \u003chttps://github.com/earthgecko/skyline\u003e`_\\ : Skyline is a near real time anomaly detection system.\n\n[**Python**] `banpei \u003chttps://github.com/tsurubee/banpei\u003e`_\\ : Banpei is a Python package of the anomaly detection.\n\n[**Python**] `telemanom \u003chttps://github.com/khundman/telemanom\u003e`_\\ : A framework for using LSTMs to detect anomalies in multivariate time series data.\n\n[**Python**] `DeepADoTS \u003chttps://github.com/KDD-OpenSource/DeepADoTS\u003e`_\\ : 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 \u003chttps://github.com/numenta/NAB\u003e`_\\ : NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications.\n\n[**Python**] `CueObserve \u003chttps://github.com/cuebook/CueObserve\u003e`_\\ : Anomaly detection on SQL data warehouses and databases.\n\n[**Python**] `Chaos Genius \u003chttps://github.com/chaos-genius/chaos_genius\u003e`_\\ : ML powered analytics engine for outlier/anomaly detection and root cause analysis.\n\n[**R**] `CRAN Task View: Anomaly Detection with R \u003chttps://github.com/pridiltal/ctv-AnomalyDetection\u003e`_\\ : This CRAN task view contains a list of packages that can be used for anomaly detection with R.\n\n[**R**] `AnomalyDetection \u003chttps://github.com/twitter/AnomalyDetection\u003e`_\\ : 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 \u003chttps://cran.r-project.org/web/packages/anomalize/\u003e`_\\ : 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) \u003chttps://github.com/pygod-team/pygod/\u003e`_\\ : 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 \u003chttps://github.com/aws/random-cut-forest-by-aws\u003e`_\\ : A machine learning-based anomaly detection plugins for Open Distro for Elasticsearch. See `Real Time Anomaly Detection in Open Distro for Elasticsearch \u003chttps://opendistro.github.io/for-elasticsearch/blog/odfe-updates/2019/11/real-time-anomaly-detection-in-open-distro-for-elasticsearch/\u003e`_.\n\n[**Python**] `datastream.io \u003chttps://github.com/MentatInnovations/datastream.io\u003e`_\\ : 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\n4.1. Overview \u0026 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] \u003chttps://www-users.cs.york.ac.uk/vicky/myPapers/Hodge+Austin_OutlierDetection_AIRE381.pdf\u003e`_\nAnomaly detection: A survey CSUR 2009 [#Chandola2009Anomaly]_ `[PDF] \u003chttps://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf\u003e`_\nA meta-analysis of the anomaly detection problem Preprint 2015 [#Emmott2015A]_ `[PDF] \u003chttps://arxiv.org/pdf/1503.01158.pdf\u003e`_\nOn the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study DMKD 2016 [#Campos2016On]_ `[HTML] \u003chttps://link.springer.com/article/10.1007/s10618-015-0444-8\u003e`_, `[SLIDES] \u003chttps://imada.sdu.dk/~zimek/InvitedTalks/TUVienna-2016-05-18-outlier-evaluation.pdf\u003e`_\nA comparative evaluation of unsupervised anomaly detection algorithms for multivariate data PLOS ONE 2016 [#Goldstein2016A]_ `[PDF] \u003chttp://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0152173\u0026type=printable\u003e`_\nA comparative evaluation of outlier detection algorithms: Experiments and analyses Pattern Recognition 2018 [#Domingues2018A]_ `[PDF] \u003chttps://www.researchgate.net/publication/320025854_A_comparative_evaluation_of_outlier_detection_algorithms_Experiments_and_analyses\u003e`_\nResearch Issues in Outlier Detection Book Chapter 2019 [#Suri2019Research]_ `[HTML] \u003chttps://link.springer.com/chapter/10.1007/978-3-030-05127-3_3\u003e`_\nQuantitative comparison of unsupervised anomaly detection algorithms for intrusion detection SAC 2019 [#Falcao2019Quantitative]_ `[HTML] \u003chttps://dl.acm.org/citation.cfm?id=3297314\u003e`_\nProgress in Outlier Detection Techniques: A Survey IEEE Access 2019 [#Wang2019Progress]_ `[PDF] \u003chttps://ieeexplore.ieee.org/iel7/6287639/8600701/08786096.pdf\u003e`_\nDeep learning for anomaly detection: A survey Preprint 2019 [#Chalapathy2019Deep]_ `[PDF] \u003chttps://arxiv.org/pdf/1901.03407.pdf\u003e`_\nAnomalous Instance