一种基于强化学习的PE恶意软件对抗样本生成方法OA

This paper proposes a reinforcement learning-based method for generating adversarial ex-amples against PE malware.Firstly,it regards the generation of adversarial examples for PE malware as a sequence-to-sequence generation task,which models sequences on an offline reinforcement learning dataset and leverages the powerful sequence generation capability of Transformer by incrementally gen-erating sequences through predicting actions at each step.Furthermore,an information transmission mechanism is introduced to facilitate cross-episode information transfer during the reinforcement learn-ing process,enhancing data efficiency.Experimental results demonstrate that the evasion rate of PE malware adversarial examples generated using this method outperforms those in comparative experi-ments and exhibits transferability.