VoterChoice: A ransomware detection honeypot with multiple voting framework

Keong Ng, C; Rajasegarar, S; Pan, L; Jiang, F; Zhang, LY

VoterChoice: A ransomware detection honeypot with multiple voting framework

Keong Ng, C Rajasegarar, S Pan, L Jiang, F

Zhang, LY

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Publisher:: Wiley
Publication Type:: Journal Article
Citation:: Concurrency Computation, 2020, 32, (14)
Issue Date:: 2020-07-25

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Field	Value	Language
dc.contributor.author	Keong Ng, C
dc.contributor.author	Rajasegarar, S
dc.contributor.author	Pan, L
dc.contributor.author	Jiang, F https://orcid.org/0000-0003-3765-561X
dc.contributor.author	Zhang, LY
dc.date.accessioned	2021-06-02T21:34:17Z
dc.date.available	2021-06-02T21:34:17Z
dc.date.issued	2020-07-25
dc.identifier.citation	Concurrency Computation, 2020, 32, (14)
dc.identifier.issn	1532-0626
dc.identifier.issn	1532-0634
dc.identifier.uri	http://hdl.handle.net/10453/149376
dc.description.abstract	This research presents a novel framework comprising the IPS gateway, analysis system, and honeypot for identifying and detecting ransomware based on the client honeypot concept, and active interception of downloads using Suricata inline intruder prevention system. Unlike previous frameworks that report on the accuracy rate of detecting ransomware, the proposed framework features a multiple voting platform for the validation of confidence levels in the accuracy detection rates. The proposed framework achieves high accuracy levels than other machine learning models for the detection of ransomware.
dc.language	en
dc.publisher	Wiley
dc.relation.ispartof	Concurrency Computation
dc.relation.isbasedon	10.1002/cpe.5726
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.title	VoterChoice: A ransomware detection honeypot with multiple voting framework
dc.type	Journal Article
utslib.citation.volume	32
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
utslib.copyright.status	closed_access	*
dc.date.updated	2021-06-02T21:34:13Z
pubs.issue	14
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	14

Abstract:

This research presents a novel framework comprising the IPS gateway, analysis system, and honeypot for identifying and detecting ransomware based on the client honeypot concept, and active interception of downloads using Suricata inline intruder prevention system. Unlike previous frameworks that report on the accuracy rate of detecting ransomware, the proposed framework features a multiple voting platform for the validation of confidence levels in the accuracy detection rates. The proposed framework achieves high accuracy levels than other machine learning models for the detection of ransomware.

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

http://hdl.handle.net/10453/149376