Deep Learning for Anomaly Detection: A Review

被引:1481
作者
Pang, Guansong [1 ]
Shen, Chunhua [1 ]
Cao, Longbing [2 ]
Van den Hengel, Anton [1 ]
机构
[1] Univ Adelaide, Adelaide, SA 5005, Australia
[2] Univ Technol Sydney, Sydney, NSW 2007, Australia
关键词
Anomaly detection; deep learning; outlier detection; novelty detection; one-class classification; OUTLIER DETECTION; NETWORK; SUPPORT; REPRESENTATIONS; ALGORITHMS;
D O I
10.1145/3439950
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.
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页数:38
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