Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2021

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 anomalybiased 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.

Keywords

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

Discipline

Artificial Intelligence and Robotics | OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Conference, 2021 August 14-18

First Page

1298

Last Page

1308

ISBN

9781450383325

Identifier

10.1145/3447548.3467417

Publisher

ACM

City or Country

Virtual Conference

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