The ISC International Journal of Information Security

Abstract Autonomous vehicles must balance road-safety objectives with growing cybersecurity threats. In this paper, we present a reinforcement-learning framework that jointly optimizes driving performance and resilience to Denial-of-Service (DoS) attacks.The problem is formulated as a multi-objective Markov Decision Process that integrates a safety reward with a security reward, while the partial observability of attacks is captured via a Bayesian belief. A Proximal Policy Optimization (PPO) agent controls steering, throttle, and dedicated mitigation actions. The system is implemented in the CARLA simulator with camera and LiDAR inputs and evaluated on urban driving scenarios. Experimental results demonstrate that the agent sustains stable lane-keeping and target-speed performance, while substantially reducing collision-prone incidents and retaining more than 90 % of the nominal travel distance under attack scenarios. The framework outperforms the safety-only PPO baseline and a rule-based security countermeasure.