Detection in Deep Learning: A Survey Tech Report 2020 [#Bulusu2020Deep]_ `[PDF] \u003chttps://arxiv.org/pdf/2003.06979.pdf\u003e`_\nAnomaly detection in univariate time-series: A survey on the state-of-the-art Preprint 2020 [#Braei2020Anomaly]_ `[PDF] \u003chttps://arxiv.org/pdf/2004.00433.pdf\u003e`_\nDeep Learning for Anomaly Detection: A Review CSUR 2021 [#Pang2020Deep]_ `[PDF] \u003chttps://arxiv.org/pdf/2007.02500.pdf\u003e`_\nA Comprehensive Survey on Graph Anomaly Detection with Deep Learning TKDE 2021 [#Ma2021A]_ `[PDF] \u003chttps://arxiv.org/pdf/2106.07178.pdf\u003e`_\nRevisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [#Lai2021Revisiting]_ `[PDF] \u003chttps://openreview.net/pdf?id=r8IvOsnHchr\u003e`_, `[Code] \u003chttps://github.com/datamllab/tods/tree/benchmark\u003e`_\nA Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges Preprint 2021 [#Salehi2021A]_ `[PDF] \u003chttps://arxiv.org/pdf/2110.14051.pdf\u003e`_\nSelf-Supervised Anomaly Detection: A Survey and Outlook Preprint 2022 [#Hojjati2022Self]_ `[PDF] \u003chttps://arxiv.org/pdf/2205.05173.pdf\u003e`_\nWeakly supervised anomaly detection: A survey Preprint 2023 [#Jiang2023weakly]_ `[PDF] \u003chttps://arxiv.org/abs/2302.04549\u003e`_, `[PDF] \u003chttps://github.com/yzhao062/wsad\u003e`_\nAD-LLM: Benchmarking Large Language Models for Anomaly Detection Preprint 2024 [#Yang2024ADLLM]_ `[PDF] \u003chttps://arxiv.org/abs/2412.11142\u003e`_, `[Code] \u003chttps://github.com/USC-FORTIS/AD-LLM\u003e`_\nLarge Language Models for Anomaly and Out-of-Distribution Detection: A Survey Preprint 2024 [#Xu2024LLMsurvey]_ `[PDF] \u003chttps://arxiv.org/abs/2409.01980\u003e`_\n====================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================\n\n4.2. Key Algorithms\n^^^^^^^^^^^^^^^^^^^\n\nAll these algorithms are available in `Python Outlier Detection (PyOD) \u003chttps://github.com/yzhao062/pyod\u003e`_.\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] \u003chttps://webdocs.cs.ualberta.ca/~zaiane/pub/check/ramaswamy.pdf\u003e`_\nKNN Fast outlier detection in high dimensional spaces PKDD 2002 [#Angiulli2002Fast]_ `[PDF] \u003chttps://www.researchgate.net/profile/Clara_Pizzuti/publication/220699183_Fast_Outlier_Detection_in_High_Dimensional_Spaces/links/542ea6a60cf27e39fa9635c6.pdf\u003e`_\nLOF LOF: identifying density-based local outliers ACM SIGMOD Record 2000 [#Breunig2000LOF]_ `[PDF] \u003chttp://www.dbs.ifi.lmu.de/Publikationen/Papers/LOF.pdf\u003e`_\nIForest Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] \u003chttps://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf\u003e`_\nOCSVM Estimating the support of a high-dimensional distribution Neural Computation 2001 [#Scholkopf2001Estimating]_ `[PDF] \u003chttp://users.cecs.anu.edu.au/~williams/papers/P132.pdf\u003e`_\nAutoEncoder Ensemble Outlier detection with autoencoder ensembles SDM 2017 [#Chen2017Outlier]_ `[PDF] \u003chttp://saketsathe.net/downloads/autoencode.pdf\u003e`_\nCOPOD COPOD: Copula-Based Outlier Detection ICDM 2020 [#Li2020COPOD]_ `[PDF] \u003chttps://arxiv.org/abs/2009.09463\u003e`_\nECOD Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions TKDE 2022 [#Li2021ECOD]_ `[PDF] \u003chttps://arxiv.org/abs/2201.00382\u003e`_\n==================== ================================================================================================= ================================= ===== =========================== ==============================================================================================================================================================================================\n\n4.3. Graph \u0026 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] \u003chttps://arxiv.org/pdf/1404.4679.pdf\u003e`_\nAnomaly detection in dynamic networks: a survey WIREs Computational Statistic 2015 [#Ranshous2015Anomaly]_ `[PDF] \u003chttps://onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1347\u003e`_\nOutlier detection in graphs: On the impact of multiple graph models ComSIS 2019 [#Campos2019Outlier]_ `[PDF] \u003chttp://www.comsis.org/pdf.php?id=wims-8671\u003e`_\nA Comprehensive Survey on Graph Anomaly Detection with Deep Learning TKDE 2021 [#Ma2021A]_ `[PDF] \u003chttps://arxiv.org/pdf/2106.07178.pdf\u003e`_\n================================================================================================= ============================= ===== ============================ ==========================================================================================================================================================================\n\n\n4.4. 