Securing Autonomous Vehicles: Fundamentals, Challenges, and Perspectives

This paper 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 automotive systems.