Reinforcement Learning-driven Attack on Road Traffic Signal Controllers

Intelligent Transportation Systems (ITS) combine emerging communication, computer, and system technologies to deliver intelligent road traffic services and optimize decision making within the transportation infrastructure. The advancement of connected vehicles, which generate dynamic data through wireless communications, enables ITS to improve their efficiency, especially in Traffic Signal Control (TSC), which is the backbone of traffic flow scheduling. However, wireless communications channels are vulnerable to various types of cyberattacks and can pose serious threats to dynamic TSC systems. Attackers may attempt to manipulate normal traffic flows and cause severe traffic congestion. Deep Reinforcement Learning (DRL) is a powerful technique that has been used to improve TSC systems performance in real-time environments. However, it can be used by attackers to exploit the dynamics of the ITS and learn the optimal attack policy under the lack of deterministic information about system behavior. In this work, to highlight and exploit existing vulnerabilities in TSC systems, we leverage DRL to create an intelligent Sybil attack on a traffic intersection, wherein connected vehicles with fake identities are optimally placed to alter traffic signal timings by corrupting traffic data. The results show that this attack leads to substantial increase in the vehicles’ travel time and yields disastrous traffic congestion, especially if carried out for a prolonged period of time, which will give rise to serious problems such as higher fuel consumption and air pollution in heavily dense cities. In the presence of such intelligent attacks, the design assumptions of existing TSC systems become highly questionable.