  Connected and Autonomous Vehicles (CAVs) with their evolving data gathering
capabilities will play a significant role in road safety and efficiency
applications supported by Intelligent Transport Systems (ITS), such as Traffic
Signal Control (TSC) for urban traffic congestion management. However, their
involvement will expand the space of security vulnerabilities and create larger
threat vectors. In this paper, we perform the first detailed security analysis
and implementation of a new cyber-physical attack category carried out by the
network of CAVs against Adaptive Multi-Agent Traffic Signal Control (AMATSC),
namely, coordinated Sybil attacks, where vehicles with forged or fake
identities try to alter the data collected by the AMATSC algorithms to sabotage
their decisions. Consequently, a novel, game-theoretic mitigation approach at
the application layer is proposed to minimize the impact of such sophisticated
data corruption attacks. The devised minimax game model enables the AMATSC
algorithm to generate optimal decisions under a suspected attack, improving its
resilience. Extensive experimentation is performed on a traffic dataset
provided by the City of Montreal under real-world intersection settings to
evaluate the attack impact. Our results improved time loss on attacked
intersections by approximately 48.9%. Substantial benefits can be gained from
the mitigation, yielding more robust adaptive control of traffic across
networked intersections.
