The deceitful Connected and Autonomous vehicle: Defining the concept, contextualising its dimensions and proposing mitigation policies
The Connected and Autonomous Vehicle (CAV) is an emerging mobility technology that may hold a paradigm-changing potential for the future of transport policy and planning. Despite a wealth of likely benefits that have made their eventual launch inescapable, CAVs may also be a source of unprecedented disruption for tomorrow's travel eco-systems because of their vulnerability to cyber-threats, hacking and misinformation. CAVs manipulated by users, traffic controllers or third parties may act in deceitful ways. This scene-setting work introduces the deceitful CAV, a vehicle that operates in a deceitful manner towards routing and control functionality for ‘selfish’ or malicious purposes and contextualises its diverse expressions and dimensions. It specifically offers a systematic taxonomy of eight distinctive deceitful behaviours namely: suppression/camouflage, overloading, mistake, substitution, target conditioning, repackaging capability signatures, amplification and reinforcing impression. These as exemplified by their most common attack forms (i.e., starvation, denial-of-service, session hijacking, man-in-the-middle, poisoning, masquerading, flooding and spoofing) are then benchmarked against five key dimensions referring to time frame (short to long duration), engagement (localised to systemic), urban traffic controller infrastructure (single to multiple components), scale (low to high), and impact (low to high). The authors then suggest mitigation strategies to protect CAV technology against these dangers. These span from purely technological measures referring to the machine-centric triad of vehicles, communication, and control system including adversarial training, heuristic decision algorithms and weighted voting mechanisms to human factor measures that focus on education, training, awareness enhancement, licensing and legislation initiatives that will enable users and controllers to prevent, control or report deceitful activities.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/29485010
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Supplemental Notes:
- © 2022 Alexandros Nikitas et al. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
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Authors:
- Nikitas, Alexandros
- Parkinson, Simon
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0000-0002-1747-9914
- Vallati, Mauro
- Publication Date: 2022-6
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 1-10
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Serial:
- Transport Policy
- Volume: 122
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0967-070X
- Serial URL: http://www.elsevier.com/locate/issn/096707X
Subject/Index Terms
- TRT Terms: Artificial intelligence; Autonomous vehicles; Behavior; Computer security; Connected vehicles; Traffic control
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01850297
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 27 2022 5:19PM