The role of KL divergence in anomaly detection

Zhang, L; Veitch, D; Ramamohanarao, K

The role of KL divergence in anomaly detection

Zhang, L Veitch, D

Ramamohanarao, K

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Publication Type:: Conference Proceeding
Citation:: Performance Evaluation Review, 2011, 39 (1 SPEC. ISSUE), pp. 123 - 124
Issue Date:: 2011-07-15

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Field	Value	Language
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Veitch, D https://orcid.org/0000-0001-8163-3464	en_US
dc.contributor.author	Ramamohanarao, K	en_US
dc.date.issued	2011-07-15	en_US
dc.identifier.citation	Performance Evaluation Review, 2011, 39 (1 SPEC. ISSUE), pp. 123 - 124	en_US
dc.identifier.isbn	9781450302623	en_US
dc.identifier.issn	0163-5999	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116973
dc.description.abstract	We study the role of Kullback-Leibler divergence in the framework of anomaly detection, where its abilities as a statistic underlying detection have never been investigated in depth. We give an in-principle analysis of network attack detection, showing explicitly attacks may be masked at minimal cost through 'camouflage'. We illustrate on both synthetic distributions and ones taken from real traffic.	en_US
dc.relation.ispartof	Performance Evaluation Review	en_US
dc.relation.isbasedon	10.1145/1993744.1993787	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	The role of KL divergence in anomaly detection	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1 SPEC. ISSUE	en_US
utslib.citation.volume	39	en_US
utslib.for	080503 Networking and Communications	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.issue	1 SPEC. ISSUE	en_US
pubs.publication-status	Published	en_US
pubs.volume	39	en_US

Abstract:

We study the role of Kullback-Leibler divergence in the framework of anomaly detection, where its abilities as a statistic underlying detection have never been investigated in depth. We give an in-principle analysis of network attack detection, showing explicitly attacks may be masked at minimal cost through 'camouflage'. We illustrate on both synthetic distributions and ones taken from real traffic.

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

http://hdl.handle.net/10453/116973