A guided processor security fuzz testing scheme based on Q-learning reinforcement learning
A guided processor security fuzz testing scheme based on Q-learning reinforcement learning was proposed to address the issue of blindness in genetic algorithms during fine-grained mutations for processor security fuzz testing,which often leads to test cases triggering the same type of vulnerability.By constructing a reward function using the state values of test cases and the weights corresponding to the types of triggered vulnerabilities,reinforcement learning was adopted to guide the generation of targeted and directional test cases,quickly triggering a variety of vulnerabilities.Experiments on the Hikey970 platform verified the effectiveness of the ARM v8-based test case generation framework.Compared with the traditional strategy using genetic algorithms as feedback,this scheme generates 19.15％more effective test cases and identifies 80.00％more types of vulnerabilities within the same time frame.
万方数据
维普
