Anomaly Detection Learning Resources

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Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>_ (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify outlying objects that are deviant from the general data distribution. Outlier detection has been proven critical in many fields, such as credit card fraud analytics, network intrusion detection, and mechanical unit defect detection.

This repository collects:

#. Books & Academic Papers #. Online Courses and Videos #. Outlier Datasets #. Open-source and Commercial Libraries/Toolkits #. Key Conferences & Journals

More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ ([email protected]). Enjoy reading!

BTW, you may find my [GitHub] <https://github.com/yzhao062>, [USC FORTIS Lab] <https://github.com/USC-FORTIS>, and [Google Scholar] <https://scholar.google.com/citations?user=zoGDYsoAAAAJ&hl=en>_ relevant, especially 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>_,.

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Table of Contents

• 1. Books & Tutorials & Benchmarks <#1-books--tutorials--benchmarks>_

  • 1.1. Benchmarks <#13-benchmarks>_
  • 1.2. Tutorials <#12-tutorials>_
  • 1.3. Books <#11-books>_

• 2. Courses/Seminars/Videos <#2-coursesseminarsvideos>_

• 3. Toolbox & Datasets <#3-toolbox--datasets>_

  • 3.1. Multivariate data outlier detection <#31-multivariate-data>_
  • 3.2. Time series outlier detection <#32-time-series-outlier-detection>_
  • 3.3. Graph Outlier Detection <#33-graph-outlier-detection>_
  • 3.4. Real-time Elasticsearch <#34-real-time-elasticsearch>_
  • 3.5. Datasets <#35-datasets>_

• 4. Papers <#4-papers>_

  • 4.1. LLM and LLM Agents for Anomaly Detection <#41-llm-and-llm-agents-for-anomaly-detection>_
  • 4.2. Emerging and Interesting Topics <#42-emerging-and-interesting-topics>_
  • 4.3. Weakly-supervised Methods <#43-weakly-supervised-methods>_
  • 4.4. Machine Learning Systems for Outlier Detection <#44-machine-learning-systems-for-outlier-detection>_
  • 4.5. Automated Outlier Detection <#45-automated-outlier-detection>_
  • 4.6. Outlier Detection with Neural Networks <#46-outlier-detection-with-neural-networks>_
  • 4.7. Interpretability <#47-interpretability>_
  • 4.8. Representation Learning in Outlier Detection <#48-representation-learning-in-outlier-detection>_
  • 4.9. Outlier Detection in Evolving Data <#49-outlier-detection-in-evolving-data>_
  • 4.10. Outlier Ensembles <#410-outlier-ensembles>_
  • 4.11. High-dimensional & Subspace Outliers <#411-high-dimensional--subspace-outliers>_
  • 4.12. Feature Selection in Outlier Detection <#412-feature-selection-in-outlier-detection>_
  • 4.13. Time Series Outlier Detection <#413-time-series-outlier-detection>_
  • 4.14. Graph & Network Outlier Detection <#414-graph--network-outlier-detection>_
  • 4.15. Key Algorithms <#415-key-algorithms>_
  • 4.16. Overview & Survey Papers <#416-overview--survey-papers>_
  • 4.17. Isolation-based Methods <#417-isolation-based-methods>_
  • 4.18. Fairness and Bias in Outlier Detection <#418-fairness-and-bias-in-outlier-detection>_
  • 4.19. Outlier Detection Applications <#419-outlier-detection-applications>_
  • 4.20. Outlier Detection in Other fields <#420-outlier-detection-in-other-fields>_
  • 4.21. Interactive Outlier Detection <#421-interactive-outlier-detection>_
  • 4.22. Active Anomaly Detection <#422-active-anomaly-detection>_

• 5. Key Conferences/Workshops/Journals <#5-key-conferencesworkshopsjournals>_

  • 5.1. Conferences & Workshops <#51-conferences--workshops>_
  • 5.2. Journals <#52-journals>_

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1. Books & Tutorials & Benchmarks

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1.1. Benchmarks ^^^^^^^^^^^^^^^

News: We have two new works on NLP-based and LLM-based anomaly detection:

