Anomaly Detection Learning Resources

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:

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 @ (zhaoy@cmu.edu). Enjoy reading!

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

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	¹	[Video]
Outlier detection techniques	ACM SIGKDD	2010	²	[PDF]
Anomaly Detection: A Tutorial	ICDM	2011	³	[PDF]
Anomaly Detection in Networks	KDD	2017	⁴	[Page]
Which Anomaly Detector should I use?	ICDM	2018	⁵	[PDF]
Deep Learning for Anomaly Detection	KDD	2020	⁶	[HTML], [Video]
Deep Learning for Anomaly Detection	WSDM	2021	⁷	[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	⁸	[PDF]
Anomaly detection: A survey	CSUR	2009	⁹	[PDF]
A meta-analysis of the anomaly detection problem	Preprint	2015	¹⁰	[PDF]
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study	DMKD	2016	¹¹	[HTML], [SLIDES]
A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data	PLOS ONE	2016	¹²	[PDF]
A comparative evaluation of outlier detection algorithms: Experiments and analyses	Pattern Recognition	2018	¹³	[PDF]
Research Issues in Outlier Detection	Book Chapter	2019	¹⁴	[HTML]
Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection	SAC	2019	¹⁵	[HTML]
Progress in Outlier Detection Techniques: A Survey	IEEE Access	2019	¹⁶	[PDF]
Deep learning for anomaly detection: A survey	Preprint	2019	¹⁷	[PDF]
Anomalous Instance Detection in Deep Learning: A Survey	Tech Report	2020	¹⁸	[PDF]
Anomaly detection in univariate time-series: A survey on the state-of-the-art	Preprint	2020	¹⁹	[PDF]
Deep Learning for Anomaly Detection: A Review	CSUR	2021	²⁰	[PDF]
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning	Preprint	2021	²¹	[PDF]
Revisiting Time Series Outlier Detection: Definitions and Benchmarks	NeurIPS	2021	²²	[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	²³	[PDF]
KNN	Fast outlier detection in high dimensional spaces	PKDD	2002	²⁴	[PDF]
LOF	LOF: identifying density-based local outliers	ACM SIGMOD Record	2000	²⁵	[PDF]
IForest	Isolation forest	ICDM	2008	²⁶	[PDF]
OCSVM	Estimating the support of a high-dimensional distribution	Neural Computation	2001	²⁷	[PDF]
AutoEncoder Ensemble	Outlier detection with autoencoder ensembles	SDM	2017	²⁸	[PDF]
COPOD	COPOD: Copula-Based Outlier Detection	ICDM	2020	²⁹	[PDF]

4.3. Graph & Network Outlier Detection

Paper Title	Venue	Year	Ref	Materials
Graph based anomaly detection and description: a survey	DMKD	2015	³⁰	[PDF]
Anomaly detection in dynamic networks: a survey	WIREs Computational Statistic	2015	³¹	[PDF]
Outlier detection in graphs: On the impact of multiple graph models	ComSIS	2019	³²	[PDF]
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning	Preprint	2021	³³	[PDF]

4.4. Time Series Outlier Detection

Paper Title	Venue	Year	Ref	Materials
Outlier detection for temporal data: A survey	TKDE	2014	³⁴	[PDF]
Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding	KDD	2018	³⁵	[PDF], [Code]
Time-Series Anomaly Detection Service at Microsoft	KDD	2019	³⁶	[PDF]
Revisiting Time Series Outlier Detection: Definitions and Benchmarks	NeurIPS	2021	³⁷	[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	³⁸	[PDF]
Learning homophily couplings from non-iid data for joint feature selection and noise-resilient outlier detection	IJCAI	2017	³⁹	[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	⁴⁰	[HTML]
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection	SIGKDD	2018	⁴¹	[PDF]
Reverse Nearest Neighbors in Unsupervised Distance-Based Outlier Detection	TKDE	2015	⁴²	[PDF], [SLIDES]
Outlier detection for high-dimensional data	Biometrika	2015	⁴³	[PDF]

