CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories

Lin, X; Ma, B; Wang, X; Yu, G; He, Y; Ni, W; Liu, RP

CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories

Lin, X

Ma, B

Wang, X

Yu, G

He, Y

Ni, W Liu, RP

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Intelligent Transportation Systems, 2025, PP, (99)
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Lin, X https://orcid.org/0000-0002-0133-4737
dc.contributor.author	Ma, B https://orcid.org/0000-0003-4167-2797
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9439-6437
dc.contributor.author	Yu, G https://orcid.org/0000-0002-6111-1607
dc.contributor.author	He, Y https://orcid.org/0000-0003-1603-9375
dc.contributor.author	Ni, W
dc.contributor.author	Liu, RP
dc.date.accessioned	2025-03-12T03:45:27Z
dc.date.available	2025-03-12T03:45:27Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE Transactions on Intelligent Transportation Systems, 2025, PP, (99)
dc.identifier.issn	1524-9050
dc.identifier.issn	1558-0016
dc.identifier.uri	http://hdl.handle.net/10453/185730
dc.description.abstract	Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages to uncover driving trajectories, posing a significant risk to drivers' long-term privacy. A new trajectory reconstruction algorithm is proposed to transform the CAN messages, specifically vehicle speed and accelerator pedal position, into weighted graphs accommodating various driving statuses. CAN-Trace identifies driving trajectories using graph-matching algorithms applied to the created graphs in comparison to road networks. We also design a new metric to evaluate matched candidates, which allows for potential data gaps and matching inaccuracies. Empirical validation under various real-world conditions, encompassing different vehicles and driving regions, demonstrates the efficacy of CAN-Trace: it achieves an attack success rate of up to 90.59% in the urban region, and 99.41% in the suburban region.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Intelligent Transportation Systems
dc.relation.isbasedon	10.1109/TITS.2025.3532455
dc.rights	info:eu-repo/semantics/openAccess
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	CAN-Trace Attack: Exploit CAN Messages to Uncover Driving Trajectories
dc.type	Journal Article
utslib.citation.volume	PP
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
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Global Big Data Technologies Centre (GBDTC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Cyber Security and Privacy (CCSP)
utslib.copyright.status	open_access	*
dc.date.updated	2025-03-12T03:45:18Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Driving trajectory data remains vulnerable to privacy breaches despite existing mitigation measures. Traditional methods for detecting driving trajectories typically rely on map-matching the path using Global Positioning System (GPS) data, which is susceptible to GPS data outage. This paper introduces CAN-Trace, a novel privacy attack mechanism that leverages Controller Area Network (CAN) messages to uncover driving trajectories, posing a significant risk to drivers' long-term privacy. A new trajectory reconstruction algorithm is proposed to transform the CAN messages, specifically vehicle speed and accelerator pedal position, into weighted graphs accommodating various driving statuses. CAN-Trace identifies driving trajectories using graph-matching algorithms applied to the created graphs in comparison to road networks. We also design a new metric to evaluate matched candidates, which allows for potential data gaps and matching inaccuracies. Empirical validation under various real-world conditions, encompassing different vehicles and driving regions, demonstrates the efficacy of CAN-Trace: it achieves an attack success rate of up to 90.59% in the urban region, and 99.41% in the suburban region.

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http://hdl.handle.net/10453/185730