TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network

Nayak, RP; Sethi, S; Bhoi, SK; Mohapatra, D; Sahoo, RR; Sharma, PK; Puthal, D

TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network

Nayak, RP Sethi, S Bhoi, SK Mohapatra, D Sahoo, RR Sharma, PK Puthal, D

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Journal of Supercomputing, 2022, 78, (6), pp. 7948-7981
Issue Date:: 2022-04-01

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Field	Value	Language
dc.contributor.author	Nayak, RP
dc.contributor.author	Sethi, S
dc.contributor.author	Bhoi, SK
dc.contributor.author	Mohapatra, D
dc.contributor.author	Sahoo, RR
dc.contributor.author	Sharma, PK
dc.contributor.author	Puthal, D https://orcid.org/0000-0002-8332-278X
dc.date.accessioned	2023-04-06T01:46:47Z
dc.date.available	2023-04-06T01:46:47Z
dc.date.issued	2022-04-01
dc.identifier.citation	Journal of Supercomputing, 2022, 78, (6), pp. 7948-7981
dc.identifier.issn	0920-8542
dc.identifier.issn	1573-0484
dc.identifier.uri	http://hdl.handle.net/10453/169257
dc.description.abstract	In this paper, a trust framework is proposed for misbehavior detection in software defined vehicular networks (TFMD-SDVN) to detect the correct events in the network reported by the trusted or untrusted nodes. The trust value of a node is calculated based on rating, recommendation, and similarity. If the trust value is greater than a threshold, then the event reported by the event reporting node (ERN) is assumed to be correct. The performance of the proposed work is evaluated using OMNeT++ network simulator and SUMO traffic simulator in Veins hybrid framework. The performance parameters taken are True Positive Rate (TPR), False Positive Rate (FPR), Detection Time (DT), and Packet Delivery Ratio (PDR). Simulation results show that the proposed approach performs better than ART scheme, RPRep scheme, and BYOR scheme.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Journal of Supercomputing
dc.relation.isbasedon	10.1007/s11227-021-04227-z
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0803 Computer Software, 0805 Distributed Computing
dc.subject.classification	Distributed Computing
dc.title	TFMD-SDVN: a trust framework for misbehavior detection in the edge of software-defined vehicular network
dc.type	Journal Article
utslib.citation.volume	78
utslib.for	0803 Computer Software
utslib.for	0805 Distributed Computing
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 Information, Systems and Modelling
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-06T01:46:46Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	78
utslib.citation.issue	6

Abstract:

In this paper, a trust framework is proposed for misbehavior detection in software defined vehicular networks (TFMD-SDVN) to detect the correct events in the network reported by the trusted or untrusted nodes. The trust value of a node is calculated based on rating, recommendation, and similarity. If the trust value is greater than a threshold, then the event reported by the event reporting node (ERN) is assumed to be correct. The performance of the proposed work is evaluated using OMNeT++ network simulator and SUMO traffic simulator in Veins hybrid framework. The performance parameters taken are True Positive Rate (TPR), False Positive Rate (FPR), Detection Time (DT), and Packet Delivery Ratio (PDR). Simulation results show that the proposed approach performs better than ART scheme, RPRep scheme, and BYOR scheme.

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http://hdl.handle.net/10453/169257