Deep Learning for Anomaly Detection A Review

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...

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Bibliographic Details
Published inACM computing surveys Vol. 54; no. 2; pp. 1 - 38
Main Authors Pang, Guansong, Shen, Chunhua, Cao, Longbing, Hengel, Anton Van Den
Format Journal Article
LanguageEnglish
Published Baltimore Association for Computing Machinery 31.03.2022
Subjects
Anomalies
Computer science
Data analysis
Deep learning
Outliers (statistics)
Taxonomy
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Summary: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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ISSN:0360-0300
1557-7341
DOI:10.1145/3439950