• NLP-ADBench: NLP Anomaly Detection Benchmark
• AD-LLM: Benchmarking Large Language Models for Anomaly Detection

============= ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Data Types Paper Title Venue Year Ref Materials ============= ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== NLP NLP-ADBench: NLP Anomaly Detection Benchmark Preprint 2024 [#Li2024NLPADBench]_ [PDF] <https://arxiv.org/abs/2412.04784>, [Code] <https://github.com/USC-FORTIS/NLP-ADBench> NLP 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> Time-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>_ Graph GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection NeurIPS 2023 [#Tang2023GADBench]_ [PDF] <https://arxiv.org/abs/2306.12251>, [Code] <https://github.com/squareRoot3/GADBench> Tabular ADGym: Design Choices for Deep Anomaly Detection NeurIPS 2023 [#Jiang2023adgym]_ [PDF] <https://arxiv.org/abs/2309.15376>, [Code] <https://github.com/Minqi824/ADGym> Graph 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> Tabular ADBench: Anomaly Detection Benchmark NeurIPS 2022 [#Han2022Adbench]_ [PDF] <https://arxiv.org/abs/2206.09426>, [Code] <https://github.com/Minqi824/ADBench> Time-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> ============= ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================

1.2. Tutorials ^^^^^^^^^^^^^^

===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== Tutorial Title Venue Year Ref Materials ===================================================== ============================================ ===== ============================ ========================================================================================================================================================================== Trustworthy Anomaly Detection SDM 2024 [#Yuan2024Trustworthy]_ [HTML] <https://yuan.shuhan.org/talks/SDM24/>_ Recent 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> Deep Learning for Anomaly Detection WSDM 2021 [#Pang2021Deep]_ [HTML] <https://sites.google.com/site/gspangsite/wsdm21_tutorial>_ Toward Explainable Deep Anomaly Detection KDD 2021 [#Pang2021Toward]_ [HTML] <https://sites.google.com/site/gspangsite/kdd21_tutorial>_ Deep 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> Which Outlier Detector Should I use? ICDM 2018 [#Ting2018Which]_ [PDF] <https://ieeexplore.ieee.org/document/8594824>_ Outlier detection techniques ACM SIGKDD 2010 [#Kriegel2010Outlier]_ [PDF] <https://imada.sdu.dk/~zimek/publications/KDD2010/kdd10-outlier-tutorial.pdf>_ Data mining for anomaly detection PKDD 2008 [#Lazarevic2008Data]_ [Video] <http://videolectures.net/ecmlpkdd08_lazarevic_dmfa/>_ ===================================================== ============================================ ===== ============================ ==========================================================================================================================================================================

1.3. Books ^^^^^^^^^^

Outlier Analysis <https://link.springer.com/book/10.1007/978-3-319-47578-3>_ by Charu Aggarwal: Classical text book covering most of the outlier analysis techniques. A must-read for people in the field of outlier detection. [Preview.pdf] <http://charuaggarwal.net/outlierbook.pdf>_

Outlier Ensembles: An Introduction <https://www.springer.com/gp/book/9783319547640>_ by Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier analysis.

Data Mining: Concepts and Techniques (3rd) <https://www.elsevier.com/books/data-mining-concepts-and-techniques/han/978-0-12-381479-1>_ by 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>_

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2. Courses/Seminars/Videos

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Coursera Introduction to Anomaly Detection (by IBM)\ : [See Video] <https://www.coursera.org/learn/ai/lecture/ASPv0/introduction-to-anomaly-detection>_

Get started with the Anomaly Detection API (by IBM)\ : [See Website] <https://developer.ibm.com/learningpaths/get-started-anomaly-detection-api/>_

Practical Anomaly Detection by appliedAI Institute: [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>_

Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic\ : [See Video] <https://www.coursera.org/learn/real-time-cyber-threat-detection>_

Coursera Machine Learning by Andrew Ng also partly covers the topic\ :

• Anomaly Detection vs. Supervised Learning <https://www.coursera.org/learn/machine-learning/lecture/Rkc5x/anomaly-detection-vs-supervised-learning>_
• Developing and Evaluating an Anomaly Detection System <https://www.coursera.org/learn/machine-learning/lecture/Mwrni/developing-and-evaluating-an-anomaly-detection-system>_

Udemy Outlier Detection Algorithms in Data Mining and Data Science\ : [See Video] <https://www.udemy.com/outlier-detection-techniques/>_

Stanford Data Mining for Cyber Security also covers part of anomaly detection techniques\ : [See Video] <http://web.stanford.edu/class/cs259d/>_

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3. Toolbox & Datasets

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[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.

