Evaluating Cybersecurity Risks of Cooperative Ramp Merging in Mixed Traffic Environments

Connected and automated vehicle (CAV) technology has the potential to greatly improve transportation mobility, safety, and energy efficiency. However, ubiquitous vehicular connectivity also opens up the door to cyberattacks. In this study, we investigate cybersecurity risks of a representative cooperative traffic management application, i.e., highway on-ramp merging, in a mixed traffic environment. We develop threat models with two trajectory spoofing strategies on CAVs to create traffic congestion and devise an attack-resilient strategy for system defense. Furthermore, we leverage VEhicular NeTwork Open Simulator, a Veins extension simulator made for CAV applications, to evaluate cybersecurity risks of the attacks and performance of the proposed defense strategy. A comprehensive case study is conducted across different traffic congestion levels, penetration rates of CAVs, and attack ratios. As expected, the results show that mobility performance decreases up to 55.19% in the worst case when the attack ratio increases, as do safety and energy. With our proposed mitigation defense algorithm, the system’s cyberattack resiliency is greatly improved.