4.7. Outlier Ensembles

Paper Title	Venue	Year	Ref	Materials
Outlier ensembles: position paper	SIGKDD Explorations	2013	⁴⁴	[PDF]
Ensembles for unsupervised outlier detection: challenges and research questions a position paper	SIGKDD Explorations	2014	⁴⁵	[PDF]
An Unsupervised Boosting Strategy for Outlier Detection Ensembles	PAKDD	2018	⁴⁶	[HTML]
LSCP: Locally selective combination in parallel outlier ensembles	SDM	2019	⁴⁷	[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	⁴⁸	[PDF]
Unsupervised real-time anomaly detection for streaming data	Neurocomputing	2017	⁴⁹	[PDF]
Outlier Detection in Feature-Evolving Data Streams	SIGKDD	2018	⁵⁰	[PDF], [Github]
Evaluating Real-Time Anomaly Detection Algorithms--The Numenta Anomaly Benchmark	ICMLA	2015	⁵¹	[PDF], [Github]
MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams	AAAI	2020	⁵²	[PDF], [Github]
NETS: Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing	VLDB	2019	⁵³	[PDF], [Github], [Slide]
Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping	KDD	2020	⁵⁴	[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	⁵⁵	[PDF]
Learning representations for outlier detection on a budget	Preprint	2015	⁵⁶	[PDF]
XGBOD: improving supervised outlier detection with unsupervised representation learning	IJCNN	2018	⁵⁷	[PDF]

4.10. Interpretability

Paper Title	Venue	Year	Ref	Materials
Explaining Anomalies in Groups with Characterizing Subspace Rules	DMKD	2018	⁵⁸	[PDF]
Beyond Outlier Detection: LookOut for Pictorial Explanation	ECML-PKDD	2018	⁵⁹	[PDF]
Contextual outlier interpretation	IJCAI	2018	⁶⁰	[PDF]
Mining multidimensional contextual outliers from categorical relational data	IDA	2015	⁶¹	[PDF]
Discriminative features for identifying and interpreting outliers	ICDE	2014	⁶²	[PDF]
Sequential Feature Explanations for Anomaly Detection	TKDD	2019	⁶³	[HTML]
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network	WWW	2021	⁶⁴	[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	⁶⁵	[PDF], [Code]
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks	ICANN	2019	⁶⁶	[PDF], [Code]
Generative Adversarial Active Learning for Unsupervised Outlier Detection	TKDE	2019	⁶⁷	[PDF], [Code]
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection	ICLR	2018	⁶⁸	[PDF], [Code]
Deep Anomaly Detection with Outlier Exposure	ICLR	2019	⁶⁹	[PDF], [Code]
Unsupervised Anomaly Detection With LSTM Neural Networks	TNNLS	2019	⁷⁰	[PDF], [IEEE],
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network	NeurIPS	2019	⁷¹	[PDF] [Code]

4.12. Active Anomaly Detection

Paper Title	Venue	Year	Ref	Materials
Active learning for anomaly and rare-category detection	NeurIPS	2005	⁷²	[PDF]
Outlier detection by active learning	SIGKDD	2006	⁷³	[PDF]
Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability	Preprint	2019	⁷⁴	[PDF]
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning	ICDM	2020	⁷⁵	[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	⁷⁶	[PDF]
Interactive anomaly detection on attributed networks	WSDM	2019	⁷⁷	[PDF]
eX2: a framework for interactive anomaly detection	IUI Workshop	2019	⁷⁸	[PDF]
Tripartite Active Learning for Interactive Anomaly Discovery	IEEE Access	2019	⁷⁹	[PDF]

4.14. Outlier Detection in Other fields

Field	Paper Title	Venue	Year	Ref	Materials
Text	Outlier detection for text data	SDM	2017	⁸⁰	[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	⁸¹	[PDF]
Security	Anomaly-based network intrusion detection: Techniques, systems and challenges	Computers & Security	2009	⁸²	[PDF]
Finance	A survey of anomaly detection techniques in financial domain	Future Gener Comput Syst	2016	⁸³	[PDF]
Traffic	Outlier Detection in Urban Traffic Data	WIMS	2018	⁸⁴	[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	⁸⁶	[PDF]
Machine Failure	Detecting the Onset of Machine Failure Using Anomaly Detection Methods	DAWAK	2019	⁸⁷	[PDF]
Video Surveillance	AnomalyNet: An anomaly detection network for video surveillance	TIFS	2019	⁸⁸	[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	⁸⁹	[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	⁹¹	[PDF], [Code]
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection	MLSys	2021	⁹²	[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	⁹³	[PDF]
FAIROD: Fairness-aware Outlier Detection	AIES	2021	⁹⁴	[PDF]

4.19. Isolation-Based Methods

Paper Title	Venue	Year	Ref	Materials
Isolation forest	ICDM	2008	⁹⁵	[PDF]
Isolation‐based anomaly detection using nearest‐neighbor ensembles	Computational Intelligence	2018	⁹⁶	[PDF], [Code]
Extended Isolation Forest	TKDE	2019	⁹⁷	[PDF], [Code]
Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection	KDD	2020	⁹⁸	[PDF], [Code]

