Anomaly Detection Learning Resources

Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, aiming to identify outlying data objects. It has been proven critical in many fields, such as credit card fraud analytics, network intrusion detection, and mechanical unit defect detection.

In this repository, you could find:

Books & Academic Papers
Learning Materials, e.g., online courses and videos
Outlier Datasets
Open-source Libraries & Demo Codes
Paper Downloader (under development): a Python script to download open access papers listed in this repository.

I would continue adding more items to the repository. Please feel free to suggest some critical materials by opening an issue report, submitting a pull request, or dropping me an email @ (yuezhao@cs.toronto.edu). Enjoy reading!

1. Books & Tutorials
- 1.1. Books
- 1.2. Tutorials
2. Courses/Seminars/Videos
3. Toolbox & Datasets
4. Papers
5. Key Conferences/Workshops/Journals
- 5.1. Conferences & Workshops
- 5.2. Journals

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 Micheline Kamber Jian Pei: Chapter 12 discusses outlier detection with many fundamental points. [Google Search]

1.2. Tutorials

Tutorial Title	Place	Year	Ref	Materials
Outlier detection techniques	ACM SIGKDD 2010	2010	[15]	[PDF]
Anomaly Detection: A Tutorial	ICDM 2011	2011	[9]	[PDF]
Data mining for anomaly detection	PKDD 2008	2008	[16]	[See Video]

2. Courses/Seminars/Videos

Coursera Introduction to Anomaly Detection (by IBM): https://www.coursera.org/learn/ai/lecture/ASPv0/introduction-to-anomaly-detection

Coursera Real-Time Cyber Threat Detection and Mitigation partly covers the topic: https://www.coursera.org/learn/real-time-cyber-threat-detection

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

Udemy Outlier Detection Algorithms in Data Mining and Data Science: https://www.udemy.com/outlier-detection-techniques/

Stanford Data Mining for Cyber Security also covers part of anomaly detection techniques. http://web.stanford.edu/class/cs259d/

3. Toolbox & Datasets

3.1. Python

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

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.

3.2. Matlab

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

3.3. 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.

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.

3.4. Time series outlier detection

3.5. 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	Year	Ref	Materials
Anomaly detection: A survey	2009	[8]	[PDF]
A survey of outlier detection methodologies	2004	[13]	[PDF]
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study	2016	[6]	[HTML], [SLIDES]
Outlier detection: applications and techniques	2012	[26]	[PDF]
A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data	2016	[12]	[PDF]

4.2. Key Algorithms

Abbreviation	Paper Title	Year	Ref	Materials
kNN	Efficient algorithms for mining outliers from large data sets	2000	[24]	[PDF]
KNN	Fast outlier detection in high dimensional spaces	2002	[4]	[HTML]
LOF	LOF: identifying density-based local outliers	2000	[5]	[PDF]
IForest	Isolation forest	2008	[17]	[PDF]
OCSVM	Time-series novelty detection using one-class support vector machines	2003	[19]	[PDF]
AutoEncoder Ensemble	Outlier detection with autoencoder ensembles	2017	[14]	[PDF]

4.3. Graph & Network Outlier Detection

Paper Title	Year	Ref	Materials
Graph based anomaly detection and description: a survey	2015	[3]	[PDF]
Anomaly detection in dynamic networks: a survey	2015	[25]	[PDF]

4.4. Time Series Outlier Detection

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. [PDF]

4.5. Feature Selection in Outlier Detection

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. [PDF]

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. [PDF]

4.6. High-dimensional & Subspace Outliers

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. [Downloadable Link]

Pang, G., Cao, L., Chen, L. and Liu, H., 2018. Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection. In 24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD). 2018. [PDF]

4.7. Outlier Ensembles

Paper Title	Year	Ref	Materials
Outlier ensembles: position paper	2013	[1]	[PDF]
Ensembles for unsupervised outlier detection: challenges and research questions a position paper	2014	[30]	[PDF]
An Unsupervised Boosting Strategy for Outlier Detection Ensembles	2018	[7]	[HTML]

4.8. Outlier Detection in Evolving Data

Salehi, Mahsa & Rashidi, Lida. (2018). A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]. ACM SIGKDD Explorations Newsletter. 20. 13-23. [PDF]

Emaad Manzoor, Hemank Lamba, Leman Akoglu. Outlier Detection in Feature-Evolving Data Streams. In 24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD). 2018. [PDF] [Github]

