Toward deep supervised anomaly detection: reinforcement learning from partially labeled anomaly data

Guansong Pang*, Anton van den Hengel, Chunhua Shen, Longbing Cao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

55 Citations (Scopus)

Abstract

We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. This is a common scenario in many important applications. Existing related methods either exclusively fit the limited anomaly examples that typically do not span the entire set of anomalies, or proceed with unsupervised learning from the unlabeled data. We propose here instead a deep reinforcement learning-based approach that enables an end-to-end optimization of the detection of both labeled and unlabeled anomalies. This approach learns the known abnormality by automatically interacting with an anomaly-biased simulation environment, while continuously extending the learned abnormality to novel classes of anomaly (i.e., unknown anomalies) by actively exploring possible anomalies in the unlabeled data. This is achieved by jointly optimizing the exploitation of the small labeled anomaly data and the exploration of the rare unlabeled anomalies. Extensive experiments on 48 real-world datasets show that our model significantly outperforms five state-of-the-art competing methods.

Original languageEnglish
Title of host publicationKDD '21
Subtitle of host publicationproceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages1298-1308
Number of pages11
ISBN (Electronic)9781450383325
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore
Duration: 14 Aug 202118 Aug 2021

Conference

Conference27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
Country/TerritorySingapore
CityVirtual, Online
Period14/08/2118/08/21

Keywords

  • Anomaly Detection
  • Deep Learning
  • Reinforcement Learning
  • Neural Networks
  • Outlier Detection
  • Intrusion Detection

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