Security and Resilience in Autonomous Vehicles: A Proactive Design Approach

TL;DR

This paper introduces a proactive design framework for enhancing the security and resilience of autonomous vehicles through layered threat modeling, anomaly detection, and practical defense mechanisms validated on a real platform.

Contribution

It presents a comprehensive AV resilient architecture integrating redundancy, diversity, and adaptive reconfiguration supported by anomaly detection techniques.

Findings

Effective detection of depth camera blinding attacks

Successful identification of software tampering in perception modules

Operational continuity maintained under adversarial conditions

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.