Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2019

Abstract

Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform indirect optimization of anomaly scores, leading to data-inefficient learning and suboptimal anomaly scoring. Also, they are typically designed as unsupervised learning due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage prior knowledge (e.g., a few labeled anomalies) when such information is available as in many real-world anomaly detection applications. This paper introduces a novel anomaly detection framework and its instantiation to address these problems. Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e.g., multiple to dozens) labeled anomalies and a prior probability to enforce statistically significant deviations of the anomaly scores of anomalies from that of normal data objects in the upper tail. Extensive results show that our method can be trained substantially more data-efficiently and achieves significantly better anomaly scoring than state-of-the-art competing methods.

Keywords

Anomaly Detection, Deep Learning, Representation Learning, Neural Networks, Outlier Detection

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, USA, 2019 August 4-8

First Page

353

Last Page

362

ISBN

9781450362016

Identifier

10.1145/3292500.3330871

Publisher

ACM

City or Country

Anchorage, USA

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