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异常检测学习资源（Anomaly Detection Learning Resources）

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异常检测 (anomaly detection) (又名 Outlier Detection) 是一个重要但非常有挑战性的领域。异常检测的目标主要是找到数据中 偏离于主要分布的案例--它在很多领域都有重要意义，包括「信用卡诈骗检测」、「网络入侵检测」、 「机械故障检测」等。

这个仓库中收藏了关于异常检测的：

  1. 专业书籍与学术论文
  2. 在线课程与视频
  3. 异常检测数据集
  4. 开源与商业工具库
  5. 重要的会议与期刊

更多内容会被陆续添加到当前仓库中来。 请建议/推荐相关资源，你可以选择提交issue report、pull request或者给我发邮件 (zhaoy@cmu.edu)。 Enjoy reading！

目录

1. 书籍 & 教程

1.1. 书籍

Outlier Analysis 作者: Charu Aggarwal: 经典异常检测教科书，内容涵盖了大部分相关算法与应用。异常检测领域人士必读。 [预览.pdf]

Outlier Ensembles: An Introduction 作者: Charu Aggarwal and Saket Sathe: 非常权威的集成异常检测教科书。

Data Mining: Concepts and Techniques (3rd) 作者: 韩家炜 (Jiawei Han) and Micheline Kamber and Jian Pei (裴健): 该书第十二章讨论了异常检测技术。 [Google Search]

1.2. 教程

Tutorial Title Venue Year Ref Materials
Outlier detection techniques ACM SIGKDD 2010 1 [PDF]
Anomaly Detection: A Tutorial ICDM 2011 2 [PDF]
Data mining for anomaly detection PKDD 2008 3 [Video]

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] Scikit-learn Novelty and Outlier Detection. It supports some popular algorithms like LOF, Isolation Forest, and One-class SVM.

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

[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.

[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.

3.3. 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

4. Papers

4.1. Overview & Survey Papers

Paper Title Venue Year Ref Materials
A survey of outlier detection methodologies ARTIF INTELL REV 2004 4 [PDF]
Anomaly detection: A survey CSUR 2009 5 [PDF]
A meta-analysis of the anomaly detection problem Preprint 2015 6 [PDF]
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study DMKD 2016 7 [HTML], [SLIDES]
A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data PLOS ONE 2016 8 [PDF]
A comparative evaluation of outlier detection algorithms: Experiments and analyses Pattern Recognition 2018 9 [PDF]
Research Issues in Outlier Detection Book Chapter 2019 10 [HTML]
Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection SAC 2019 11 [HTML]

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 12 [PDF]
KNN Fast outlier detection in high dimensional spaces PKDD 2002 13 [PDF]
LOF LOF: identifying density-based local outliers ACM SIGMOD Record 2000 14 [PDF]
IForest Isolation forest ICDM 2008 15 [PDF]
OCSVM Estimating the support of a high-dimensional distribution Neural Computation 2001 16 [PDF]
AutoEncoder Ensemble Outlier detection with autoencoder ensembles SDM 2017 17 [PDF]

4.3. Graph & Network Outlier Detection

Paper Title Venue Year Ref Materials
Graph based anomaly detection and description: a survey DMKD 2015 18 [PDF]
Anomaly detection in dynamic networks: a survey WIREs Computational Statistic 2015 19 [PDF]

4.4. Time Series Outlier Detection

Paper Title Venue Year Ref Materials
Outlier detection for temporal data: A survey TKDE 2014 20 [PDF]
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 21 [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 22 [PDF]
Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection IJCAI 2017 23 [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

24

[HTML]
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection SIGKDD 2018 25 [PDF]
Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection TKDE 2015 26 [PDF], [SLIDES]
Outlier detection for high-dimensional data Biometrika 2015 27 [PDF]

4.7. Outlier Ensembles

Paper Title Venue Year Ref Materials
Outlier ensembles: position paper SIGKDD Explorations 2013 28 [PDF]
Ensembles for unsupervised outlier detection: challenges and research questions a position paper SIGKDD Explorations 2014 29 [PDF]
An Unsupervised Boosting Strategy for Outlier Detection Ensembles PAKDD 2018 30 [HTML]
LSCP: Locally selective combination in parallel outlier ensembles SDM 2019 31 [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 32 [PDF]
Unsupervised real-time anomaly detection for streaming data Neurocomputing 2017 33 [PDF]
Outlier Detection in Feature-Evolving Data Streams SIGKDD 2018 34 [PDF], [Github]

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 35 [PDF]
Learning representations for outlier detection on a budget Preprint 2015 36 [PDF]
XGBOD: improving supervised outlier detection with unsupervised representation learning IJCNN 2018 37 [PDF]

4.10. Interpretability

Paper Title Venue Year Ref Materials
Explaining Anomalies in Groups with Characterizing Subspace Rules DMKD 2018 38 [PDF]
Beyond Outlier Detection: LookOut for Pictorial Explanation ECML-PKDD 2018 39 [PDF]
Contextual outlier interpretation IJCAI 2018 40 [PDF]
Mining multidimensional contextual outliers from categorical relational data IDA 2015 41 [PDF]
Discriminative features for identifying and interpreting outliers ICDE 2014 42 [PDF]
Sequential Feature Explanations for Anomaly Detection TKDD 2019 43 [HTML]

4.11. Outlier Detection with Neural Networks

Paper Title Venue Year Ref Materials
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding KDD 2018 44 [PDF], [Code]
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks Preprint 2019 45 [PDF], [Code]
Generative Adversarial Active Learning for Unsupervised Outlier Detection TKDE 2019 46 [PDF], [Code]
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection ICLR 2018 47 [PDF], [Code]

