Deep learning for anomaly detection: challenges, methods, and opportunities
Date
2021
Authors
Pang, G.
Cao, L.
Aggarwal, C.
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Conference paper
Citation
Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WDSM'21), 2021, pp.1127-1130
Statement of Responsibility
Guansong Pang, Longbing Cao, Charu Aggarwal
Conference Name
ACM International Conference on Web Search and Data Mining (WSDM) (8 Mar 2021 - 12 Mar 2021 : virtual online)
Abstract
In this tutorial we aim to present a comprehensive survey of the advances in deep learning techniques specifically designed for anomaly detection (deep anomaly detection for short). Deep learning has gained tremendous success in transforming many data mining and machine learning tasks, but popular deep learning techniques are inapplicable to anomaly detection due to some unique characteristics of anomalies, e.g., rarity, heterogeneity, boundless nature, and prohibitively high cost of collecting large-scale anomaly data. Through this tutorial, audiences would gain a systematic overview of this area, learn the key intuitions, objective functions, underlying assumptions, advantages and disadvantages of different categories of state-of-the-art deep anomaly detection methods, and recognize its broad real-world applicability in diverse domains. We also discuss what challenges the current deep anomaly detection methods can address and envision this area from multiple different perspectives. Any audience who may be interested in deep learning, anomaly/ outlier/novelty detection, out-of-distribution detection, representation learning with limited labeled data, and self-supervised representation learning would find it very helpful in attending this tutorial. Researchers and practitioners in finance, cybersecurity, healthcare would also find the tutorial helpful in practice.
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© 2021 Association for Computing Machinery.