Disrupting Adaptive Traffic Lights Cycles through Selective Jamming Attacks

Adaptive traffic lights are critical components in Intelligent Transportation Systems (ITS) aiming to minimize the trip times for vehicles as well as reduce their fuel emission rates. With the recent advances in embedded systems and communication technologies, traffic lights receive signals from nearby vehicles to learn about the current load conditions as well as the types of vehicles on various lanes. This information is used by the traffic lights to adjust the traffic cycles appropriately to optimize efficiency. In this paper, we study the impact of selective jamming attacks in which a subset of the signals from the vehicles to the traffic light are jammed. This causes the traffic light to adapt its cycle to incorrect load estimates, leading to an increase in the trip times for vehicles and higher fuel emission rates. We focus on stealthy types of attacks that take the cost of the attack in consideration when mounting attacks. In particular, the attacker aims to maximize the marginal utility of the jamming attack. We evaluate the effect of our exposed attacks using SUMO simulations under various types of scenarios and for different metrics of damage and cost.