Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
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Anomaly Detection Learning Resources ==================================== .. image:: https://img.shields.io/github/stars/yzhao062/anomaly-detection-resources.svg :target: https://github.com/yzhao062/anomaly-detection-resources/stargazers :alt: GitHub stars .. image:: https://img.shields.io/github/forks/yzhao062/anomaly-detection-resources.svg?color=blue :target: https://github.com/yzhao062/anomaly-detection-resources/network :alt: GitHub forks .. image:: https://img.shields.io/github/license/yzhao062/anomaly-detection-resources.svg?color=blue :target: https://github.com/yzhao062/anomaly-detection-resources/blob/master/LICENSE :alt: License .. image:: https://awesome.re/badge-flat2.svg :target: https://awesome.re/badge-flat2.svg :alt: Awesome .. image:: https://img.shields.io/badge/ADBench-benchmark_results-pink :target: https://github.com/Minqi824/ADBench :alt: Benchmark ---- `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 @ (yzhao010@usc.edu). 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>`_,. ---- 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>`_ ---- 1. Books & Tutorials & Benchmarks --------------------------------- 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>`_ ---- 2. Courses/Seminars/Videos -------------------------- **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/>`_ ---- 3. Toolbox & Datasets --------------------- [**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 ---- 4. Papers --------- 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/Papers/LOF.pdf>`_ IForest Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_ OCSVM Estimating the support of a high-dimensional distribution Neural Computation 2001 [#Scholkopf2001Estimating]_ `[PDF] <http://users.cecs.anu.edu.au/~williams/papers/P132.pdf>`_ AutoEncoder Ensemble Outlier detection with autoencoder ensembles SDM 2017 [#Chen2017Outlier]_ `[PDF] <http://saketsathe.net/downloads/autoencode.pdf>`_ COPOD COPOD: Copula-Based Outlier Detection ICDM 2020 [#Li2020COPOD]_ `[PDF] <https://arxiv.org/abs/2009.09463>`_ ECOD Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions TKDE 2022 [#Li2021ECOD]_ `[PDF] <https://arxiv.org/abs/2201.00382>`_ ==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== 4.16. Overview & Survey Papers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Papers are sorted by the publication year. ====================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ====================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== A survey of outlier detection methodologies ARTIF INTELL REV 2004 [#Hodge2004A]_ `[PDF] <https://www-users.cs.york.ac.uk/vicky/myPapers/Hodge+Austin_OutlierDetection_AIRE381.pdf>`_ Anomaly detection: A survey CSUR 2009 [#Chandola2009Anomaly]_ `[PDF] <https://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf>`_ A meta-analysis of the anomaly detection problem Preprint 2015 [#Emmott2015A]_ `[PDF] <https://arxiv.org/pdf/1503.01158.pdf>`_ On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study DMKD 2016 [#Campos2016On]_ `[HTML] <https://link.springer.com/article/10.1007/s10618-015-0444-8>`_, `[SLIDES] <https://imada.sdu.dk/~zimek/InvitedTalks/TUVienna-2016-05-18-outlier-evaluation.pdf>`_ A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data PLOS ONE 2016 [#Goldstein2016A]_ `[PDF] <http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0152173&type=printable>`_ A comparative evaluation of outlier detection algorithms: Experiments and analyses Pattern Recognition 2018 [#Domingues2018A]_ `[PDF] <https://www.researchgate.net/publication/320025854_A_comparative_evaluation_of_outlier_detection_algorithms_Experiments_and_analyses>`_ Research Issues in Outlier Detection Book Chapter 2019 [#Suri2019Research]_ `[HTML] <https://link.springer.com/chapter/10.1007/978-3-030-05127-3_3>`_ Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection SAC 2019 [#Falcao2019Quantitative]_ `[HTML] <https://dl.acm.org/citation.cfm?id=3297314>`_ Progress in Outlier Detection Techniques: A Survey IEEE Access 2019 [#Wang2019Progress]_ `[PDF] <https://ieeexplore.ieee.org/iel7/6287639/8600701/08786096.pdf>`_ Deep learning for anomaly detection: A survey Preprint 2019 [#Chalapathy2019Deep]_ `[PDF] <https://arxiv.org/pdf/1901.03407.pdf>`_ Anomalous Instance Detection in Deep Learning: A Survey Tech Report 2020 [#Bulusu2020Deep]_ `[PDF] <https://arxiv.org/pdf/2003.06979.pdf>`_ Anomaly detection in univariate time-series: A survey on the state-of-the-art Preprint 2020 [#Braei2020Anomaly]_ `[PDF] <https://arxiv.org/pdf/2004.00433.pdf>`_ Deep Learning for Anomaly Detection: A Review CSUR 2021 [#Pang2020Deep]_ `[PDF] <https://arxiv.org/pdf/2007.02500.pdf>`_ A Comprehensive Survey on Graph Anomaly Detection with Deep Learning TKDE 2021 [#Ma2021A]_ `[PDF] <https://arxiv.org/pdf/2106.07178.