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] \u003chttps://www.microsoft.com/en-us/research/wp-content/uploads/2014/01/gupta14_tkde.pdf\u003e`_\nDetecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [#Hundman2018Detecting]_ `[PDF] \u003chttps://arxiv.org/pdf/1802.04431.pdf\u003e`_, `[Code] \u003chttps://github.com/khundman/telemanom\u003e`_\nTime-Series Anomaly Detection Service at Microsoft KDD 2019 [#Ren2019Time]_ `[PDF] \u003chttps://arxiv.org/pdf/1906.03821.pdf\u003e`_\nRevisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [#Lai2021Revisiting]_ `[PDF] \u003chttps://openreview.net/pdf?id=r8IvOsnHchr\u003e`_, `[Code] \u003chttps://github.com/datamllab/tods/tree/benchmark\u003e`_\nGraph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series ICLR 2022 [#Dai2022Graph]_ `[PDF] \u003chttps://openreview.net/pdf?id=45L_dgP48Vd\u003e`_, `[Code] \u003chttps://github.com/EnyanDai/GANF\u003e`_\nDrift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection NeurIPS 2023 [#Wang2023Drift]_ `[PDF] \u003chttps://openreview.net/pdf?id=aW5bSuduF1\u003e`_, `[Code] \u003chttps://github.com/ForestsKing/D3R\u003e`_\n===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.5. 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] \u003chttps://opus.lib.uts.edu.au/bitstream/10453/107356/4/DSFS_ICDM2016.pdf\u003e`_\nLearning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection IJCAI 2017 [#Pang2017Learning]_ `[PDF] \u003chttps://www.ijcai.org/proceedings/2017/0360.pdf\u003e`_\n================================================================================================================ ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.6. High-dimensional \u0026 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] \u003chttps://onlinelibrary.wiley.com/doi/abs/10.1002/sam.11161\u003e`_\nLearning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ `[PDF] \u003chttps://arxiv.org/pdf/1806.04808.pdf\u003e`_\nReverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection TKDE 2015 [#Radovanovic2015Reverse]_ `[PDF] \u003chttps://ieeexplore.ieee.org/document/6948273\u003e`_, `[SLIDES] \u003chttps://pdfs.semanticscholar.org/c8aa/832362422418287ff56793c780b425afa93f.pdf\u003e`_\nOutlier detection for high-dimensional data Biometrika 2015 [#Ro2015Outlier]_ `[PDF] \u003chttp://web.hku.hk/~gyin/materials/2015RoZouWangYinBiometrika.pdf\u003e`_\n================================================================================================== ============================ ===== ============================ =======================================================================================================================================================================================================\n\n\n4.7. Outlier Ensembles\n^^^^^^^^^^^^^^^^^^^^^^\n\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\nPaper Title Venue Year Ref Materials\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\nOutlier ensembles: position paper SIGKDD Explorations 2013 [#Aggarwal2013Outlier]_ `[PDF] \u003chttps://pdfs.semanticscholar.org/841e/ce7c3812bbf799c99c84c064bbcf77916ba9.pdf\u003e`_\nEnsembles for unsupervised outlier detection: challenges and research questions a position paper SIGKDD Explorations 2014 [#Zimek2014Ensembles]_ `[PDF] \u003chttp://www.kdd.org/exploration_files/V15-01-02-Zimek.pdf\u003e`_\nAn Unsupervised Boosting Strategy for Outlier Detection Ensembles PAKDD 2018 [#Campos2018An]_ `[HTML] \u003chttps://link.springer.com/chapter/10.1007/978-3-319-93034-3_45\u003e`_\nLSCP: Locally selective combination in parallel outlier ensembles SDM 2019 [#Zhao2019LSCP]_ `[PDF] \u003chttps://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.66\u003e`_\nAdaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream KDD 2022 [#Yoon2022ARCUS]_ `[PDF] \u003chttps://dl.acm.org/doi/pdf/10.1145/3534678.3539348\u003e`_, `[Github] \u003chttps://github.com/kaist-dmlab/ARCUS\u003e`_, `[Slide] \u003chttps://drive.google.com/file/d/1JhrnEj1vScqGy69cfNUpfTjQYZh-vj_D/view\u003e`_\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\n\n4.8. 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] \u003chttp://www.kdd.org/exploration_files/20-1-Article2.pdf\u003e`_\nUnsupervised real-time anomaly detection for streaming data Neurocomputing 2017 [#Ahmad2017Unsupervised]_ `[PDF] \u003chttps://www.researchgate.net/publication/317325599_Unsupervised_real-time_anomaly_detection_for_streaming_data\u003e`_\nOutlier Detection in Feature-Evolving Data Streams SIGKDD 2018 [#Manzoor2018Outlier]_ `[PDF] \u003chttps://www.andrew.cmu.edu/user/lakoglu/pubs/18-kdd-xstream.pdf\u003e`_, `[Github] \u003chttps://cmuxstream.github.io/\u003e`_\nEvaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark ICMLA 2015 [#Lavin2015Evaluating]_ `[PDF] \u003chttps://arxiv.org/pdf/1510.03336.pdf\u003e`_, `[Github] \u003chttps://github.com/numenta/NAB\u003e`_\nMIDAS: Microcluster-Based Detector of Anomalies in Edge Streams AAAI 2020 [#Bhatia2020MIDAS]_ `[PDF] \u003chttps://www.comp.nus.edu.sg/~sbhatia/assets/pdf/midas.pdf\u003e`_, `[Github] \u003chttps://github.com/bhatiasiddharth/MIDAS\u003e`_\nNETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing VLDB 2019 [#Yoon2019NETS]_ `[PDF] \u003chttp://www.vldb.org/pvldb/vol12/p1303-yoon.pdf\u003e`_, `[Github] \u003chttps://github.com/kaist-dmlab/NETS\u003e`_, `[Slide] \u003chttps://drive.google.com/file/d/1wqKJZhEE4nTWe0zODu21ejgPDsDA_xaF/view?usp=sharing\u003e`_\nUltrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping KDD 2020 [#Yoon2020STARE]_ `[PDF] \u003chttps://dl.acm.org/doi/pdf/10.1145/3394486.3403171\u003e`_, `[Github] \u003chttps://github.com/kaist-dmlab/STARE\u003e`_, `[Slide] \u003chttps://drive.google.com/file/d/11y7Gs703SKJBkPZ4nKKgua__dHXXMbkV/view?usp=sharing\u003e`_\nMultiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries SIGMOD 2021 [#Yoon2021MDUAL]_ `[PDF] \u003chttps://dl.acm.org/doi/pdf/10.1145/3448016.3452810\u003e`_, `[Github] \u003chttps://github.com/kaist-dmlab/MDUAL\u003e`_, `[Slide] \u003chttps://drive.google.com/file/d/1wmkkKCAcF9Dk8Wg49WnJF4U--lbtWy9J/view\u003e`_\nAdaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream KDD 2022 [#Yoon2022ARCUS]_ `[PDF] \u003chttps://dl.acm.org/doi/pdf/10.1145/3534678.3539348\u003e`_, `[Github] \u003chttps://github.com/kaist-dmlab/ARCUS\u003e`_, `[Slide] \u003chttps://drive.google.com/file/d/1JhrnEj1vScqGy69cfNUpfTjQYZh-vj_D/view\u003e`_\n================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.9. 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] \u003chttps://arxiv.org/pdf/1806.04808.pdf\u003e`_\nLearning representations for outlier detection on a budget Preprint 2015 [#Micenkova2015Learning]_ `[PDF] \u003chttps://arxiv.org/pdf/1507.08104.pdf\u003e`_\nXGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 [#Zhao2018Xgbod]_ `[PDF] \u003chttps://arxiv.org/abs/1912.00290\u003e`_\n================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.10. Interpretability\n^^^^^^^^^^^^^^^^^^^^^^\n\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\nPaper Title Venue Year Ref Materials\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\nExplaining Anomalies in Groups with Characterizing Subspace Rules DMKD 2018 [#Macha2018Explaining]_ `[PDF] \u003chttps://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-journal-xpacs.pdf\u003e`_\nBeyond Outlier Detection: LookOut for Pictorial Explanation ECML-PKDD 2018 [#Gupta2018Beyond]_ `[PDF] \u003chttps://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-lookout.pdf\u003e`_\nContextual