Can Cyberattacks on Adaptive Cruise Control Vehicles Be Effectively Detected?

Automated Vehicles (AVs), particularly those with Adaptive Cruise Control (ACC), are increasingly integral to intelligent transportation systems, but they bring new cybersecurity challenges. This study explores the subtleties of cyberattacks targeting ACC vehicles, specifically through false data injection, and assesses their impact on traffic dynamics. We innovatively adapt and implement strategically designed cyberattacks in simulations, providing a realistic evaluation of their effects on traffic. Our approach not only synthesizes these attacks but also rigorously tests their detectability against state-of-the-art detection algorithms. The findings reveal the intrinsic difficulty in detecting such stealthily designed attacks, highlighting a significant gap in current cybersecurity measures. Despite the precision of our detection methods, the low recall rates emphasize the stealthiness of these attacks. The open-sourced experiment code is made available at https://github.com/tianyi17/simulations_IV24. This research accentuates the urgent need for more sophisticated detection and defense strategies to protect ACC vehicles against evolving cyber threats, ensuring the reliability and safety of future transportation systems.