Stealthy Cyber-Attacks on Vehicle Lateral Dynamics: A System-Theoretic Analysis

TL;DR

This paper analyzes the feasibility and impact of stealthy cyber-attacks on vehicle lateral dynamics using a system-theoretic approach, considering different sensor configurations and attack types.

Contribution

It provides a systematic analysis of replay, zero dynamics, and covert attacks on vehicle lateral control, highlighting how sensor choices influence attack detectability and system vulnerability.

Findings

Replay attacks are largely model-agnostic.

Output selection can eliminate unstable zero dynamics.

Covert attacks enable sustained stealthy deviations when system access is sufficient.

Abstract

This paper studies the vehicle bicycle model under three classes of stealthy cyber-attacks: replay attacks, zero dynamics attacks, and covert attacks. Using a system-theoretic framework, we analyze the feasibility and impact of these attacks on vehicle lateral dynamics. The investigation considers different measurement configurations, including yaw rate, lateral acceleration, and longitudinal acceleration outputs, to evaluate how sensor selection influences attack detectability and system vulnerability. Each attack class is characterized in terms of required system knowledge, communication access, and impact. The analysis shows that replay attacks remain largely model-agnostic, while zero dynamics attacks are fundamentally constrained by control-oriented design choices, particularly output selection, which can eliminate unstable zero dynamics and limit the attack impact. In contrast, covert attacks, enabled by coordinated actuator and sensor manipulation, allow sustained and stealthy deviation of lateral states when sufficient access and system knowledge are available. The effects of actuator and tire saturation are also examined, revealing attack-dependent impacts on stealthiness and effectiveness. Finally, simulation case studies are conducted by using CarSim-Simulink co-simulation to validate and verify the theoretical results.