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>`_ A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges Preprint 2021 [#Salehi2021A]_ `[PDF] <https://arxiv.org/pdf/2110.14051.pdf>`_ Self-Supervised Anomaly Detection: A Survey and Outlook Preprint 2022 [#Hojjati2022Self]_ `[PDF] <https://arxiv.org/pdf/2205.05173.pdf>`_ Weakly supervised anomaly detection: A survey Preprint 2023 [#Jiang2023weakly]_ `[PDF] <https://arxiv.org/abs/2302.04549>`_, `[PDF] <https://github.com/yzhao062/wsad>`_ 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>`_ Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey Preprint 2024 [#Xu2024LLMsurvey]_ `[PDF] <https://arxiv.org/abs/2409.01980>`_ ====================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== 4.17. Isolation-Based Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_ Isolation‐based anomaly detection using nearest‐neighbor ensembles Computational Intelligence 2018 [#Bandaragoda2018Isolation]_ `[PDF] <https://www.researchgate.net/publication/322359651_Isolation-based_anomaly_detection_using_nearest-neighbor_ensembles_iNNE>`_, `[Code] <https://github.com/zhuye88/iNNE>`_ Extended Isolation Forest TKDE 2019 [#Hariri2019Extended]_ `[PDF] <https://arxiv.org/pdf/1811.02141.pdf>`_, `[Code] <https://github.com/sahandha/eif>`_ Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection KDD 2020 [#Ting2020Isolation]_ `[PDF] <https://arxiv.org/pdf/2009.12196.pdf>`_, `[Code] <https://github.com/IsolationKernel/Codes/tree/main/IDK>`_ Deep Isolation Forest for Anomaly Detection TKDE 2023 [#Xu2023Deep]_ `[PDF] <https://arxiv.org/abs/2206.06602>`_, `[Code] <https://github.com/xuhongzuo/deep-iforest>`_ ================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== 4.18. Fairness and Bias in Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== A Framework for Determining the Fairness of Outlier Detection ECAI 2020 [#Davidson2020A]_ `[PDF] <https://web.cs.ucdavis.edu/~davidson/Publications/TR.pdf>`_ FAIROD: Fairness-aware Outlier Detection AIES 2021 [#Shekhar2021FAIROD]_ `[PDF] <https://arxiv.org/pdf/2012.03063.pdf>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.19. Outlier Detection Applications ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Field Paper Title Venue Year Ref Materials ======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== **Security** A survey of distance and similarity measures used within network intrusion anomaly detection IEEE Commun. Surv. Tutor. 2015 [#WellerFahy2015A]_ `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6853338>`_ **Security** Anomaly-based network intrusion detection: Techniques, systems and challenges Computers & Security 2009 [#GarciaTeodoro2009Anomaly]_ `[PDF] <https://www2.cs.uh.edu/~acl/cs6397/Doc/2009-Elsevier-Anomaly-based%20network%20intrusion%20detection.pdf>`_ **Finance** A survey of anomaly detection techniques in financial domain Future Gener Comput Syst 2016 [#Ahmed2016A]_ `[PDF] <https://www.sciencedirect.com/science/article/abs/pii/S0167739X15000023>`_ **Traffic** Outlier Detection in Urban Traffic Data WIMS 2018 [#Djenouri2018Outlier]_ `[PDF] <http://dss.sdu.dk/assets/fpd-lof/outlier-detection-urban.pdf>`_ **Social Media** A survey on social media anomaly detection SIGKDD Explorations 2016 [#Yu2016A]_ `[PDF] <https://arxiv.org/pdf/1601.01102.pdf>`_ **Social Media** GLAD: group anomaly detection in social media analysis TKDD 2015 [#Yu2015Glad]_ `[PDF] <https://arxiv.org/pdf/1410.1940.pdf>`_ **Machine Failure** Detecting the Onset of Machine Failure Using Anomaly Detection Methods DAWAK 2019 [#Riazi2019Detecting]_ `[PDF] <https://webdocs.cs.ualberta.ca/~zaiane/postscript/DAWAK19.pdf>`_ **Video Surveillance** AnomalyNet: An anomaly detection network for video surveillance TIFS 2019 [#Zhou2019AnomalyNet]_ `[IEEE] <https://ieeexplore.ieee.org/document/8649753>`_, `Code <https://github.com/joeyzhouty/AnomalyNet>`_ ======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.20. Outlier Detection in Other fields ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Field Paper Title Venue Year Ref Materials ============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== **Text** Outlier detection for text data SDM 2017 [#Kannan2017Outlier]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611974973.55>`_ ============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.21. Interactive Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback SDM 2019 [#Lamba2019Learning]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.69>`_ Interactive anomaly detection on attributed networks WSDM 2019 [#Ding2019Interactive]_ `[PDF] <http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf>`_ eX2: a framework for interactive anomaly detection IUI Workshop 2019 [#Arnaldo2019ex2]_ `[PDF] <http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf>`_ Tripartite Active Learning for Interactive Anomaly Discovery IEEE Access 2019 [#Zhu2019Tripartite]_ `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.22. Active Anomaly Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Active learning for anomaly and rare-category detection NeurIPS 2005 [#Pelleg2005Active]_ `[PDF] <http://papers.nips.cc/paper/2554-active-learning-for-anomaly-and-rare-category-detection.pdf>`_ Outlier detection by active learning SIGKDD 2006 [#Abe2006Outlier]_ `[PDF] <https://www.researchgate.net/profile/Naoki_Abe2/publication/221653343_Outlier_detection_by_active_learning/links/5441464a0cf2e6f0c0f60abb.pdf>`_ Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Preprint 2019 [#Das2019Active]_ `[PDF] <https://arxiv.org/pdf/1901.08930.pdf>`_ Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning ICDM 2020 [#Zha2020Meta]_ `[PDF] <https://arxiv.org/pdf/2009.07415.pdf>`_ A3: Activation Anomaly Analysis ECML-PKDD 2020 [#Sperl2021A3]_ `[PDF] <https://arxiv.org/pdf/2003.01801>`_, `[Code] <https://github.com/Fraunhofer-AISEC/A3>`_ ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== ---- 5. Key Conferences/Workshops/Journals ------------------------------------- 5.1. Conferences & Workshops ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Key data mining conference **deadlines**, **historical acceptance rates**, and more can be found `data-mining-conferences <https://github.com/yzhao062/data-mining-conferences>`_. `ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) <http://www.kdd.org/conferences>`_. **Note**: SIGKDD usually has an Outlier Detection Workshop (ODD), see `ODD 2021 <https://oddworkshop.github.io/>`_. `ACM International Conference on Management of Data (SIGMOD) <https://sigmod.org/>`_ `The Web Conference (WWW) <https://www2018.thewebconf.org/>`_ `IEEE International Conference on Data Mining (ICDM) <https://icdm2024.org//>`_ `SIAM International Conference on Data Mining (SDM) <https://www.siam.org/Conferences/CM/Main/sdm19>`_ `IEEE International Conference on Data Engineering (ICDE) <https://icde2018.org/>`_ `ACM InternationalConference on Information and Knowledge Management (CIKM) <http://www.cikmconference.org/>`_ `ACM International Conference on Web Search and Data Mining (WSDM) <http://www.wsdm-conference.org/2018/>`_ `The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) <http://www.ecmlpkdd2018.org/>`_ `The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) <http://pakdd2019.medmeeting.org>`_ 5.2. Journals ^^^^^^^^^^^^^ `ACM Transactions on Knowledge Discovery from Data (TKDD) <https://tkdd.acm.org/>`_ `IEEE Transactions on Knowledge and Data Engineering (TKDE) <https://www.computer.org/web/tkde>`_ `ACM SIGKDD Explorations Newsletter <http://www.kdd.org/explorations>`_ `Data Mining and Knowledge Discovery <https://link.springer.com/journal/10618>`_ `Knowledge and Information Systems (KAIS) <https://link.springer.com/journal/10115>`_ ---- References ---------- .. [#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. .. [#Aggarwal2013Outlier] Aggarwal, C.C., 2013. Outlier ensembles: position paper. *ACM SIGKDD Explorations Newsletter*\ , 14(2), pp.49-58. .. [#Ahmed2016A] Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. 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[#Kriegel2010Outlier] Kriegel, H.P., Kröger, P. and Zimek, A., 2010. Outlier detection techniques. *Tutorial at ACM SIGKDD 2010*. .. [#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. .. [#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. .. [#Jiang2025MMAD] Jiang, X., Li, J., Deng, H., Liu, Y., Gao, B., Zhou, Y., Li, J., Wang, C. and Zheng, F., 2025. MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection. In *ICLR 2025*. .. [#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. .. 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[#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. .. [#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. .. [#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. .. [#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. .. [#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. .. [#Liu2008Isolation] Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In *International Conference on Data Mining*\ , pp. 413-422. IEEE. .. [#Liu2018Clustering] Liu, H., Li, J., Wu, Y. and Fu, Y., 2019. Clustering with outlier removal. *IEEE transactions on knowledge and data engineering*. .. [#Liu2018Contextual] Liu, N., Shin, D. and Hu, X., 2017. Contextual outlier interpretation. In *International Joint Conference on Artificial Intelligence (IJCAI-18)*, pp.2461-2467. .. [#Liu2019Generative] Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M. and He, X., 2019. Generative Adversarial Active Learning for Unsupervised Outlier Detection. *IEEE transactions on knowledge and data engineering*. .. [#Li2020AutoOD] Li, Y., Chen, Z., Zha, D., Zhou, K., Jin, H., Chen, H. and Hu, X., 2020. AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning. *ICDE*. .. [#Liu2022Benchmarking] Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K., Sun, L., Li, J., Chen, G.H., Jia, Z., and Yu, P.S. 2022. Benchmarking Node Outlier Detection on Graphs. arXiv preprint arXiv:2206.10071. .. [#Liu2024Elephant] Liu, Q. and Paparrizos, J., 2024. The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark. *NeurIPS 2024 Datasets and Benchmarks Track*. .. [#Ma2021A] Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q.Z., Xiong, H. and Akoglu, L., 2021. A comprehensive survey on graph anomaly detection with deep learning. *IEEE Transactions on Knowledge and Data Engineering*. .. [#Macha2018Explaining] Macha, M. and Akoglu, L., 2018. Explaining anomalies in groups with characterizing subspace rules. Data Mining and Knowledge Discovery, 32(5), pp.1444-1480. .. [#Manzoor2018Outlier] Manzoor, E., Lamba, H. and Akoglu, L. Outlier Detection in Feature-Evolving Data Streams. 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[#Pang2023recent] Guansong Pang, Joey Tianyi Zhou, Radu Tudor Ionescu, Yu Tian, and Kihyuk Sohn. "Recent Advances in Anomaly Detection". In: *CVPR'23*. Vancouver, Canada. .. [#Park2025LLMTSAD] Park, J., Jung, K., Lee, D., Lee, H., Gwak, D., Park, C., Choo, J. and Cho, J., 2025. Delving into Large Language Models for Effective Time-Series Anomaly Detection. In *NeurIPS 2025*. .. [#Pelleg2005Active] Pelleg, D. and Moore, A.W., 2005. Active learning for anomaly and rare-category detection. In *Advances in neural information processing systems*\, pp. 1073-1080. .. [#Radovanovic2015Reverse] Radovanović, M., Nanopoulos, A. and Ivanović, M., 2015. Reverse nearest neighbors in unsupervised distance-based outlier detection. *IEEE transactions on knowledge and data engineering*, 27(5), pp.1369-1382. .. [#Ramaswamy2000Efficient] Ramaswamy, S., Rastogi, R. and Shim, K., 2000, May. Efficient algorithms for mining outliers from large data sets. *ACM SIGMOD Record*\ , 29(2), pp. 427-438. .. 