research-article

Deep Learning for Anomaly Detection: A Review

Authors:

Anton Van Den HengelAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 2

Article No.: 38, Pages 1 - 38

https://doi.org/10.1145/3439950

Published: 05 March 2021 Publication History

Abstract

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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Index Terms

Deep Learning for Anomaly Detection: A Review
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene anomaly detection
  2. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Anomaly detection
    2. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 2

March 2022

800 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3450359

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 05 March 2021

Accepted: 01 November 2020

Revised: 01 October 2020

Received: 01 July 2020

Published in CSUR Volume 54, Issue 2

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