TY - JOUR
T1 - The Deceitful Connected and Autonomous Vehicle
T2 - Defining the Concept, Contextualising its Dimensions and Proposing Mitigation Policies
AU - Nikitas, Alexandros
AU - Parkinson, Simon
AU - Vallati, Mauro
N1 - Funding Information:
We want to thank Mr Fernando Solis for helping us design Fig. 2 . Dr Mauro Vallati was supported by a UKRI Future Leaders Fellowship [grant number MR/T041196/1 ].
Publisher Copyright:
© 2022 The Authors
PY - 2022/6/1
Y1 - 2022/6/1
N2 - 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). We 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.
AB - 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). We 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.
KW - Connected and autonomous vehicles
KW - Deceitful connected and autonomous vehicles
KW - Artificial intelligence and deceitful behaviour
KW - Urban traffic control
KW - Future mobility disruption
UR - http://www.scopus.com/inward/record.url?scp=85129315953&partnerID=8YFLogxK
U2 - 10.1016/j.tranpol.2022.04.011
DO - 10.1016/j.tranpol.2022.04.011
M3 - Article
VL - 122
SP - 1
EP - 10
JO - Transport Policy
JF - Transport Policy
SN - 0967-070X
ER -