网络安全与数据治理2024，Vol.43Issue(7)：13-20,8.DOI:10.19358/j.issn.2097-1788.2024.07.003
基于生成对抗网络的工控协议模糊测试研究
Research on fuzzing of industrial control protocol based on generative adversarial network
摘要
Abstract
Traditional fuzzing relies on expert knowledge and protocol specifications,while neural network-based methods are constrained by the quality of training data and model structure.These methods exhibit poor effectiveness across different Industrial Control Protocols(ICPs)and lack a universal and efficient fuzzing approach.To address these issues,this paper proposes an ICP fuzzing method based on Wasserstein Generative Adversarial Network with Gradient Penalty(WGAN-GP),incorporating statisti-cal language model N-gram to refine the training results.This paper developed a universal fuzzing framework,GPFuzz,tailored for various ICPs.Experiments conducted in laboratory's oil and gas collection and transmission full-process industrial attack-defense range on three common ICPs(Modbus/TCP,Ethernet/IP,S7comm)demonstrate that the framework generates diverse test cases.These cases outperform other fuzzing methods in terms of acceptance rate and anomaly triggering indicators,providing an efficient and general security assessment method for ICS and enhancing the overall system security.关键词
漏洞挖掘/模糊测试/工业控制协议/生成对抗网络Key words
vulnerability mining/fuzzing/industrial control protocol/generative adversarial networks分类
信息技术与安全科学引用本文复制引用
宗学军,隋一凡,王国刚,宁博伟,何戡,连莲,孙逸菲..基于生成对抗网络的工控协议模糊测试研究[J].网络安全与数据治理,2024,43(7):13-20,8.基金项目
辽宁省自然科学基金项目(2023-MSLH-273) （2023-MSLH-273）
辽宁省科学技术计划项目(2023JH1/10400082) （2023JH1/10400082）
辽宁省人工智能创新发展计划项目(2023JH26/1030008) （2023JH26/1030008）
辽宁省科技创新平台建设计划项目([2022]36号) （[2022]36号）
