计算机工程与科学2026，Vol.48Issue(4)：617-627,11.DOI:10.3969/j.issn.1007-130X.2026.04.006
一种基于强化学习的PE恶意软件对抗样本生成方法
A reinforcement learning-based method for generating adversarial examples against PE malware
摘要
Abstract
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.关键词
强化学习/对抗样本/PE恶意软件/恶意软件检测Key words
reinforcement learning/adversarial example/PE malware/malware detection分类
信息技术与安全科学引用本文复制引用
张朝然,马玉骐,张三峰,杨望..一种基于强化学习的PE恶意软件对抗样本生成方法[J].计算机工程与科学,2026,48(4):617-627,11.基金项目
国家重点研发计划(2022YFB3104601) （2022YFB3104601）
