March 3, 2026Open Access

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

Key Points

AMCFF-RL enhances fuzzing efficiency and effectiveness by learning and adapting based on real-time feedback from the network.
The framework integrates multi-modal feature extraction with deep reinforcement learning for systematic vulnerability analysis.
Visualization tools provide human-interpretable insights and help generate feature representations for anomaly detection.
This approach offers a significant advantage over traditional fuzzing techniques, suggesting more robust cyber defense in modern vehicles.

Abstract

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.

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

Key Points

Abstract

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