AMCFF-RL: An Adaptive Multi-Modal CAN Bus Fuzzing Framework Leveraging Deep Reinforcement Learning

The increasing complexity and connectivity of modern vehicles have made automotive networks, particularly the Controller Area Network (CAN) bus, vulnerable to cyberattacks. Fuzzing is a critical technique for proactively finding security weaknesses, but traditional methods are inefficient and struggle to scale with the complexity of modern vehicles. This paper introduces AMCFF-RL, an adaptive framework that uses Deep Reinforcement Learning (DRL) with multi-modal feature extraction to systematically analyse for vulnerabilities. Rather than relying on unguided or purely random fuzzing, AMCFF-RL integrates multi-modal feature extraction with DRL and advanced visualization, allowing it to learn and adapt its strategy based on real-time feedback from the network and thereby improve the efficiency and effectiveness of the fuzzing process. Comprehensive visualization tools serve a dual purpose: they offer human-interpretable insights while also generating rich feature representations that support the anomaly detection pipeline and the DRL agent.