4.20. Emerging and Interesting Topics

Paper Title	Venue	Year	Ref	Materials
Clustering with Outlier Removal	Preprint	2018	⁹⁹	[PDF]
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning	IEEE Trans. Ind. Informat.	2020	¹⁰⁰	[PDF]
SSD: A Unified Framework for Self-Supervised Outlier Detection	ICLR	2021	¹⁰¹	[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

Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V. and Srivastava, J., 2008, September. Data mining for anomaly detection. Tutorial at ECML PKDD 2008.↩
Kriegel, H.P., Kröger, P. and Zimek, A., 2010. Outlier detection techniques. Tutorial at ACM SIGKDD 2010.↩
Chawla, S. and Chandola, V., 2011, Anomaly Detection: A Tutorial. Tutorial at ICDM 2011.↩
Mendiratta, B.V., 2017. Anomaly Detection in Networks. Tutorial at ACM SIGKDD 2017.↩
Ting, KM., Aryal, S. and Washio, T., 2018, Which Anomaly Detector should I use? Tutorial at ICDM 2018.↩
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.↩
Pang, G., Cao, L. and Aggarwal, C., 2021. Deep Learning for Anomaly Detection. Tutorial at WSDM 2021.↩
Hodge, V. and Austin, J., 2004. A survey of outlier detection methodologies. Artificial intelligence review, 22(2), pp.85-126.↩
Chandola, V., Banerjee, A. and Kumar, V., 2009. Anomaly detection: A survey. ACM computing surveys , 41(3), p.15.↩
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.↩
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.↩
Goldstein, M. and Uchida, S., 2016. A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one, 11(4), p.e0152173.↩
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.↩
Suri, N.R. and Athithan, G., 2019. Research Issues in Outlier Detection. In Outlier Detection: Techniques and Applications, pp. 29-51. Springer, Cham.↩
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.↩
Wang, H., Bah, M.J. and Hammad, M., 2019. Progress in Outlier Detection Techniques: A Survey. IEEE Access, 7, pp.107964-108000.↩
Chalapathy, R. and Chawla, S., 2019. Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.↩
Bulusu, S., Kailkhura, B., Li, B., Varshney, P. and Song, D., 2020. Anomalous instance detection in deep learning: A survey (No. LLNL-CONF-808677). Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States).↩
Braei, M. and Wagner, S., 2020. Anomaly detection in univariate time-series: A survey on the state-of-the-art. arXiv preprint arXiv:2004.00433.↩
Pang, G., Shen, C., Cao, L. and Hengel, A.V.D., 2021. Deep Learning for Anomaly Detection: A Review. ACM Computing Surveys (CSUR), 54(2), pp.1-38.↩
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. arXiv preprint arXiv:2106.07178.↩
Lai, K.H., Zha, D., Xu, J., Zhao, Y., Wang, G. and Hu, X., 2021. Revisiting Time Series Outlier Detection: Definitions and Benchmarks. Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track.↩
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.↩
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.↩
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.↩
Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In International Conference on Data Mining, pp. 413-422. IEEE.↩
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.↩
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.↩
Li, Z., Zhao, Y., Botta, N., Ionescu, C. and Hu, X. COPOD: Copula-Based Outlier Detection. IEEE International Conference on Data Mining (ICDM), 2020.↩
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.↩
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.↩
Campos, G.O., Moreira, E., Meira Jr, W. and Zimek, A., 2019. Outlier Detection in Graphs: A Study on the Impact of Multiple Graph Models. Computer Science & Information Systems, 16(2).↩
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. arXiv preprint arXiv:2106.07178.↩
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.↩
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.↩
Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Xing, T., Yang, M., Tong, J. and Zhang, Q., 2019. Time-Series Anomaly Detection Service at Microsoft. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM.↩
Lai, K.H., Zha, D., Xu, J., Zhao, Y., Wang, G. and Hu, X., 2021. Revisiting Time Series Outlier Detection: Definitions and Benchmarks. Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track.↩
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.↩
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.↩
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.↩
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.↩
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.↩
Ro, K., Zou, C., Wang, Z. and Yin, G., 2015. Outlier detection for high-dimensional data. Biometrika, 102(3), pp.589-599.↩
Aggarwal, C.C., 2013. Outlier ensembles: position paper. ACM SIGKDD Explorations Newsletter, 14(2), pp.49-58.↩
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.↩
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.↩
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.↩
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.↩
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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