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[#Siddiqui2019Sequential] Siddiqui, M.A., Fern, A., Dietterich, T.G. and Wong, W.K., 2019. Sequential Feature Explanations for Anomaly Detection. *ACM Transactions on Knowledge Discovery from Data (TKDD)*, 13(1), p.1. .. [#Sperl2021A3] Sperl, P., Schulze, J.-P., and Böttinger, K., 2021. Activation Anomaly Analysis. *European Conference on Machine Learning and Data Mining (ECML-PKDD) 2020*. .. [#Suri2019Research] Suri, N.R. and Athithan, G., 2019. Research Issues in Outlier Detection. In *Outlier Detection: Techniques and Applications*, pp. 29-51. Springer, Cham. .. [#Tang2015Mining] Tang, G., Pei, J., Bailey, J. and Dong, G., 2015. Mining multidimensional contextual outliers from categorical relational data. *Intelligent Data Analysis*, 19(5), pp.1171-1192. .. [#Tang2023GADBench] Tang, J., Hua, F., Gao, Z., Zhao, P. and Li, J., 2023. GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection. *NeurIPS*, Datasets and Benchmarks Track. .. [#Ting2018Which] Ting, KM., Aryal, S. and Washio, T., 2018, Which Anomaly Detector should I use? *Tutorial at ICDM 2018*. .. [#Ting2020Isolation] Ting, Kai Ming, Bi-Cun Xu, Takashi Washio, and Zhi-Hua Zhou. "Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection." In *Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*, pp. 198-206. 2020. .. [#Wang2019Effective] Wang, S., Zeng, Y., Liu, X., Zhu, E., Yin, J., Xu, C. and Kloft, M., 2019. Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network. In *33rd Conference on Neural Information Processing Systems*. .. [#Wang2019Progress] Wang, H., Bah, M.J. and Hammad, M., 2019. Progress in Outlier Detection Techniques: A Survey. *IEEE Access*, 7, pp.107964-108000. .. [#Wang2020Deep] Wang, R., Nie, K., Chang, Y. J., Gong, X., Wang, T., Yang, Y., Long, B., 2020. Deep Learning for Anomaly Detection. *Tutorial at KDD 2020*. .. [#WellerFahy2015A] Weller-Fahy, D.J., Borghetti, B.J. and Sodemann, A.A., 2015. A survey of distance and similarity measures used within network intrusion anomaly detection. *IEEE Communications Surveys & Tutorials*\ , 17(1), pp.70-91. .. [#Xu2021Beyond] Xu, H., Wang, Y., Jian, S., Huang, Z., Wang, Y., Liu, N. and Li, F., 2021, April. Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network. In *Proceedings of the Web Conference* 2021 (pp. 1328-1339). .. [#Xu2023Deep] Xu, H., Pang, G., Wang, Y., Wang, Y., 2023. Deep Isolation Forest for Anomaly Detection. *IEEE Transactions on Knowledge and Data Engineering*. .. [#Xu2023Fascinating] Xu, H., Wang, Y., Wei, J., Jian, S., Li, Y., Liu, N., 2023. Fascinating Supervisory Signals and Where to Find Them: Deep Anomaly Detection with Scale Learning. *International Conference on Machine Learning (ICML)*. .. [#Xu2024LLMsurvey] Xu, R. and Ding, K., 2024. Large language models for anomaly and out-of-distribution detection: A survey. arXiv preprint arXiv:2409.01980. .. [#Yang2024ADLLM] Yang, T., Nian, Y., Li, S., Xu, R., Li, Y., Li, J., Xiao, Z., Hu, X., Rossi, R., Ding, K., Hu, X. and Zhao, Y. "AD-LLM: Benchmarking Large Language Models for Anomaly Detection." Findings of ACL, 2025. .. [#Yang2025ADAGENT] Yang, T., Liu, J., Siu, W., Wang, J., Qian, Z., Song, C., Cheng, C., Hu, X., and Zhao, Y. "AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection." Findings of IJCNLP-AACL, 2025. .. [#Yoon2019NETS] Yoon, S., Lee, J. G., & Lee, B. S., 2019. NETS: extremely fast outlier detection from a data stream via set-based processing. Proceedings of the VLDB Endowment, 12(11), 1303-1315. .. [#Yoon2020STARE] Yoon, S., Lee, J. G., & Lee, B. S., 2020. Ultrafast local outlier detection from a data stream with stationary region skipping. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 1181-1191) .. [#Yoon2021MDUAL] Yoon, S., Shin, Y., Lee, J. G., & Lee, B. S. (2021, June). Multiple dynamic outlier-detection from a data stream by exploiting duality of data and queries. In Proceedings of the 2021 International Conference on Management of Data (SIGMOD). .. [#Yoon2022ARCUS] Yoon, S., Lee, Y., Lee, J.G. and Lee, B.S., 2022, August. Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2347-2357). .. [#Yu2015Glad] Yu, R., He, X. and Liu, Y., 2015. GLAD: group anomaly detection in social media analysis. *ACM Transactions on Knowledge Discovery from Data (TKDD)*\ , 10(2), p.18. .. [#Yu2016A] Yu, R., Qiu, H., Wen, Z., Lin, C. and Liu, Y., 2016. A survey on social media anomaly detection. *ACM SIGKDD Explorations Newsletter*\ , 18(1), pp.1-14. .. [#Yuan2024Trustworthy] Yuan, S., Xu, D. and Wu, X., 2024 Trustworthy Anomaly Detection. *Tutorial at SDM 2024*. .. [#Zha2020Meta] Zha, D., Lai, K.H., Wan, M. and Hu, X., 2020. Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning. *ICDM*. .. [#Zhao2018Xgbod] Zhao, Y. and Hryniewicki, M.K., 2018, July. XGBOD: improving supervised outlier detection with unsupervised representation learning. In *2018 International Joint Conference on Neural Networks (IJCNN)*. IEEE. .. [#Zhao2019LSCP] Zhao, Y., Nasrullah, Z., Hryniewicki, M.K. and Li, Z., 2019, May. LSCP: Locally selective combination in parallel outlier ensembles. In *Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)*, pp. 585-593. Society for Industrial and Applied Mathematics. .. [#Zhao2019PYOD] Zhao, Y., Nasrullah, Z. and Li, Z., PyOD: A Python Toolbox for Scalable Outlier Detection. *Journal of Machine Learning Research*, 20, pp.1-7. .. [#Zhao2020Automating] Zhao, Y., Rossi, R.A. and Akoglu, L., 2021. Automatic Unsupervised Outlier Model Selection. *Advances in Neural Information Processing Systems*. .. [#Zhao2021SUOD] Zhao, Y., Hu, X., Cheng, C., Wang, C., Wan, C., Wang, W., Yang, J., Bai, H., Li, Z., Xiao, C. and Wang, Y., 2021. SUOD: Accelerating Large-scale