outlier interpretation IJCAI 2018 [#Liu2018Contextual]_ `[PDF] \u003chttps://arxiv.org/pdf/1711.10589.pdf\u003e`_\nMining multidimensional contextual outliers from categorical relational data IDA 2015 [#Tang2015Mining]_ `[PDF] \u003chttp://www.cs.sfu.ca/~jpei/publications/Contextual%20outliers.pdf\u003e`_\nDiscriminative features for identifying and interpreting outliers ICDE 2014 [#Dang2014Discriminative]_ `[PDF] \u003chttps://ieeexplore.ieee.org/abstract/document/6816642\u003e`_\nSequential Feature Explanations for Anomaly Detection TKDD 2019 [#Siddiqui2019Sequential]_ `[HTML] \u003chttps://dl.acm.org/citation.cfm?id=3230666\u003e`_\nA Survey on Explainable Anomaly Detection TKDD 2023 [#Li2023XAD]_ `[HTML] \u003chttps://dl.acm.org/doi/10.1145/3609333\u003e`_\nExplainable Contextual Anomaly Detection Using Quantile Regression Forests DMKD 2023 [#Li2023QCAD]_ `[HTML] \u003chttps://link.springer.com/article/10.1007/s10618-023-00967-z\u003e`_\nBeyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network WWW 2021 [#Xu2021Beyond]_ `[PDF] \u003chttps://jiansonglei.github.io/files/21WWW.pdf\u003e`_\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.11. 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] \u003chttps://arxiv.org/pdf/1802.04431.pdf\u003e`_, `[Code] \u003chttps://github.com/khundman/telemanom\u003e`_\nMAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks ICANN 2019 [#Li2019MAD]_ `[PDF] \u003chttps://arxiv.org/pdf/1901.04997.pdf\u003e`_, `[Code] \u003chttps://github.com/LiDan456/MAD-GANs\u003e`_\nGenerative Adversarial Active Learning for Unsupervised Outlier Detection TKDE 2019 [#Liu2019Generative]_ `[PDF] \u003chttps://arxiv.org/pdf/1809.10816.pdf\u003e`_, `[Code] \u003chttps://github.com/leibinghe/GAAL-based-outlier-detection\u003e`_\nDeep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection ICLR 2018 [#Zong2018Deep]_ `[PDF] \u003chttp://www.cs.ucsb.edu/~bzong/doc/iclr18-dagmm.pdf\u003e`_, `[Code] \u003chttps://github.com/danieltan07/dagmm\u003e`_\nDeep Anomaly Detection with Outlier Exposure ICLR 2019 [#Hendrycks2019Deep]_ `[PDF] \u003chttps://arxiv.org/pdf/1812.04606.pdf\u003e`_, `[Code] \u003chttps://github.com/hendrycks/outlier-exposure\u003e`_\nUnsupervised Anomaly Detection With LSTM Neural Networks TNNLS 2019 [#Ergen2019Unsupervised]_ `[PDF] \u003chttps://arxiv.org/pdf/1710.09207.pdf\u003e`_, `[IEEE] \u003chttps://ieeexplore.ieee.org/document/8836638\u003e`_,\nEffective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network NeurIPS 2019 [#Wang2019Effective]_ `[PDF] \u003chttps://papers.nips.cc/paper/8830-effective-end-to-end-unsupervised-outlier-detection-via-inlier-priority-of-discriminative-network.pdf\u003e`_ `[Code] \u003chttps://github.com/demonzyj56/E3Outlier\u003e`_\nFascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning ICML 2023 [#Xu2023Fascinating]_ `[PDF] \u003chttps://arxiv.org/abs/2305.16114\u003e`_, `[Code] \u003chttps://github.com/xuhongzuo/scale-learning\u003e`_ \n================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.12. 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] \u003chttp://papers.nips.cc/paper/2554-active-learning-for-anomaly-and-rare-category-detection.pdf\u003e`_\nOutlier detection by active learning SIGKDD 2006 [#Abe2006Outlier]_ `[PDF] \u003chttps://www.researchgate.net/profile/Naoki_Abe2/publication/221653343_Outlier_detection_by_active_learning/links/5441464a0cf2e6f0c0f60abb.pdf\u003e`_\nActive Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Preprint 2019 [#Das2019Active]_ `[PDF] \u003chttps://arxiv.org/pdf/1901.08930.pdf\u003e`_\nMeta-AAD: Active Anomaly Detection with Deep Reinforcement Learning ICDM 2020 [#Zha2020Meta]_ `[PDF] \u003chttps://arxiv.org/pdf/2009.07415.pdf\u003e`_\nA3: Activation Anomaly Analysis ECML-PKDD 2020 [#Sperl2021A3]_ `[PDF] \u003chttps://arxiv.org/pdf/2003.01801\u003e`_, `[Code] \u003chttps://github.com/Fraunhofer-AISEC/A3\u003e`_\n================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.13. 