A Cyberattack Detection-Isolation Scheme For CAV Under Changing Driving Environment

TL;DR

This paper presents a novel cyberattack detection and isolation scheme for Connected Autonomous Vehicles (CAVs) that adapts to changing driving environments, enhancing cybersecurity by identifying and isolating malicious communication within vehicle platoons.

Contribution

It introduces a unified detection-isolation algorithm using residual generators with Lyapunov-based guarantees, tailored for dynamic driving conditions and real-world data.

Findings

Effective detection of cyberattacks in CAV platoons

Successful isolation of infrastructure-level traffic manipulation

Enhanced cybersecurity robustness in changing environments

Abstract

Under a changing driving environment, a Connected Autonomous Vehicle (CAV) platoon relies strongly on the acquisition of accurate traffic information from neighboring vehicles as well as reliable commands from a centralized supervisory controller through the communication network. Even though such modalities are imperative to ensure the safe and efficient driving performance of CAVs, they led to multiple security challenges. Thus, a cyberattack on this network can corrupt vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, which can lead to unsafe or undesired driving scenarios. Hence, in this paper, we propose a cyberattack detection-isolation algorithm comprised of a unified V2V and V2I cyberattack detection scheme along with a V2I isolation scheme for CAVs under changing driving conditions. The proposed algorithm is constructed using a bank of residual generators with Lyapunov function-based performance guarantees, such as disturbance-to-state stability, robustness, and sensitivity. Finally, we showcase the efficacy of our proposed algorithm through extensive Monte-Carlo simulations using real-world highway and urban driving data. The results show that the proposed algorithm can enhance the cybersecurity of CAVs by detecting cyberattacks on CAV platoons and isolating infrastructure-level traffic manipulation.