FMutator: A Probabilistic Mutation-Based Model for Enhanced Industrial Control Fuzz Testing

Fuzz testing has been recognized as a potent methodology for uncovering vulnerabilities in industrial control systems (ICS). In this work, we proposed a novel probabilistic mutation fuzz testing model named FMutator. FMutator is innovatively designed to augment the diversity of test cases in ICS fuzz testing and enhance the efficiency of vulnerability detection. FMutator integrates the principles of probabilistic mutation with capabilities of a Bi-directional long short-term memory (Bi-LSTM) network. This integration facilitates an end-to-end automated learning and evolutionary process, eliminating the need for manually defined encoding schemes or formulation of fitness functions. Experiments show that FMutator can autonomously learn mutation probabilities for each data field, effectively eliminating the necessity for prior protocol-specific knowledge. When benchmarked against traditional genetic mutation and contemporary deep learning algorithms, FMutator shows superior proficiency, particularly in terms of recognition rate and diversity of generated test cases. Experiments show that FMutator's test case recognition rate exceeds 80% across different sample sizes, highlighting FMutator's efficacy in enhancing ICS security, and marks a significant advancement in fuzz testing methodologies.