Security and Resilience in Autonomous Vehicles: A Proactive Design Approach
Chieh Tsai, Murad Mehrab Abrar, Salim Hariri
TLDR
This paper introduces a proactive design for autonomous vehicles, enhancing security and resilience against cyberattacks via layered threat modeling and an adaptive architecture.
Key contributions
- Categorizes potential cyberattacks on AVs across perception, control, V2X, and software supply chain layers.
- Proposes an AV Resilient architecture with redundancy, diversity, adaptive reconfiguration, and intrusion detection.
- Validates the architecture on a Quanser QCar, detecting depth camera blinding and software tampering attacks.
- Demonstrates operational continuity under adversarial conditions using fast anomaly detection and fallback mechanisms.
Why it matters
This paper is crucial for developing safer autonomous vehicles by addressing critical security vulnerabilities. It provides a comprehensive attack taxonomy and a practical, resilient architecture. Experimental validation shows how AVs can maintain operation under attack, building trust in future transportation systems.
Original Abstract
Autonomous vehicles (AVs) promise efficient, clean and cost-effective transportation systems, but their reliance on sensors, wireless communications, and decision-making systems makes them vulnerable to cyberattacks and physical threats. This chapter presents novel design techniques to strengthen the security and resilience of AVs. We first provide a taxonomy of potential attacks across different architectural layers, from perception and control manipulation to Vehicle-to-Any (V2X) communication exploits and software supply chain compromises. Building on this analysis, we present an AV Resilient architecture that integrates redundancy, diversity, and adaptive reconfiguration strategies, supported by anomaly- and hash-based intrusion detection techniques. Experimental validation on the Quanser QCar platform demonstrates the effectiveness of these methods in detecting depth camera blinding attacks and software tampering of perception modules. The results highlight how fast anomaly detection combined with fallback and backup mechanisms ensures operational continuity, even under adversarial conditions. By linking layered threat modeling with practical defense implementations, this work advances AV resilience strategies for safer and more trustworthy autonomous vehicles.
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