An Analytical Framework for Modeling and Synthesizing Malicious Attacks on Adaptive Cruise Control Vehicles

Abstract

While emerging adaptive cruise control (ACC) technologies are being increasingly integrated into vehicles, they also introduce vulnerabilities to potential malicious cyberattacks. Previous research has often focused on constant or stochastic attacks without explicitly addressing their malicious and covert characteristics. Consequently, these attacks may inadvertently benefit compromised vehicles, which is inconsistent with real-world scenarios. In contrast, we establish an analytical framework for modeling and synthesizing a range of candidate attacks, offering a physical interpretation from the attacker’s perspective. Specifically, we introduce a mathematical framework that describes mixed traffic scenarios, including both ACC vehicles and human-driven vehicles (HDVs), based on car-following dynamics. Within this framework, we synthesize and integrate a class of false data injection attacks into ACC sensor measurements, thereby influencing traffic flow dynamics. As a first-of-its-kind study, our work provides an analytical characterization of attacks, emphasizing their malicious and stealthy attributes while explicitly accounting for vehicle driving behavior, resulting in a set of candidate attacks with physical interpretability. To demonstrate the modeling process, we conduct a series of numerical simulations to holistically assess the effects of these attacks on car-following dynamics, traffic efficiency, and vehicular fuel consumption. Our primary findings indicate that strategically synthesized candidate attacks can cause significant disruptions to traffic flow while subtly altering the driving behavior of compromised ACC vehicles to remain stealthy, as supported by a series of analytical results.