Leveraging Byte-Level Features for LSTM-based Anomaly Detection in Controller Area Networks

Liang, L Lin, X Ma, B

Wang, X He, Y Liu, R Ni, W

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	RE_based_features_to_IDS_LSTM__GlobalCom_2022_May_8v1.pdf	Accepted version	574.45 kB	Adobe PDF	View/Open

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Field	Value	Language
dc.contributor.author	Liang, L
dc.contributor.author	Lin, X
dc.contributor.author	Ma, B https://orcid.org/0000-0003-4167-2797
dc.contributor.author	Wang, X
dc.contributor.author	He, Y
dc.contributor.author	Liu, R
dc.contributor.author	Ni, W
dc.date	2022-12-04
dc.date.accessioned	2022-10-13T06:20:48Z
dc.date.available	2022-10-13T06:20:48Z
dc.date.issued	2022-01-01
dc.identifier.citation	2022
dc.identifier.uri	http://hdl.handle.net/10453/162562
dc.language	en
dc.relation.ispartof	2022 IEEE Global Communications Conference
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Leveraging Byte-Level Features for LSTM-based Anomaly Detection in Controller Area Networks
dc.type	Conference Proceeding
utslib.location.activity	Rio de Janeiro, Brazil
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	embargoed	*
pubs.consider-herdc	false
utslib.copyright.embargo	2025-01-11T00:00:00+1000Z
dc.date.updated	2022-10-13T06:20:47Z
pubs.finish-date	2022-12-08
pubs.publication-status	Accepted
pubs.start-date	2022-12-04

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