3.1. Multivariate Data ^^^^^^^^^^^^^^^^^^^^^^

[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.

[Python, GPU] TOD: Tensor-based Outlier Detection (PyTOD) <https://github.com/yzhao062/pytod>_: A general GPU-accelerated framework for outlier detection.

[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.

[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.

[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.

[Julia] OutlierDetection.jl <https://github.com/OutlierDetectionJL/OutlierDetection.jl>_\ : OutlierDetection.jl is a Julia toolkit for detecting outlying objects, also known as anomalies.

[Java] ELKI: Environment for Developing KDD-Applications Supported by Index-Structures <https://elki-project.github.io/>_\ : ELKI 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.

[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.

[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.

[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.

[Matlab] Anomaly Detection Toolbox - Beta <http://dsmi-lab-ntust.github.io/AnomalyDetectionToolbox/>_\ : A collection of popular outlier detection algorithms in Matlab.

3.2. Time Series Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

[Python] TODS <https://github.com/datamllab/tods>_\ : TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.

[Python] skyline <https://github.com/earthgecko/skyline>_\ : Skyline is a near real time anomaly detection system.

[Python] banpei <https://github.com/tsurubee/banpei>_\ : Banpei is a Python package of the anomaly detection.

[Python] telemanom <https://github.com/khundman/telemanom>_\ : A framework for using LSTMs to detect anomalies in multivariate time series data.

[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.

[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.

[Python] CueObserve <https://github.com/cuebook/CueObserve>_\ : Anomaly detection on SQL data warehouses and databases.

[Python] Chaos Genius <https://github.com/chaos-genius/chaos_genius>_\ : ML powered analytics engine for outlier/anomaly detection and root cause analysis.

[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.

[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.

[R] anomalize <https://cran.r-project.org/web/packages/anomalize/>_\ : The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.

3.3. Graph Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^

[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

3.4. Real-time Elasticsearch ^^^^^^^^^^^^^^^^^^^^^^^^^^^^

[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/>.

[Python] datastream.io <https://github.com/MentatInnovations/datastream.io>_\ : An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.

3.5. Datasets ^^^^^^^^^^^^^

NLP-ADBench: NLP Anomaly Detection Benchmark and Datasets: https://github.com/USC-FORTIS/NLP-ADBench

ELKI Outlier Datasets\ : https://elki-project.github.io/datasets/outlier

Outlier Detection DataSets (ODDS)\ : http://odds.cs.stonybrook.edu/#table1

Unsupervised Anomaly Detection Dataverse\ : https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OPQMVF

Anomaly Detection Meta-Analysis Benchmarks\ : https://ir.library.oregonstate.edu/concern/datasets/47429f155

Skoltech Anomaly Benchmark (SKAB)\ : https://github.com/waico/skab

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4. Papers

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Recommended reading order (latest-first):

• 4.1. LLM and LLM Agents for Anomaly Detection <#41-llm-and-llm-agents-for-anomaly-detection>_
• 4.2. Emerging and Interesting Topics <#42-emerging-and-interesting-topics>_
• 4.3. Weakly-supervised Methods <#43-weakly-supervised-methods>_
• 4.4. Machine Learning Systems for Outlier Detection <#44-machine-learning-systems-for-outlier-detection>_
• 4.5. Automated Outlier Detection <#45-automated-outlier-detection>_

4.1. LLM and LLM Agents for Anomaly Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

============================================================================================================== ============================ ===== ============================ ===================================================================================================================================================================================== Paper Title Venue Year Ref Materials ============================================================================================================== ============================ ===== ============================ ===================================================================================================================================================================================== AD-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> NLP-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> AD-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> LogSAD: 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> MMAD: 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> Delving 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> ============================================================================================================== ============================ ===== ============================ =====================================================================================================================================================================================