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] \u003chttps://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.69\u003e`_\nInteractive anomaly detection on attributed networks WSDM 2019 [#Ding2019Interactive]_ `[PDF] \u003chttp://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf\u003e`_\neX2: a framework for interactive anomaly detection IUI Workshop 2019 [#Arnaldo2019ex2]_ `[PDF] \u003chttp://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf\u003e`_\nTripartite Active Learning for Interactive Anomaly Discovery IEEE Access 2019 [#Zhu2019Tripartite]_ `[PDF] \u003chttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963\u003e`_\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.14. 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] \u003chttps://epubs.siam.org/doi/pdf/10.1137/1.9781611974973.55\u003e`_\n============== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.15. 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] \u003chttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6853338\u003e`_\n**Security** Anomaly-based network intrusion detection: Techniques, systems and challenges Computers \u0026 Security 2009 [#GarciaTeodoro2009Anomaly]_ `[PDF] \u003chttps://www2.cs.uh.edu/~acl/cs6397/Doc/2009-Elsevier-Anomaly-based%20network%20intrusion%20detection.pdf\u003e`_\n**Finance** A survey of anomaly detection techniques in financial domain Future Gener Comput Syst 2016 [#Ahmed2016A]_ `[PDF] \u003chttps://www.sciencedirect.com/science/article/abs/pii/S0167739X15000023\u003e`_\n**Traffic** Outlier Detection in Urban Traffic Data WIMS 2018 [#Djenouri2018Outlier]_ `[PDF] \u003chttp://dss.sdu.dk/assets/fpd-lof/outlier-detection-urban.pdf\u003e`_\n**Social Media** A survey on social media anomaly detection SIGKDD Explorations 2016 [#Yu2016A]_ `[PDF] \u003chttps://arxiv.org/pdf/1601.01102.pdf\u003e`_\n**Social Media** GLAD: group anomaly detection in social media analysis TKDD 2015 [#Yu2015Glad]_ `[PDF] \u003chttps://arxiv.org/pdf/1410.1940.pdf\u003e`_\n**Machine Failure** Detecting the Onset of Machine Failure Using Anomaly Detection Methods DAWAK 2019 [#Riazi2019Detecting]_ `[PDF] \u003chttps://webdocs.cs.ualberta.ca/~zaiane/postscript/DAWAK19.pdf\u003e`_\n**Video Surveillance** AnomalyNet: An anomaly detection network for video surveillance TIFS 2019 [#Zhou2019AnomalyNet]_ `[IEEE] \u003chttps://ieeexplore.ieee.org/document/8649753\u003e`_, `Code \u003chttps://github.com/joeyzhouty/AnomalyNet\u003e`_\n======================== ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.16. 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] \u003chttps://www.researchgate.net/publication/358364044_AutoML_state_of_the_art_with_a_focus_on_anomaly_detection_challenges_and_research_directions\u003e`_\nAutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning ICDE 2020 [#Li2020AutoOD]_ `[PDF] \u003chttps://arxiv.org/pdf/2006.11321.pdf\u003e`_\nAutomatic Unsupervised Outlier Model Selection NeurIPS 2021 [#Zhao2020Automating]_ `[PDF] \u003chttps://openreview.net/forum?id=KCd-3Pz8VjM\u003e`_, `[Code] \u003chttps://github.com/yzhao062/MetaOD\u003e`_\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n4.17. 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] \u003chttps://www.jmlr.org/papers/volume20/19-011/19-011.pdf\u003e`_, `[Code] \u003chttps://github.com/yzhao062/pyod\u003e`_\nSUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection MLSys 2021 [#Zhao2021SUOD]_ `[PDF] \u003chttps://arxiv.org/pdf/2003.05731.pdf\u003e`_, `[Code] \u003chttps://github.com/yzhao062/suod\u003e`_\nTOD: Tensor-based Outlier Detection Preprint 2021 [#Zhao2021TOD]_ `[PDF] \u003chttps://arxiv.org/pdf/2110.14007.pdf\u003e`_, `[Code] \u003chttps://github.com/yzhao062/pytod\u003e`_\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\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] \u003chttps://web.cs.ucdavis.edu/~davidson/Publications/TR.pdf\u003e`_\nFAIROD: Fairness-aware Outlier Detection AIES 2021 [#Shekhar2021FAIROD]_ `[PDF] \u003chttps://arxiv.org/pdf/2012.03063.pdf\u003e`_\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n\n4.19. Isolation-Based Methods\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================\nPaper Title Venue Year Ref Materials\n================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================\nIsolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] \u003chttps://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf\u003e`_\nIsolation‐based anomaly detection using nearest‐neighbor ensembles Computational Intelligence 2018 [#Bandaragoda2018Isolation]_ `[PDF] \u003chttps://www.researchgate.net/publication/322359651_Isolation-based_anomaly_detection_using_nearest-neighbor_ensembles_iNNE\u003e`_, `[Code] \u003chttps://github.com/zhuye88/iNNE\u003e`_\nExtended Isolation Forest TKDE 2019 [#Hariri2019Extended]_ `[PDF] \u003chttps://arxiv.org/pdf/1811.02141.pdf\u003e`_, `[Code] \u003chttps://github.com/sahandha/eif\u003e`_\nIsolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection KDD 2020 [#Ting2020Isolation]_ `[PDF] \u003chttps://arxiv.org/pdf/2009.12196.pdf\u003e`_, `[Code] \u003chttps://github.com/IsolationKernel/Codes/tree/main/IDK\u003e`_\nDeep Isolation Forest for Anomaly Detection TKDE 2023 [#Xu2023Deep]_ `[PDF] \u003chttps://arxiv.org/abs/2206.06602\u003e`_, `[Code] \u003chttps://github.com/xuhongzuo/deep-iforest\u003e`_\n================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================\n\n\n4.20. 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] \u003chttps://arxiv.org/abs/1912.00290\u003e`_\nFeature Encoding With Autoencoders for Weakly Supervised Anomaly Detection TNNLS 2021 [#Zhou2021Feature]_ `[PDF] \u003chttps://arxiv.org/pdf/2105.10500.pdf\u003e`_, `[Code] \u003chttps://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection\u003e`_\n================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================\n\n\n\n4.21. Emerging and Interesting Topics\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\nPaper Title Venue Year Ref Materials\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\nClustering with Outlier Removal TKDE 2019 [#Liu2018Clustering]_ `[PDF] \u003chttps://arxiv.org/pdf/1801.01899.pdf\u003e`_\nReal-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning IEEE Trans. Ind. Informat. 2020 [#Castellani2020Siamese]_ `[PDF] \u003chttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9179030\u003e`_\nSSD: A Unified Framework for Self-Supervised Outlier Detection ICLR 2021 [#Sehwag2021SSD]_ `[PDF] \u003chttps://openreview.net/pdf?id=v5gjXpmR8J\u003e`_, `[Code] \u003chttps://github.com/inspire-group/SSD\u003e`_\nAD-LLM: Benchmarking Large Language Models for Anomaly Detection Preprint 2024 [#Yang2024ADLLM]_ `[PDF] \u003chttps://arxiv.org/abs/2412.11142\u003e`_, `[Code] \u003chttps://github.com/USC-FORTIS/AD-LLM\u003e`_\n================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================\n\n\n----\n\n5. Key Conferences/Workshops/Journals\n-------------------------------------\n\n5.1. Conferences \u0026 Workshops\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nKey data mining conference **deadlines**, **historical acceptance rates**, and more\ncan be found `data-mining-conferences \u003chttps://github.com/yzhao062/data-mining-conferences\u003e`_.\n\n\n`ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) \u003chttp://www.kdd.org/conferences\u003e`_. **Note**: SIGKDD usually has an Outlier Detection Workshop (ODD), see `ODD 2021 \u003chttps://oddworkshop.github.io/\u003e`_.