Coverage-guided fuzz testing method based on reinforcement learning seed scheduling

Abstract

Abstract The existing fuzz testing methods for industrial control protocols suffer from insufficient coverage, false positives, and an inability to handle protocol semantics. This paper proposes a reinforcement learning-based seed scheduling coverage-guided fuzz testing method. Building upon coverage-guided fuzz testing techniques, we integrate reinforcement learning with seed scheduling to optimize the seed selection strategy, thereby enhancing the efficiency of protocol vulnerability detection. Experimental results demonstrate the feasibility and effectiveness of this approach. Through reinforcement learning guidance, seed scheduling is optimized, thereby strengthening the performance of fuzz testing in exploring vulnerabilities in industrial control protocols.