4.2. Emerging and Interesting Topics ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Clustering with Outlier Removal TKDE 2019 [#Liu2018Clustering]_ [PDF] <https://arxiv.org/pdf/1801.01899.pdf>_ Real-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>_ SSD: 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> 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> ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================

4.3. Weakly-Supervised Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== XGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 [#Zhao2018Xgbod]_ [PDF] <https://arxiv.org/abs/1912.00290>_ Feature 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> ================================================================================================= ============================ ===== ============================= ==============================================================================================================================================================================================

4.4. Machine Learning Systems for Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

This section summarizes a list of systems for outlier detection, which may overlap with the section of tools and libraries.

================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== PyOD: 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> SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection MLSys 2021 [#Zhao2021SUOD]_ [PDF] <https://arxiv.org/pdf/2003.05731.pdf>, [Code] <https://github.com/yzhao062/suod> TOD: Tensor-based Outlier Detection Preprint 2021 [#Zhao2021TOD]_ [PDF] <https://arxiv.org/pdf/2110.14007.pdf>, [Code] <https://github.com/yzhao062/pytod> ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================

4.5. Automated Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== AutoML: 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>_ AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning ICDE 2020 [#Li2020AutoOD]_ [PDF] <https://arxiv.org/pdf/2006.11321.pdf>_ Automatic Unsupervised Outlier Model Selection NeurIPS 2021 [#Zhao2020Automating]_ [PDF] <https://openreview.net/forum?id=KCd-3Pz8VjM>, [Code] <https://github.com/yzhao062/MetaOD> ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================

4.6. Outlier Detection with Neural Networks ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Detecting 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> MAD-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> Generative 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> Deep 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> Deep Anomaly Detection with Outlier Exposure ICLR 2019 [#Hendrycks2019Deep]_ [PDF] <https://arxiv.org/pdf/1812.04606.pdf>, [Code] <https://github.com/hendrycks/outlier-exposure> Unsupervised Anomaly Detection With LSTM Neural Networks TNNLS 2019 [#Ergen2019Unsupervised]_ [PDF] <https://arxiv.org/pdf/1710.09207.pdf>, [IEEE] <https://ieeexplore.ieee.org/document/8836638>, Effective 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>_ Fascinating 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> ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================

4.7. Interpretability ^^^^^^^^^^^^^^^^^^^^^^

================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Explaining Anomalies in Groups with Characterizing Subspace Rules DMKD 2018 [#Macha2018Explaining]_ [PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-journal-xpacs.pdf>_ Beyond Outlier Detection: LookOut for Pictorial Explanation ECML-PKDD 2018 [#Gupta2018Beyond]_ [PDF] <https://www.andrew.cmu.edu/user/lakoglu/pubs/18-pkdd-lookout.pdf>_ Contextual outlier interpretation IJCAI 2018 [#Liu2018Contextual]_ [PDF] <https://arxiv.org/pdf/1711.10589.pdf>_ Mining multidimensional contextual outliers from categorical relational data IDA 2015 [#Tang2015Mining]_ [PDF] <http://www.cs.sfu.ca/~jpei/publications/Contextual%20outliers.pdf>_ Discriminative features for identifying and interpreting outliers ICDE 2014 [#Dang2014Discriminative]_ [PDF] <https://ieeexplore.ieee.org/abstract/document/6816642>_ Sequential Feature Explanations for Anomaly Detection TKDD 2019 [#Siddiqui2019Sequential]_ [HTML] <https://dl.acm.org/citation.cfm?id=3230666>_ A Survey on Explainable Anomaly Detection TKDD 2023 [#Li2023XAD]_ [HTML] <https://dl.acm.org/doi/10.1145/3609333>_ Explainable Contextual Anomaly Detection Using Quantile Regression Forests DMKD 2023 [#Li2023QCAD]_ [HTML] <https://link.springer.com/article/10.1007/s10618-023-00967-z>_ Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network WWW 2021 [#Xu2021Beyond]_ [PDF] <https://jiansonglei.github.io/files/21WWW.pdf>_ ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================

