LAUSR

There is an increasing trend in the use of wireless communication along with new traffic signal control (TSC) algorithms to leverage and accommodate connected and autonomous vehicles. However, this development has increased the potential for cyber-attacks on TSC that can undermine the benefits of these new algorithms. An advanced persistent adversary can learn the behavior of TSC algorithms and launch attacks to preferentially get green time and/or to create traffic congestion in one intersection which can spread to the entire network. In this paper, we consider backpressure-based (BP-based) TSC algorithms and compare their performance under two misinformation attacks - 1) time spoofing attack in which vehicles alter their arrival times at the intersection and 2) ghost vehicle attack in which vehicles disconnect the wireless communication and thereby hide from the TSC. We show that these misinformation can influence the signal phases determined by BP-based TSC algorithms. We consider an adversary that determines a set of arriving vehicles to be attack vehicles from many candidate sets (attack strategies) in order to maximize the number of disrupted signal phases. We show that by formulating the problem as a 0/1 Knapsack problem, the adversary can explore the space of attack strategies and determine the optimal strategy that maximally compromises the performance in terms of average delay and fairness. We propose two protection algorithms, namely, auction-based (APA) and hybrid-based (HPA) algorithms and show that they are able to mitigate the impacts of the misinformation attacks.