Unsupervised Heterogeneous Outlier Detection. *Proceedings of Machine Learning and Systems (MLSys)*. .. [#Zhao2021TOD] Zhao, Y., Chen, G.H. and Jia, Z., 2021. TOD: Tensor-based Outlier Detection. arXiv preprint arXiv:2110.14007. .. [#Zhang2025LogSAD] Zhang, J., Wang, G., Jin, Y. and Huang, D., 2025. Towards Training-free Anomaly Detection with Vision and Language Foundation Models. In *CVPR 2025*. .. [#Zhou2019AnomalyNet] Zhou, J.T., Du, J., Zhu, H., Peng, X., Liu, Y. and Goh, R.S.M., 2019. AnomalyNet: An anomaly detection network for video surveillance. *IEEE Transactions on Information Forensics and Security*. .. [#Zhou2021Feature] Zhou, Y., Song, X., Zhang, Y., Liu, F., Zhu, C., & Liu, L. (2021). Feature encoding with autoencoders for weakly supervised anomaly detection. IEEE Transactions on Neural Networks and Learning Systems, 33(6), 2454-2465. .. [#Zhu2019Tripartite] Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. *IEEE Access*. .. [#Zimek2012A] Zimek, A., Schubert, E. and Kriegel, H.P., 2012. A survey on unsupervised outlier detection in high‐dimensional numerical data. *Statistical Analysis and Data Mining: The ASA Data Science Journal*\ , 5(5), pp.363-387. .. [#Zimek2014Ensembles] Zimek, A., Campello, R.J. and Sander, J., 2014. Ensembles for unsupervised outlier detection: challenges and research questions a position paper. *ACM Sigkdd Explorations Newsletter*\ , 15(1), pp.11-22. .. [#Zong2018Deep] Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D. and Chen, H., 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. International Conference on Learning Representations (ICLR). .. [#Wang2023Drift] Wang, C., Zhuang, Z., Qi, Q., Wang, J., Wang, X., Sun, H., & Liao, J. (2023). Drift doesn't matter: dynamic decomposition with diffusion reconstruction for unstable multivariate time series anomaly detection. Advances in Neural Information Processing Systems, 36.
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Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
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Anomaly Detection Learning Resources ==================================== .. image:: https://img.shields.io/github/stars/yzhao062/anomaly-detection-resources.svg :target: https://github.com/yzhao062/anomaly-detection-resources/stargazers :alt: GitHub stars .. image:: https://img.shields.io/github/forks/yzhao062/anomaly-detection-resources.svg?color=blue :target: https://github.com/yzhao062/anomaly-detection-resources/network :alt: GitHub forks .. image:: https://img.shields.io/github/license/yzhao062/anomaly-detection-resources.svg?color=blue :target: https://github.com/yzhao062/anomaly-detection-resources/blob/master/LICENSE :alt: License .. image:: https://awesome.re/badge-flat2.svg :target: https://awesome.re/badge-flat2.svg :alt: Awesome .. image:: https://img.shields.io/badge/ADBench-benchmark_results-pink :target: https://github.com/Minqi824/ADBench :alt: Benchmark ---- `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 @ (yzhao010@usc.edu). 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>`_,. ---- 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>`_ ---- 1. Books & Tutorials & Benchmarks --------------------------------- 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>`_ ---- 2. Courses/Seminars/Videos -------------------------- **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/>`_ ---- 3. Toolbox & Datasets --------------------- [**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 ---- 4. Papers --------- 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/Papers/LOF.pdf>`_ IForest Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_ OCSVM Estimating the support of a high-dimensional distribution Neural Computation 2001 [#Scholkopf2001Estimating]_ `[PDF] <http://users.cecs.anu.edu.au/~williams/papers/P132.pdf>`_ AutoEncoder Ensemble Outlier detection with autoencoder ensembles SDM 2017 [#Chen2017Outlier]_ `[PDF] <http://saketsathe.net/downloads/autoencode.pdf>`_ COPOD COPOD: Copula-Based Outlier Detection ICDM 2020 [#Li2020COPOD]_ `[PDF] <https://arxiv.org/abs/2009.09463>`_ ECOD Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions TKDE 2022 [#Li2021ECOD]_ `[PDF] <https://arxiv.org/abs/2201.00382>`_ ==================== ================================================================================================= ================================= ===== =========================== ============================================================================================================================================================================================== 4.16. Overview & Survey Papers ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Papers are sorted by the publication year. ====================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ====================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== A survey of outlier detection methodologies ARTIF INTELL REV 2004 [#Hodge2004A]_ `[PDF] <https://www-users.cs.york.ac.uk/vicky/myPapers/Hodge+Austin_OutlierDetection_AIRE381.pdf>`_ Anomaly detection: A survey CSUR 2009 [#Chandola2009Anomaly]_ `[PDF] <https://www.vs.inf.ethz.ch/edu/HS2011/CPS/papers/chandola09_anomaly-detection-survey.pdf>`_ A meta-analysis of the anomaly detection problem Preprint 2015 [#Emmott2015A]_ `[PDF] <https://arxiv.org/pdf/1503.01158.pdf>`_ On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study DMKD 2016 [#Campos2016On]_ `[HTML] <https://link.springer.com/article/10.1007/s10618-015-0444-8>`_, `[SLIDES] <https://imada.sdu.dk/~zimek/InvitedTalks/TUVienna-2016-05-18-outlier-evaluation.pdf>`_ A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data PLOS ONE 2016 [#Goldstein2016A]_ `[PDF] <http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0152173&type=printable>`_ A comparative evaluation of outlier detection algorithms: Experiments and analyses Pattern Recognition 2018 [#Domingues2018A]_ `[PDF] <https://www.researchgate.net/publication/320025854_A_comparative_evaluation_of_outlier_detection_algorithms_Experiments_and_analyses>`_ Research Issues in Outlier Detection Book Chapter 2019 [#Suri2019Research]_ `[HTML] <https://link.springer.com/chapter/10.1007/978-3-030-05127-3_3>`_ Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection SAC 2019 [#Falcao2019Quantitative]_ `[HTML] <https://dl.acm.org/citation.cfm?id=3297314>`_ Progress in Outlier Detection Techniques: A Survey IEEE Access 2019 [#Wang2019Progress]_ `[PDF] <https://ieeexplore.ieee.org/iel7/6287639/8600701/08786096.pdf>`_ Deep learning for anomaly detection: A survey Preprint 2019 [#Chalapathy2019Deep]_ `[PDF] <https://arxiv.org/pdf/1901.03407.pdf>`_ Anomalous Instance Detection in Deep Learning: A Survey Tech Report 2020 [#Bulusu2020Deep]_ `[PDF] <https://arxiv.org/pdf/2003.06979.pdf>`_ Anomaly detection in univariate time-series: A survey on the state-of-the-art Preprint 2020 [#Braei2020Anomaly]_ `[PDF] <https://arxiv.org/pdf/2004.00433.pdf>`_ Deep Learning for Anomaly Detection: A Review CSUR 2021 [#Pang2020Deep]_ `[PDF] <https://arxiv.org/pdf/2007.02500.pdf>`_ A Comprehensive Survey on Graph Anomaly Detection with Deep Learning TKDE 2021 [#Ma2021A]_ `[PDF] <https://arxiv.org/pdf/2106.07178.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>`_ A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges Preprint 2021 [#Salehi2021A]_ `[PDF] <https://arxiv.org/pdf/2110.14051.pdf>`_ Self-Supervised Anomaly Detection: A Survey and Outlook Preprint 2022 [#Hojjati2022Self]_ `[PDF] <https://arxiv.org/pdf/2205.05173.pdf>`_ Weakly supervised anomaly detection: A survey Preprint 2023 [#Jiang2023weakly]_ `[PDF] <https://arxiv.org/abs/2302.04549>`_, `[PDF] <https://github.com/yzhao062/wsad>`_ 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>`_ Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey Preprint 2024 [#Xu2024LLMsurvey]_ `[PDF] <https://arxiv.org/abs/2409.01980>`_ ====================================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== 4.17. Isolation-Based Methods ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== Isolation forest ICDM 2008 [#Liu2008Isolation]_ `[PDF] <https://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf>`_ Isolation‐based anomaly detection using nearest‐neighbor ensembles Computational Intelligence 2018 [#Bandaragoda2018Isolation]_ `[PDF] <https://www.researchgate.net/publication/322359651_Isolation-based_anomaly_detection_using_nearest-neighbor_ensembles_iNNE>`_, `[Code] <https://github.com/zhuye88/iNNE>`_ Extended Isolation Forest TKDE 2019 [#Hariri2019Extended]_ `[PDF] <https://arxiv.org/pdf/1811.02141.pdf>`_, `[Code] <https://github.com/sahandha/eif>`_ Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection KDD 2020 [#Ting2020Isolation]_ `[PDF] <https://arxiv.org/pdf/2009.12196.pdf>`_, `[Code] <https://github.com/IsolationKernel/Codes/tree/main/IDK>`_ Deep Isolation Forest for Anomaly Detection TKDE 2023 [#Xu2023Deep]_ `[PDF] <https://arxiv.org/abs/2206.06602>`_, `[Code] <https://github.com/xuhongzuo/deep-iforest>`_ ================================================================================================= ============================ ===== ============================= ============================================================================================================================================================================================== 4.18. Fairness and Bias in Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== A Framework for Determining the Fairness of Outlier Detection ECAI 2020 [#Davidson2020A]_ `[PDF] <https://web.cs.ucdavis.edu/~davidson/Publications/TR.pdf>`_ FAIROD: Fairness-aware Outlier Detection AIES 2021 [#Shekhar2021FAIROD]_ `[PDF] <https://arxiv.org/pdf/2012.03063.pdf>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.19. Outlier Detection Applications ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Field Paper Title Venue Year Ref Materials ======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== **Security** A survey of distance and similarity measures used within network intrusion anomaly detection IEEE Commun. Surv. Tutor. 2015 [#WellerFahy2015A]_ `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6853338>`_ **Security** Anomaly-based network intrusion detection: Techniques, systems and challenges Computers & Security 2009 [#GarciaTeodoro2009Anomaly]_ `[PDF] <https://www2.cs.uh.edu/~acl/cs6397/Doc/2009-Elsevier-Anomaly-based%20network%20intrusion%20detection.pdf>`_ **Finance** A survey of anomaly detection techniques in financial domain Future Gener Comput Syst 2016 [#Ahmed2016A]_ `[PDF] <https://www.sciencedirect.com/science/article/abs/pii/S0167739X15000023>`_ **Traffic** Outlier Detection in Urban Traffic Data WIMS 2018 [#Djenouri2018Outlier]_ `[PDF] <http://dss.sdu.dk/assets/fpd-lof/outlier-detection-urban.pdf>`_ **Social Media** A survey on social media anomaly detection SIGKDD Explorations 2016 [#Yu2016A]_ `[PDF] <https://arxiv.org/pdf/1601.01102.pdf>`_ **Social Media** GLAD: group anomaly detection in social media analysis TKDD 2015 [#Yu2015Glad]_ `[PDF] <https://arxiv.org/pdf/1410.1940.pdf>`_ **Machine Failure** Detecting the Onset of Machine Failure Using Anomaly Detection Methods DAWAK 2019 [#Riazi2019Detecting]_ `[PDF] <https://webdocs.cs.ualberta.ca/~zaiane/postscript/DAWAK19.pdf>`_ **Video Surveillance** AnomalyNet: An anomaly detection network for video surveillance TIFS 2019 [#Zhou2019AnomalyNet]_ `[IEEE] <https://ieeexplore.ieee.org/document/8649753>`_, `Code <https://github.com/joeyzhouty/AnomalyNet>`_ ======================== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.20. Outlier Detection in Other fields ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Field Paper Title Venue Year Ref Materials ============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== **Text** Outlier detection for text data SDM 2017 [#Kannan2017Outlier]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611974973.55>`_ ============== ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.21. Interactive Outlier Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback SDM 2019 [#Lamba2019Learning]_ `[PDF] <https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.69>`_ Interactive anomaly detection on attributed networks WSDM 2019 [#Ding2019Interactive]_ `[PDF] <http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf>`_ eX2: a framework for interactive anomaly detection IUI Workshop 2019 [#Arnaldo2019ex2]_ `[PDF] <http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf>`_ Tripartite Active Learning for Interactive Anomaly Discovery IEEE Access 2019 [#Zhu2019Tripartite]_ `[PDF] <https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963>`_ ================================================================================================= ============================ ===== ============================ ========================================================================================================================================================================== 4.22. Active Anomaly Detection ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Paper Title Venue Year Ref Materials ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== Active learning for anomaly and rare-category detection NeurIPS 2005 [#Pelleg2005Active]_ `[PDF] <http://papers.nips.cc/paper/2554-active-learning-for-anomaly-and-rare-category-detection.pdf>`_ Outlier detection by active learning SIGKDD 2006 [#Abe2006Outlier]_ `[PDF] <https://www.researchgate.net/profile/Naoki_Abe2/publication/221653343_Outlier_detection_by_active_learning/links/5441464a0cf2e6f0c0f60abb.pdf>`_ Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Preprint 2019 [#Das2019Active]_ `[PDF] <https://arxiv.org/pdf/1901.08930.pdf>`_ Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning ICDM 2020 [#Zha2020Meta]_ `[PDF] <https://arxiv.org/pdf/2009.07415.pdf>`_ A3: Activation Anomaly Analysis ECML-PKDD 2020 [#Sperl2021A3]_ `[PDF] <https://arxiv.org/pdf/2003.01801>`_, `[Code] <https://github.com/Fraunhofer-AISEC/A3>`_ ================================================================================================== ============================ ===== ============================ ========================================================================================================================================================================== ---- 5. Key Conferences/Workshops/Journals ------------------------------------- 5.1. Conferences & Workshops ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Key data mining conference **deadlines**, **historical acceptance rates**, and more can be found `data-mining-conferences <https://github.com/yzhao062/data-mining-conferences>`_. `ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD) <http://www.kdd.org/conferences>`_. **Note**: SIGKDD usually has an Outlier Detection Workshop (ODD), see `ODD 2021 <https://oddworkshop.github.io/>`_. `ACM International Conference on Management of Data (SIGMOD) <https://sigmod.org/>`_ `The Web Conference (WWW) <https://www2018.thewebconf.org/>`_ `IEEE International Conference on Data Mining (ICDM) <https://icdm2024.org//>`_ `SIAM International Conference on Data Mining (SDM) <https://www.siam.org/Conferences/CM/Main/sdm19>`_ `IEEE International Conference on Data Engineering (ICDE) <https://icde2018.org/>`_ `ACM InternationalConference on Information and Knowledge Management (CIKM) <http://www.cikmconference.org/>`_ `ACM International Conference on Web Search and Data Mining (WSDM) <http://www.wsdm-conference.org/2018/>`_ `The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) <http://www.ecmlpkdd2018.org/>`_ `The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) <http://pakdd2019.medmeeting.org>`_ 5.2. Journals ^^^^^^^^^^^^^ `ACM Transactions on Knowledge Discovery from Data (TKDD) <https://tkdd.acm.org/>`_ `IEEE Transactions on Knowledge and Data Engineering (TKDE) <https://www.computer.org/web/tkde>`_ `ACM SIGKDD Explorations Newsletter <http://www.kdd.org/explorations>`_ `Data Mining and Knowledge Discovery <https://link.springer.com/journal/10618>`_ `Knowledge and Information Systems (KAIS) <https://link.springer.com/journal/10115>`_ ---- References ---------- .. [#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. .. [#Aggarwal2013Outlier] Aggarwal, C.C., 2013. Outlier ensembles: position paper. *ACM SIGKDD Explorations Newsletter*\ , 14(2), pp.49-58. .. [#Ahmed2016A] Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. 