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Anomaly Detection Learning Resources

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Outlier 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:

  1. Books & Academic Papers
  2. Online Courses and Videos
  3. Outlier Datasets
  4. Open-source and Commercial Libraries/Toolkits
  5. 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 @ (zhaoy@cmu.edu). Enjoy reading!

BTW, you may find my [GitHub] and [outlier detection papers] useful.


Table of Contents


1. Books & Tutorials

1.1. Books

Outlier Analysis 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]

Outlier Ensembles: An Introduction by Charu Aggarwal and Saket Sathe: Great intro book for ensemble learning in outlier analysis.

Data Mining: Concepts and Techniques (3rd) by Jiawei Han and Micheline Kamber and Jian Pei: Chapter 12 discusses outlier detection with many key points. [Google Search]

1.2. Tutorials

Tutorial Title Venue Year Ref Materials
Data mining for anomaly detection PKDD 2008 [42] [Video]
Outlier detection techniques ACM SIGKDD 2010 [38] [PDF]
Anomaly Detection: A Tutorial ICDM 2011 [19] [PDF]
Anomaly Detection in Networks KDD 2017 [53] [Page]
Which Anomaly Detector should I use? ICDM 2018 [75] [PDF]
Deep Learning for Anomaly Detection KDD 2020 [79] [HTML], [Video]
Deep Learning for Anomaly Detection WSDM 2021 [60] [HTML]

2. Courses/Seminars/Videos

Coursera Introduction to Anomaly Detection (by IBM): [See Video]

Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic: [See Video]

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

Udemy Outlier Detection Algorithms in Data Mining and Data Science: [See Video]

Stanford Data Mining for Cyber Security also covers part of anomaly detection techniques: [See Video]


3. Toolbox & Datasets

3.1. Multivariate Data

[Python] Python Outlier Detection (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] Python Streaming Anomaly Detection (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. It supports some popular algorithms like LOF, Isolation Forest, and One-class SVM.

[Python] Scalable Unsupervised Outlier Detection (SUOD): SUOD (Scalable Unsupervised Outlier Detection) is an acceleration framework for large-scale unsupervised outlier detector training and prediction, on top of PyOD.

[Java] ELKI: Environment for Developing KDD-Applications Supported by Index-Structures: 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: 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: This CRAN task view contains a list of packages that can be used for anomaly detection with R.

[R] outliers package: A collection of some tests commonly used for identifying outliers in R.

[Matlab] Anomaly Detection Toolbox - Beta: A collection of popular outlier detection algorithms in Matlab.

3.2. Time series outlier detection

[Python] TODS: TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.

[Python] skyline: Skyline is a near real time anomaly detection system.

[Python] banpei: Banpei is a Python package of the anomaly detection.

[Python] telemanom: A framework for using LSTMs to detect anomalies in multivariate time series data.

[Python] 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: NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications.

[R] CRAN Task View: Anomaly Detection with R: This CRAN task view contains a list of packages that can be used for anomaly detection with R.

[R] 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: The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.

3.3. Real-time Elasticsearch

[Open Distro] Real Time Anomaly Detection in Open Distro for Elasticsearch by Amazon: A machine learning-based anomaly detection plugins for Open Distro for Elasticsearch. See Real Time Anomaly Detection in Open Distro for Elasticsearch.

[Python] datastream.io: An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.

3.4. Datasets

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

4.1. 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 [35] [PDF]
Anomaly detection: A survey CSUR 2009 [18] [PDF]
A meta-analysis of the anomaly detection problem Preprint 2015 [27] [PDF]
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study DMKD 2016 [13] [HTML], [SLIDES]
A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data PLOS ONE 2016 [31] [PDF]
A comparative evaluation of outlier detection algorithms: Experiments and analyses Pattern Recognition 2018 [26] [PDF]
Research Issues in Outlier Detection Book Chapter 2019 [73] [HTML]
Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection SAC 2019 [29] [HTML]
Progress in Outlier Detection Techniques: A Survey IEEE Access 2019 [78] [PDF]
Deep learning for anomaly detection: A survey Preprint 2019 [17] [PDF]
Anomalous Instance Detection in Deep Learning: A Survey Tech Report 2020 [12] [PDF]
Anomaly detection in univariate time-series: A survey on the state-of-the-art Preprint 2020 [10] [PDF]
Deep Learning for Anomaly Detection: A Review CSUR 2021 [59] [PDF]
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning Preprint 2021 [50] [PDF]
Revisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [39] [PDF], [Code]