\n\n`ACM International Conference on Management of Data (SIGMOD) \u003chttps://sigmod.org/\u003e`_\n\n`The Web Conference (WWW) \u003chttps://www2018.thewebconf.org/\u003e`_\n\n`IEEE International Conference on Data Mining (ICDM) \u003chttps://icdm2024.org//\u003e`_\n\n`SIAM International Conference on Data Mining (SDM) \u003chttps://www.siam.org/Conferences/CM/Main/sdm19\u003e`_\n\n`IEEE International Conference on Data Engineering (ICDE) \u003chttps://icde2018.org/\u003e`_\n\n`ACM InternationalConference on Information and Knowledge Management (CIKM) \u003chttp://www.cikmconference.org/\u003e`_\n\n`ACM International Conference on Web Search and Data Mining (WSDM) \u003chttp://www.wsdm-conference.org/2018/\u003e`_\n\n`The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) \u003chttp://www.ecmlpkdd2018.org/\u003e`_\n\n`The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) \u003chttp://pakdd2019.medmeeting.org\u003e`_\n\n5.2. Journals\n^^^^^^^^^^^^^\n\n`ACM Transactions on Knowledge Discovery from Data (TKDD) \u003chttps://tkdd.acm.org/\u003e`_\n\n`IEEE Transactions on Knowledge and Data Engineering (TKDE) \u003chttps://www.computer.org/web/tkde\u003e`_\n\n`ACM SIGKDD Explorations Newsletter \u003chttp://www.kdd.org/explorations\u003e`_\n\n`Data Mining and Knowledge Discovery \u003chttps://link.springer.com/journal/10618\u003e`_\n\n`Knowledge and Information Systems (KAIS) \u003chttps://link.springer.com/journal/10115\u003e`_\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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[#Djenouri2018Outlier] Djenouri, Y. and Zimek, A., 2018, June. Outlier detection in urban traffic data. In *Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics*. ACM.\n\n.. [#Domingues2018A] Domingues, R., Filippone, M., Michiardi, P. and Zouaoui, J., 2018. A comparative evaluation of outlier detection algorithms: Experiments and analyses. *Pattern Recognition*, 74, pp.406-421.\n\n.. [#Emmott2015A] Emmott, A., Das, S., Dietterich, T., Fern, A. and Wong, W.K., 2015. A meta-analysis of the anomaly detection problem. arXiv preprint arXiv:1503.01158.\n\n.. [#Ergen2019Unsupervised] Ergen, T. and Kozat, S.S., 2019. Unsupervised Anomaly Detection With LSTM Neural Networks. *IEEE transactions on neural networks and learning systems*.\n\n.. [#Falcao2019Quantitative] Falcão, F., Zoppi, T., Silva, C.B.V., Santos, A., Fonseca, B., Ceccarelli, A. and Bondavalli, A., 2019, April. Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection. In *Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing*, (pp. 318-327). ACM.\n\n.. [#GarciaTeodoro2009Anomaly] Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G. and Vázquez, E., 2009. Anomaly-based network intrusion detection: Techniques, systems and challenges. *Computers \u0026 Security*\\ , 28(1-2), pp.18-28.\n\n.. [#Goldstein2016A] Goldstein, M. and Uchida, S., 2016. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. *PloS one*\\ , 11(4), p.e0152173.\n\n.. [#Gupta2014Outlier] Gupta, M., Gao, J., Aggarwal, C.C. and Han, J., 2014. Outlier detection for temporal data: A survey. *IEEE Transactions on Knowledge and Data Engineering*\\ , 26(9), pp.2250-2267.\n\n.. [#Hariri2019Extended] Hariri, S., Kind, M.C. and Brunner, R.J., 2019. Extended Isolation Forest. *IEEE Transactions on Knowledge and Data Engineering*.\n\n.. 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In *Proceedings of the 2017 SIAM International Conference on Data Mining*, pp. 489-497. Society for Industrial and Applied Mathematics. \n\n.. [#Kriegel2010Outlier] Kriegel, H.P., Kröger, P. and Zimek, A., 2010. Outlier detection techniques. *Tutorial at ACM SIGKDD 2010*.\n\n.. [#Jiang2023adgym] Jiang, M., Hou, C., Zheng, A., Han, S., Huang, H., Wen, Q., Hu, X. and Zhao, Y., 2023. ADGym: Design Choices for Deep Anomaly Detection. *NeurIPS*, Datasets and Benchmarks Track.\n\n.. [#Jiang2023weakly] Jiang, M., Hou, C., Zheng, A., Hu, X., Han, S., Huang, H., He, X., Yu, P.S. and Zhao, Y., 2023. Weakly supervised anomaly detection: A survey. arXiv preprint arXiv:2302.04549.\n\n.. [#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.. 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Deep Learning for Anomaly Detection. *Tutorial at KDD 2020*.\n\n.. [#WellerFahy2015A] Weller-Fah","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyzhao062%2Fanomaly-detection-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyzhao062%2Fanomaly-detection-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyzhao062%2Fanomaly-detection-resources/lists"}