遗传算法在模糊测试中的应用研究综述  

Review of the Application of Genetic Algorithm in Fuzzing

作　　者：马航 董卫宇[1] 唐之卓 吕鑫 雷久刚 MA Hang;DONG Weiyu;TANG Zhizhuo;L Xin;LEI Jiugang(Cyberspace Security Institute,Information Engineering University,Zhengzhou 450001,China)

机构地区：[1]信息工程大学网络空间安全学院,郑州450001

出　　处：《小型微型计算机系统》2025年第4期948-957,共10页Journal of Chinese Computer Systems

基　　金：河南省重点研发专项项目(221111210300)资助.

摘　　要：遗传算法在模糊测试领域的应用提高了这一主流漏洞挖掘方法的性能和效率,诞生了一批研究成果.目前对基于遗传算法的模糊测试还没有全面的分析和总结.首先介绍了模糊测试、遗传算法,说明了遗传算法在模糊测试中应用的基本流程,其次,分3个方面(提高漏洞发现能力、执行效率和自动化程度)总结了遗传算法在模糊测试中的作用和实现方式,然后分析了遗传算法用于模糊测试的适应性改进(编码、进化组织、适应度函数设计),最后讨论了当前研究的不足并提出了进一步研究方向.The application of Genetic Algorithm(GA)in the field of fuzzing has improved the performance and efficiency of this mainstream vulnerability mining method,and produced a number of research results.At present,there is no comprehensive analysis and summary of GA based fuzzing.First,it introduces fuzzing and Genetic Algorithm,and explains the basic process of the application of GA in fuzzing.Secondly,it summarizes the role and the implementation of genetic algorithms in fuzzing in three aspects(improving bug-finding capabilities,execution efficiency,and automation).And then the adaptive improvement of GA for fuzzing(encoding,evolutionary organization,fitness function design)is analyzed.Finally,the shortcomings of current research are discussed and future research directions are proposed.

关 键 词：遗传算法 模糊测试 漏洞挖掘 适应度函数 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]