4.2. Key Algorithms

Abbreviation Paper Title Venue Year Ref Materials
kNN Efficient algorithms for mining outliers from large data sets ACM SIGMOD Record 2000 [63] [PDF]
KNN Fast outlier detection in high dimensional spaces PKDD 2002 [6] [PDF]
LOF LOF: identifying density-based local outliers ACM SIGMOD Record 2000 [11] [PDF]
IForest Isolation forest ICDM 2008 [45] [PDF]
OCSVM Estimating the support of a high-dimensional distribution Neural Computation 2001 [69] [PDF]
AutoEncoder Ensemble Outlier detection with autoencoder ensembles SDM 2017 [20] [PDF]
COPOD COPOD: Copula-Based Outlier Detection ICDM 2020 [44] [PDF]

4.3. Graph & Network Outlier Detection

Paper Title Venue Year Ref Materials
Graph based anomaly detection and description: a survey DMKD 2015 [5] [PDF]
Anomaly detection in dynamic networks: a survey WIREs Computational Statistic 2015 [64] [PDF]
Outlier detection in graphs: On the impact of multiple graph models ComSIS 2019 [15] [PDF]
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning Preprint 2021 [50] [PDF]

4.4. Time Series Outlier Detection

Paper Title Venue Year Ref Materials
Outlier detection for temporal data: A survey TKDE 2014 [32] [PDF]
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [36] [PDF], [Code]
Time-Series Anomaly Detection Service at Microsoft KDD 2019 [65] [PDF]
Revisiting Time Series Outlier Detection: Definitions and Benchmarks NeurIPS 2021 [39] [PDF], [Code]

4.5. 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 [56] [PDF]
Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection IJCAI 2017 [57] [PDF]

4.6. 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 [94] [HTML]
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 [58] [PDF]
Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection TKDE 2015 [62] [PDF], [SLIDES]
Outlier detection for high-dimensional data Biometrika 2015 [67] [PDF]

4.7. Outlier Ensembles

Paper Title Venue Year Ref Materials
Outlier ensembles: position paper SIGKDD Explorations 2013 [2] [PDF]
Ensembles for unsupervised outlier detection: challenges and research questions a position paper SIGKDD Explorations 2014 [95] [PDF]
An Unsupervised Boosting Strategy for Outlier Detection Ensembles PAKDD 2018 [14] [HTML]
LSCP: Locally selective combination in parallel outlier ensembles SDM 2019 [88] [PDF]

4.8. 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 [68] [PDF]
Unsupervised real-time anomaly detection for streaming data Neurocomputing 2017 [4] [PDF]
Outlier Detection in Feature-Evolving Data Streams SIGKDD 2018 [52] [PDF], [Github]
Evaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark ICMLA 2015 [41] [PDF], [Github]
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams AAAI 2020 [9] [PDF], [Github]
NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing VLDB 2019 [82] [PDF], [Github], [Slide]
Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping KDD 2020 [83] [PDF], [Github], [Slide]

4.9. 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 [58] [PDF]
Learning representations for outlier detection on a budget Preprint 2015 [54] [PDF]
XGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 [87] [PDF]

4.10. Interpretability

Paper Title Venue Year Ref Materials
Explaining Anomalies in Groups with Characterizing Subspace Rules DMKD 2018 [51] [PDF]
Beyond Outlier Detection: LookOut for Pictorial Explanation ECML-PKDD 2018 [55] [PDF]
Contextual outlier interpretation IJCAI 2018 [47] [PDF]
Mining multidimensional contextual outliers from categorical relational data IDA 2015 [74] [PDF]
Discriminative features for identifying and interpreting outliers ICDE 2014 [21] [PDF]
Sequential Feature Explanations for Anomaly Detection TKDD 2019 [72] [HTML]
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network WWW 2021 [81] [PDF]

4.11. Outlier Detection with Neural Networks

Paper Title Venue Year Ref Materials
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 [36] [PDF], [Code]
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks ICANN 2019 [43] [PDF], [Code]
Generative Adversarial Active Learning for Unsupervised Outlier Detection TKDE 2019 [48] [PDF], [Code]
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection ICLR 2018 [96] [PDF], [Code]
Deep Anomaly Detection with Outlier Exposure ICLR 2019 [34] [PDF], [Code]
Unsupervised Anomaly Detection With LSTM Neural Networks TNNLS 2019 [28] [PDF], [IEEE],
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network NeurIPS 2019 [77] [PDF] [Code]

