Cyber-Resilient Data-Driven Event-Triggered Secure Control for Autonomous Vehicles Under False Data Injection Attacks

TL;DR

This paper introduces a cyber-resilient control framework for autonomous vehicles that combines data-driven modeling, event-triggered communication, and advanced control techniques to counteract false data injection attacks effectively.

Contribution

It presents a novel integrated framework using data-driven modeling, event-triggered communication, and fractional-order sliding mode control for enhanced security and efficiency in autonomous vehicle control.

Findings

Significant reduction in communication load through event-triggered scheme.

Effective mitigation of false data injection attacks demonstrated in simulations.

Guaranteed system stability with exponential error convergence.

Abstract

This paper proposes a cyber-resilient secure control framework for autonomous vehicles (AVs) subject to false data injection (FDI) threats as actuator attacks. The framework integrates data-driven modeling, event-triggered communication, and fractional-order sliding mode control (FSMC) to enhance the resilience against adversarial interventions. A dynamic model decomposition (DMD)-based methodology is employed to extract the lateral dynamics from real-world data, eliminating the reliance on conventional mechanistic modeling. To optimize communication efficiency, an event-triggered transmission scheme is designed to reduce the redundant transmissions while ensuring system stability. Furthermore, an extended state observer (ESO) is developed for real-time estimation and mitigation of actuator attack effects. Theoretical stability analysis, conducted using Lyapunov methods and linear matrix inequality (LMI) formulations, guarantees exponential error convergence. Extensive simulations validate the proposed event-triggered secure control framework, demonstrating substantial improvements in attack mitigation, communication efficiency, and lateral tracking performance. The results show that the framework effectively counteracts actuator attacks while optimizing communication-resource utilization, making it highly suitable for safety-critical AV applications.