Trust in Vehicles: Toward Context-Aware Trust and Attack Resistance for the Internet of Vehicles

Siddiqui, SA; Mahmood, A; Sheng, QZ; Suzuki, H; Ni, W

Trust in Vehicles: Toward Context-Aware Trust and Attack Resistance for the Internet of Vehicles

Siddiqui, SA Mahmood, A Sheng, QZ Suzuki, H Ni, W

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Transportation Systems, 2023, 24, (9), pp. 9546-9560
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Siddiqui, SA
dc.contributor.author	Mahmood, A
dc.contributor.author	Sheng, QZ
dc.contributor.author	Suzuki, H
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.date.accessioned	2024-04-17T02:57:27Z
dc.date.available	2024-04-17T02:57:27Z
dc.date.issued	2023-09-01
dc.identifier.citation	IEEE Transactions on Intelligent Transportation Systems, 2023, 24, (9), pp. 9546-9560
dc.identifier.issn	1524-9050
dc.identifier.issn	1558-0016
dc.identifier.uri	http://hdl.handle.net/10453/178002
dc.description.abstract	Trust evaluation and management schemes have been extensively employed in a bid to alleviate diverse insider attacks. These trust values are often ascertained by taking into consideration trust parameters to determine the honesty and reliability of a vehicle and evaluating a weighted sum of the said parameters. To achieve precision and to reflect a reasonable impact of these parameters, rational weight values are imperative. Accordingly, this research primarily emphasizes on the quantification of weights associated with the contributing trust attributes by proposing a novel trust management mechanism that utilizes contextual information in addition to employing relevant impacting quantities as weights to formulate trust evaluations. Moreover, the envisaged trust management model incorporates 1) attack resilience while constituting certain parameters and 2) an adaptive and flexible threshold to mitigate malevolent behaviors. The simulation results depict that the devised parameters and the formulated trust aggregation cater to the dynamic nature of vehicular networks demonstrating the rationality of the weights' quantification, and the introduction of the adaptive threshold for misbehavior detection aligns well with the requirements of the ever-changing vehicular networks.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/LE220100078
dc.relation.ispartof	IEEE Transactions on Intelligent Transportation Systems
dc.relation.isbasedon	10.1109/TITS.2023.3268301
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0905 Civil Engineering, 1507 Transportation and Freight Services
dc.subject.classification	Logistics & Transportation
dc.subject.classification	3509 Transportation, logistics and supply chains
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4603 Computer vision and multimedia computation
dc.title	Trust in Vehicles: Toward Context-Aware Trust and Attack Resistance for the Internet of Vehicles
dc.type	Journal Article
utslib.citation.volume	24
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0905 Civil Engineering
utslib.for	1507 Transportation and Freight Services
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
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-17T02:57:25Z
pubs.issue	9
pubs.publication-status	Published
pubs.volume	24
utslib.citation.issue	9

Abstract:

Trust evaluation and management schemes have been extensively employed in a bid to alleviate diverse insider attacks. These trust values are often ascertained by taking into consideration trust parameters to determine the honesty and reliability of a vehicle and evaluating a weighted sum of the said parameters. To achieve precision and to reflect a reasonable impact of these parameters, rational weight values are imperative. Accordingly, this research primarily emphasizes on the quantification of weights associated with the contributing trust attributes by proposing a novel trust management mechanism that utilizes contextual information in addition to employing relevant impacting quantities as weights to formulate trust evaluations. Moreover, the envisaged trust management model incorporates 1) attack resilience while constituting certain parameters and 2) an adaptive and flexible threshold to mitigate malevolent behaviors. The simulation results depict that the devised parameters and the formulated trust aggregation cater to the dynamic nature of vehicular networks demonstrating the rationality of the weights' quantification, and the introduction of the adaptive threshold for misbehavior detection aligns well with the requirements of the ever-changing vehicular networks.

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http://hdl.handle.net/10453/178002