A Formal Verification-Based Risk Assessment Framework for the Authentication of Self-driving Vehicles

With self-driving vehicles becoming more and more part of the modern transportation, secure communication among vehicle to vehicle, infrastructure and cloud services are very important. Autonomous vehicles (AVs) face various security threats because they rely on wireless communications to interact with infrastructure (V2I) and other vehicles (V2V). The security and the integrity of such communication are essential, especially in large-scale network-topologies, as well as in real-time and scalable authentication processes. We proposed a framework that uses formal verification techniques to systematically identify, assess, and mitigate potential risks associated with authentication protocols in autonomous vehicles. By Incorporating probabilistic model checking, the framework facilitates a more rigorous approach to security and increases the resilience of authentication mechanisms in diverse environments and operating conditions. Our risk assessment methodology considers authentication failures caused by network delays, adversarial attacks (such as man-in-the-middle and replay attacks), and data breaches. The proposed framework is compared with existing authentication and risk assessment models and demonstrates superior accuracy, scalability, and formal security guarantees. This paper highlights the need for formal methods to provide highly secure authentication solutions for AVs and positions this framework as a major step forward in AV security in general.