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Connected and Automated Vehicle (CAV) technology has the potential to greatlyimprove transportation mobility, safety, and energy efficiency. However,ubiquitous vehicular connectivity also opens up the door for cyber-attacks. Inthis study, we investigate cybersecurity risks of a representative cooperativetraffic management application, i.e., highway on-ramp merging, in a mixedtraffic environment. We develop threat models with two trajectory spoofingstrategies on CAVs to create traffic congestion, and we also devise anattack-resilient strategy for system defense. Furthermore, we leverage VENTOS,a Veins extension simulator made for CAV applications, to evaluatecybersecurity risks of the attacks and performance of the proposed defensestrategy. A comprehensive case study is conducted across different trafficcongestion levels, penetration rates of CAVs, and attack ratios. As expected,the results show that the performance of mobility decreases up to 55.19% at theworst case when the attack ratio increases, as does safety and energy. With ourproposed mitigation defense algorithm, the system's cyber-attack resiliency isgreatly improved.

Evaluating Cybersecurity Risks of Cooperative Ramp Merging in Mixed Traffic Environments