Deep Learning for Anomaly Detection

Pang, G; Shen, C; Cao, L; Hengel, AVD

Deep Learning for Anomaly Detection

Pang, G Shen, C Cao, L

Hengel, AVD

Permalink

Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Computing Surveys, 2021, 54, (2), pp. 1-38
Issue Date:: 2021-03

Closed Access

	Filename	Description	Size
	Deep_Anomaly_Detection___Survey.pdf	Accepted version	960.86 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Pang, G
dc.contributor.author	Shen, C
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429
dc.contributor.author	Hengel, AVD
dc.date.accessioned	2021-04-01T10:52:26Z
dc.date.available	2021-04-01T10:52:26Z
dc.date.issued	2021-03
dc.identifier.citation	ACM Computing Surveys, 2021, 54, (2), pp. 1-38
dc.identifier.issn	0360-0300
dc.identifier.issn	1557-7341
dc.identifier.uri	http://hdl.handle.net/10453/147799
dc.description.abstract	<jats:p> 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., <jats:italic>deep anomaly detection</jats:italic> , 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. </jats:p>
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	http://purl.org/au-research/grants/arc/DP190101079
dc.relation	http://purl.org/au-research/grants/arc/FT190100734
dc.relation.ispartof	ACM Computing Surveys
dc.relation.isbasedon	10.1145/3439950
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.title	Deep Learning for Anomaly Detection
dc.type	Journal Article
utslib.citation.volume	54
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2021-04-01T10:52:25Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	54
utslib.citation.issue	2

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/147799