Securing Autonomous Vehicles: Fundamentals, Challenges, and Perspectives

This work introduces a comprehensive methodology aimed at enhancing security and immunity in automotive networks, placing a primary focus on the detection, prediction, and forecasting of errors in autonomous vehicles. Conventional approaches to vehicle cybersecurity often struggle to keep pace with evolving threats and provide effective error detection mechanisms. Our proposed methodology seeks to bridge this gap by incorporating a hybrid approach that combines both model and data. This integration ensures the development of secure systems and facilitates real-time analysis of deployed systems, enabling the proactive prevention of errors and attacks based on collected data. The overarching goal is to leverage data to not only prevent attacks but also rectify errors within autonomous vehicles.