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[#Kriegel2010Outlier] Kriegel, H.P., Kröger, P. and Zimek, A., 2010. Outlier detection techniques. *Tutorial at ACM SIGKDD 2010*. .. [#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. .. [#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. .. [#Jiang2025MMAD] Jiang, X., Li, J., Deng, H., Liu, Y., Gao, B., Zhou, Y., Li, J., Wang, C. and Zheng, F., 2025. MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection. In *ICLR 2025*. .. [#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. .. [#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. .. [#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. .. [#Lazarevic2008Data] Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V. and Srivastava, J., 2008, September. Data mining for anomaly detection. *Tutorial at ECML PKDD 2008*. .. [#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. .. [#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. .. [#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. .. [#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. .. [#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. .. [#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. .. [#Liu2008Isolation] Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In *International Conference on Data Mining*\ , pp. 413-422. IEEE. .. [#Liu2018Clustering] Liu, H., Li, J., Wu, Y. and Fu, Y., 2019. Clustering with outlier removal. *IEEE transactions on knowledge and data engineering*. .. [#Liu2018Contextual] Liu, N., Shin, D. and Hu, X., 2017. Contextual outlier interpretation. In *International Joint Conference on Artificial Intelligence (IJCAI-18)*, pp.2461-2467. .. [#Liu2019Generative] Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M. and He, X., 2019. Generative Adversarial Active Learning for Unsupervised Outlier Detection. *IEEE transactions on knowledge and data engineering*. .. [#Li2020AutoOD] Li, Y., Chen, Z., Zha, D., Zhou, K., Jin, H., Chen, H. and Hu, X., 2020. AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning. *ICDE*. .. [#Liu2022Benchmarking] Liu, K., Dou, Y., Zhao, Y., Ding, X., Hu, X., Zhang, R., Ding, K., Chen, C., Peng, H., Shu, K., Sun, L., Li, J., Chen, G.H., Jia, Z., and Yu, P.S. 2022. Benchmarking Node Outlier Detection on Graphs. arXiv preprint arXiv:2206.10071. .. [#Liu2024Elephant] Liu, Q. and Paparrizos, J., 2024. The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark. *NeurIPS 2024 Datasets and Benchmarks Track*. .. [#Ma2021A] Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q.Z., Xiong, H. and Akoglu, L., 2021. A comprehensive survey on graph anomaly detection with deep learning. *IEEE Transactions on Knowledge and Data Engineering*. .. [#Macha2018Explaining] Macha, M. and Akoglu, L., 2018. Explaining anomalies in groups with characterizing subspace rules. Data Mining and Knowledge Discovery, 32(5), pp.1444-1480. .. [#Manzoor2018Outlier] Manzoor, E., Lamba, H. and Akoglu, L. Outlier Detection in Feature-Evolving Data Streams. In *24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD)*. 2018. .. [#Mendiratta2017Anomaly] Mendiratta, B.V., 2017. Anomaly Detection in Networks. *Tutorial at ACM SIGKDD 2017*. .. [#Micenkova2015Learning] Micenková, B., McWilliams, B. and Assent, I., 2015. Learning representations for outlier detection on a budget. arXiv preprint arXiv:1507.08104. .. [#Gupta2018Beyond] Gupta, N., Eswaran, D., Shah, N., Akoglu, L. and Faloutsos, C., Beyond Outlier Detection: LookOut for Pictorial Explanation. *ECML PKDD 2018*. .. [#Han2022Adbench] Han, S., Hu, X., Huang, H., Jiang, M. and Zhao, Y., 2022. ADBench: Anomaly Detection Benchmark. arXiv preprint arXiv:2206.09426. .. [#Pang2016Unsupervised] Pang, G., Cao, L., Chen, L. and Liu, H., 2016, December. Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings. In Data Mining (ICDM), 2016 IEEE 16th International Conference on (pp. 410-419). IEEE. .. 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[#Pang2023recent] Guansong Pang, Joey Tianyi Zhou, Radu Tudor Ionescu, Yu Tian, and Kihyuk Sohn. "Recent Advances in Anomaly Detection". In: *CVPR'23*. Vancouver, Canada. .. [#Park2025LLMTSAD] Park, J., Jung, K., Lee, D., Lee, H., Gwak, D., Park, C., Choo, J. and Cho, J., 2025. Delving into Large Language Models for Effective Time-Series Anomaly Detection. In *NeurIPS 2025*. .. [#Pelleg2005Active] Pelleg, D. and Moore, A.W., 2005. Active learning for anomaly and rare-category detection. In *Advances in neural information processing systems*\, pp. 1073-1080. .. [#Radovanovic2015Reverse] Radovanović, M., Nanopoulos, A. and Ivanović, M., 2015. Reverse nearest neighbors in unsupervised distance-based outlier detection. *IEEE transactions on knowledge and data engineering*, 27(5), pp.1369-1382. .. [#Ramaswamy2000Efficient] Ramaswamy, S., Rastogi, R. and Shim, K., 2000, May. Efficient algorithms for mining outliers from large data sets. *ACM SIGMOD Record*\ , 29(2), pp. 427-438. .. 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A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]. *ACM SIGKDD Explorations Newsletter*. 20. 13-23. .. [#Salehi2021A] Salehi, M., Mirzaei, H., Hendrycks, D., Li, Y., Rohban, M.H., Sabokrou, M., 2021. A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges. arXiv preprint arXiv:2110.14051. .. [#Scholkopf2001Estimating] Schölkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J. and Williamson, R.C., 2001. Estimating the support of a high-dimensional distribution. *Neural Computation*, 13(7), pp.1443-1471. .. [#Sehwag2021SSD] Sehwag, V., Chiang, M., Mittal, P., 2021. SSD: A Unified Framework for Self-Supervised Outlier Detection. *International Conference on Learning Representations (ICLR)*. .. [#Shekhar2021FAIROD] Shekhar, S., Shah, N. and Akoglu, L., 2021. FAIROD: Fairness-aware Outlier Detection. AAAI/ACM Conference on AI, Ethics, and Society (AIES). .. 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