4.9. Representation Learning in Outlier Detection

Paper Title	Year	Ref	Materials
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection	2018	[23]	[PDF]
Learning representations for outlier detection on a budget	2015	[21]	[PDF]
XGBOD: improving supervised outlier detection with unsupervised representation learning	2018	[29]	[PDF]

4.10. Interpretability

Paper Title	Year	Ref	Materials
Explaining Anomalies in Groups with Characterizing Subspace Rules	2018	[20]	[PDF]
Beyond Outlier Detection: LookOut for Pictorial Explanation	2018	[22]	[PDF]
Contextual outlier interpretation	2018	[18]	[PDF]
Mining multidimensional contextual outliers from categorical relational data	2015	[27]	[PDF]
Discriminative features for identifying and interpreting outliers	2014	[10]	[PDF]

4.11. Social Media Anomaly Detection

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. [PDF]

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. [PDF]

4.12. Outlier Detection in Other fields

Kannan, R., Woo, H., Aggarwal, C.C. and Park, H., 2017, June. Outlier detection for text data. In Proceedings of the 2017 SIAM International Conference on Data Mining (pp. 489-497). Society for Industrial and Applied Mathematics. [PDF]

4.13. Outlier Detection Applications

Field	Paper Title	Year	Ref	Materials
Security	A survey of distance and similarity measures used within network intrusion anomaly detection	2015	[28]	[PDF]
Security	Anomaly-based network intrusion detection: Techniques, systems and challenges	2009	[11]	[PDF]
Finance	A survey of anomaly detection techniques in financial domain	2016	[2]	[PDF]

5. Key Conferences/Workshops/Journals

5.1. Conferences & Workshops

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

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

[2]	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.

[3]	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.

[4]	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.

[5]	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.

[6]	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.

[7]	Campos, G.O., Zimek, A. and Meira, W., 2018, June. An Unsupervised Boosting Strategy for Outlier Detection Ensembles. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 564-576). Springer, Cham.

[8]	Chandola, V., Banerjee, A. and Kumar, V., 2009. Anomaly detection: A survey. ACM computing surveys , 41(3), p.15.

[9]	Chawla, S. and Chandola, V., 2011, Anomaly Detection: A Tutorial. Tutorial at ICDM 2011.

[10]	Dang, X.H., Assent, I., Ng, R.T., Zimek, A. and Schubert, E., 2014, March. Discriminative features for identifying and interpreting outliers. In International Conference on Data Engineering (ICDE). IEEE.

[11]	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.

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

[13]	Hodge, V. and Austin, J., 2004. A survey of outlier detection methodologies. Artificial intelligence review, 22(2), pp.85-126.

[14]	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.

[15]	Kriegel, H.P., Kröger, P. and Zimek, A., 2010. Outlier detection techniques. Tutorial at ACM SIGKDD 2010.

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

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

[18]	Liu, N., Shin, D. and Hu, X., 2017. Contextual outlier interpretation. In International Joint Conference on Artificial Intelligence (IJCAI-18), pp.2461-2467.

[19]	Ma, J. and Perkins, S., 2003, July. Time-series novelty detection using one-class support vector machines. In IJCNN' 03, pp. 1741-1745. IEEE.

[20]	Macha, M. and Akoglu, L., 2018. Explaining anomalies in groups with characterizing subspace rules. Data Mining and Knowledge Discovery, 32(5), pp.1444-1480.

[21]	Micenková, B., McWilliams, B. and Assent, I., 2015. Learning representations for outlier detection on a budget. arXiv preprint arXiv:1507.08104.

[22]	Gupta, N., Eswaran, D., Shah, N., Akoglu, L. and Faloutsos, C., Beyond Outlier Detection: LookOut for Pictorial Explanation. ECML PKDD 2018.

[23]	Pang, G., Cao, L., Chen, L. and Liu, H., 2018. Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection. In 24th ACM SIGKDD International Conference on Knowledge Discovery and Data mining (KDD). 2018.

[24]	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).

[25]	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.

[26]	Singh, K., & Upadhyaya, S. (2012). Outlier detection: applications and techniques. International Journal of Computer Science Issues (IJCSI), 9(1), 307.

[27]	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.

[28]	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.

[29]	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.

[30]	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.

[31]

SunrinZ/anomaly-detection-resources