4.8. Representation Learning in Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ [PDF] <https://arxiv.org/pdf/1806.04808.pdf>_ Learning representations for outlier detection on a budget Preprint 2015 [#Micenkova2015Learning]_ [PDF] <https://arxiv.org/pdf/1507.08104.pdf>_ XGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 [#Zhao2018Xgbod]_ [PDF] <https://arxiv.org/abs/1912.00290>_ ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================

4.9. Outlier Detection in Evolving Data ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== A 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>_ Unsupervised 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>_ Outlier 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/> Evaluating 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> MIDAS: 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> NETS: 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>_ Ultrafast 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>_ Multiple 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>_ Adaptive 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>_ ================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================

4.10. Outlier Ensembles ^^^^^^^^^^^^^^^^^^^^^^

================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Outlier ensembles: position paper SIGKDD Explorations 2013 [#Aggarwal2013Outlier]_ [PDF] <https://pdfs.semanticscholar.org/841e/ce7c3812bbf799c99c84c064bbcf77916ba9.pdf>_ Ensembles 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>_ An Unsupervised Boosting Strategy for Outlier Detection Ensembles PAKDD 2018 [#Campos2018An]_ [HTML] <https://link.springer.com/chapter/10.1007/978-3-319-93034-3_45>_ LSCP: Locally selective combination in parallel outlier ensembles SDM 2019 [#Zhao2019LSCP]_ [PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.66>_ Adaptive 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>_ ================================================================================================= ============================ ===== ============================ ==========================================================================================================================================================================

4.11. High-dimensional & Subspace Outliers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= Paper Title Venue Year Ref Materials ================================================================================================== ============================ ===== ============================ ======================================================================================================================================================================================================= A 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>_ Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [#Pang2018Learning]_ [PDF] <https://arxiv.org/pdf/1806.04808.pdf>_ Reverse 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> Outlier detection for high-dimensional data Biometrika 2015 [#Ro2015Outlier]_ [PDF] <http://web.hku.hk/~gyin/materials/2015RoZouWangYinBiometrika.pdf>_ ================================================================================================== ============================ ===== ============================ =======================================================================================================================================================================================================

4.12. Feature Selection in Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================================ ============================ ===== ============================ ========================================================================================================================================================================== Unsupervised 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>_ Learning 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>_ ================================================================================================================ ============================ ===== ============================ ==========================================================================================================================================================================

4.13. Time Series Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

===================================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ===================================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Outlier 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>_ Detecting 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> Time-Series Anomaly Detection Service at Microsoft KDD 2019 [#Ren2019Time]_ [PDF] <https://arxiv.org/pdf/1906.03821.pdf>_ 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> Graph-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> Drift 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> ===================================================================================================================================== ============================ ===== ============================ ==========================================================================================================================================================================

4.14. Graph & Network Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================= ===== ============================ ========================================================================================================================================================================== Graph based anomaly detection and description: a survey DMKD 2015 [#Akoglu2015Graph]_ [PDF] <https://arxiv.org/pdf/1404.4679.pdf>_ Anomaly detection in dynamic networks: a survey WIREs Computational Statistic 2015 [#Ranshous2015Anomaly]_ [PDF] <https://onlinelibrary.wiley.com/doi/pdf/10.1002/wics.1347>_ Outlier detection in graphs: On the impact of multiple graph models ComSIS 2019 [#Campos2019Outlier]_ [PDF] <http://www.comsis.org/pdf.php?id=wims-8671>_ A Comprehensive Survey on Graph Anomaly Detection with Deep Learning TKDE 2021 [#Ma2021A]_ [PDF] <https://arxiv.org/pdf/2106.07178.pdf>_ ================================================================================================= ============================= ===== ============================ ==========================================================================================================================================================================

4.15. Key Algorithms ^^^^^^^^^^^^^^^^^^^

All these algorithms are available in Python Outlier Detection (PyOD) <https://github.com/yzhao062/pyod>_.

==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== Abbreviation Paper Title Venue Year Ref Materials ==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== kNN 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>_ KNN 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>_ LOF LOF: identifying density-based local outliers ACM SIGMOD Record 2000 [#Breunig2000LOF]_ `[PDF] <http://www.dbs.ifi.lmu.de/Publikationen/Pap