4.12. Active Anomaly Detection

Paper Title Venue Year Ref Materials
Active learning for anomaly and rare-category detection NeurIPS 2005 48 [PDF]
Outlier detection by active learning SIGKDD 2006 49 [PDF]
Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability Preprint 2019 50 [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 51 [PDF]
Interactive anomaly detection on attributed networks WSDM 2019 52 [PDF]
eX2: a framework for interactive anomaly detection IUI Workshop 2019 53 [PDF]
Tripartite Active Learning for Interactive Anomaly Discovery IEEE Access 2019 54 [PDF]

4.14. Outlier Detection in Other fields

Field Paper Title Venue Year Ref Materials
Text Outlier detection for text data SDM 2017 55 [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 56 [PDF]
Security Anomaly-based network intrusion detection: Techniques, systems and challenges Computers & Security 2009 57 [PDF]
Finance A survey of anomaly detection techniques in financial domain Future Gener Comput Syst 2016 58 [PDF]
Traffic Outlier Detection in Urban Traffic Data WIMS 2018 59 [PDF]
Social Media A survey on social media anomaly detection SIGKDD Explorations 2016 60 [PDF]
Social Media GLAD: group anomaly detection in social media analysis TKDD 2015 61 [PDF]
Machine Failure Detecting the Onset of Machine Failure Using Anomaly Detection Methods DAWAK 2019 62 [PDF]

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 2018.

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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  2. Chawla, S. and Chandola, V., 2011, Anomaly Detection: A Tutorial. Tutorial at ICDM 2011.↩

  3. Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V. and Srivastava, J., 2008, September. Data mining for anomaly detection. Tutorial at ECML PKDD 2008.↩

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  6. Emmott, A., Das, S., Dietterich, T., Fern, A. and Wong, W.K., 2015. A meta-analysis of the anomaly detection problem. arXiv preprint arXiv:1503.01158.↩

  7. Campos, G.O., Zimek, A., Sander, J., Campello, R.J., Micenková, B., Schubert, E., Assent, I. and Houle, M.E., 2016. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30(4), pp.891-927.↩

  8. Goldstein, M. and Uchida, S., 2016. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one, 11(4), p.e0152173.↩

  9. Domingues, R., Filippone, M., Michiardi, P. and Zouaoui, J., 2018. A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition, 74, pp.406-421.↩

  10. Suri, N.R. and Athithan, G., 2019. Research Issues in Outlier Detection. In Outlier Detection: Techniques and Applications, pp. 29-51. Springer, Cham.↩

  11. Falcão, F., Zoppi, T., Silva, C.B.V., Santos, A., Fonseca, B., Ceccarelli, A. and Bondavalli, A., 2019, April. Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection. In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, (pp. 318-327). ACM.↩

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  13. Angiulli, F. and Pizzuti, C., 2002, August. Fast outlier detection in high dimensional spaces. In European Conference on Principles of Data Mining and Knowledge Discovery, pp. 15-27.↩

  14. Breunig, M.M., Kriegel, H.P., Ng, R.T. and Sander, J., 2000, May. LOF: identifying density-based local outliers. ACM SIGMOD Record, 29(2), pp. 93-104.↩

  15. Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In International Conference on Data Mining, pp. 413-422. IEEE.↩

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  17. Chen, J., Sathe, S., Aggarwal, C. and Turaga, D., 2017, June. Outlier detection with autoencoder ensembles. SIAM International Conference on Data Mining, pp. 90-98. Society for Industrial and Applied Mathematics.↩

  18. Akoglu, L., Tong, H. and Koutra, D., 2015. Graph based anomaly detection and description: a survey. Data Mining and Knowledge Discovery, 29(3), pp.626-688.↩

  19. Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C. and Samatova, N.F., 2015. Anomaly detection in dynamic networks: a survey. Wiley Interdisciplinary Reviews: Computational Statistics, 7(3), pp.223-247.↩

  20. Gupta, M., Gao, J., Aggarwal, C.C. and Han, J., 2014. Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering, 26(9), pp.2250-2267.↩

  21. Hundman, K., Constantinou, V., Laporte, C., Colwell, I. and Soderstrom, T., 2018, July. Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (pp. 387-395). ACM.↩

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  23. 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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  27. Ro, K., Zou, C., Wang, Z. and Yin, G., 2015. Outlier detection for high-dimensional data. Biometrika, 102(3), pp.589-599.↩

  28. Aggarwal, C.C., 2013. Outlier ensembles: position paper. ACM SIGKDD Explorations Newsletter, 14(2), pp.49-58.↩

  29. 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.↩

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  33. Ahmad, S., Lavin, A., Purdy, S. and Agha, Z., 2017. Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, pp.134-147.↩

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  52. Ding, K., Li, J. and Liu, H., 2019, January. Interactive anomaly detection on attributed networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 357-365. ACM.↩

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  54. Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. IEEE Access.↩

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  57. Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G. and Vázquez, E., 2009. Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers & Security, 28(1-2), pp.18-28.↩

  58. Ahmed, M., Mahmood, A.N. and Islam, M.R., 2016. A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, pp.278-288.↩

  59. Djenouri, Y. and Zimek, A., 2018, June. Outlier detection in urban traffic data. In Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics. ACM.↩

  60. 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.↩

  61. 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.↩

  62. Riazi, M., Zaiane, O., Takeuchi, T., Maltais, A., Günther, J. and Lipsett, M., Detecting the Onset of Machine Failure Using Anomaly Detection Methods.↩