4.12. Active Anomaly Detection

Paper Title Venue Year Ref Materials
Active learning for anomaly and rare-category detection NeurIPS 2005 [61] [PDF]
Outlier detection by active learning SIGKDD 2006 [1] [PDF]
Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Preprint 2019 [22] [PDF]
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning ICDM 2020 [86] [PDF]

4.13. Interactive Outlier Detection

Paper Title Venue Year Ref Materials
Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback SDM 2019 [40] [PDF]
Interactive anomaly detection on attributed networks WSDM 2019 [24] [PDF]
eX2: a framework for interactive anomaly detection IUI Workshop 2019 [7] [PDF]
Tripartite Active Learning for Interactive Anomaly Discovery IEEE Access 2019 [93] [PDF]

4.14. Outlier Detection in Other fields

Field Paper Title Venue Year Ref Materials
Text Outlier detection for text data SDM 2017 [37] [PDF]

4.15. 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 [80] [PDF]
Security Anomaly-based network intrusion detection: Techniques, systems and challenges Computers & Security 2009 [30] [PDF]
Finance A survey of anomaly detection techniques in financial domain Future Gener Comput Syst 2016 [3] [PDF]
Traffic Outlier Detection in Urban Traffic Data WIMS 2018 [25] [PDF]
Social Media A survey on social media anomaly detection SIGKDD Explorations 2016 [85] [PDF]
Social Media GLAD: group anomaly detection in social media analysis TKDD 2015 [84] [PDF]
Machine Failure Detecting the Onset of Machine Failure Using Anomaly Detection Methods DAWAK 2019 [66] [PDF]
Video Surveillance AnomalyNet: An anomaly detection network for video surveillance TIFS 2019 [92] [IEEE], Code

4.16. Automated Outlier Detection

Paper Title Venue Year Ref Materials
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning ICDE 2020 [49] [PDF]
Automating Outlier Detection via Meta-Learning Preprint 2020 [90] [PDF], [Code]

4.17. 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 [89] [PDF], [Code]
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection MLSys 2021 [91] [PDF], [Code]

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 [23] [PDF]
FAIROD: Fairness-aware Outlier Detection AIES 2021 [71] [PDF]

4.19. Isolation-Based Methods

Paper Title Venue Year Ref Materials
Isolation forest ICDM 2008 [45] [PDF]
Isolation‐based anomaly detection using nearest‐neighbor ensembles Computational Intelligence 2018 [8] [PDF], [Code]
Extended Isolation Forest TKDE 2019 [33] [PDF], [Code]
Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection KDD 2020 [76] [PDF], [Code]

4.20. Emerging and Interesting Topics

Paper Title Venue Year Ref Materials
Clustering with Outlier Removal Preprint 2018 [46] [PDF]
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning IEEE Trans. Ind. Informat. 2020 [16] [PDF]
SSD: A Unified Framework for Self-Supervised Outlier Detection ICLR 2021 [70] [PDF], [Code]

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.

ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD). Note: SIGKDD usually has an Outlier Detection Workshop (ODD), see ODD 2021.

ACM International Conference on Management of Data (SIGMOD)

The Web Conference (WWW)

IEEE International Conference on Data Mining (ICDM)

SIAM International Conference on Data Mining (SDM)

IEEE International Conference on Data Engineering (ICDE)

ACM InternationalConference on Information and Knowledge Management (CIKM)

ACM International Conference on Web Search and Data Mining (WSDM)

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

5.2. Journals

ACM Transactions on Knowledge Discovery from Data (TKDD)

IEEE Transactions on Knowledge and Data Engineering (TKDE)

ACM SIGKDD Explorations Newsletter

Data Mining and Knowledge Discovery

Knowledge and Information Systems (KAIS)


References

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[55]Gupta, N., Eswaran, D., Shah, N., Akoglu, L. and Faloutsos, C., Beyond Outlier Detection: LookOut for Pictorial Explanation. ECML PKDD 2018.
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[57]Pang, G., Cao, L., Chen, L. and Liu, H., 2017, August. Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 2585-2591). AAAI Press.
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