基于改进遗传算法的Modbus协议模糊测试  

An Improved Genetic Algorithm for Fuzzing Modbus Protocols

作　　者：董宇 DONG Yu(College of Computer and Information Science of China Three Gorges University,Yichang 443000,China)

机构地区：[1]三峡大学计算机与信息学院,湖北宜昌443000

出　　处：《长江信息通信》2025年第2期53-56,共4页Changjiang Information & Communications

摘　　要：在工业控制协议漏洞挖掘领域中,模糊测试展现出显著的有效性。然而,传统模糊测试中存在着测试用例的生成冗余度高及效率低的问题,针对这些问题,文章提出了一种基于改进遗传算法的Modbus协议模糊测试方法IFSGA-Fuzzer。该方法通过构建更为高效的适应度函数,并引入一种创新的选择策略来优化种群中的个体,从而实现了对传统模糊测试的优化。在相同的实验环境中,我们将所提出的IFSGA-Fuzzer与开源模糊测试工具Peach进行了对比测试。实验结果证明,IFSGA-Fuzzer不仅有效缓解了传统遗传算法中易早收敛的问题,而且相较于Peach,在执行相同数量的测试用例时,能够触发更多的异常情况,这进一步证实了IFSGA-Fuzzer在生成高效测试用例方面的优越性。此外,IFSGA-Fuzzer方法计算的适应度值稳定且高于Peach。In the field of industrial control protocol vulnerability discovery,fuzz testing has demonstrated significant effectiveness.However,traditional fuzz testing often suffers from high redundancy in test case generation and low efficiency.To address these issues,this paper proposes an improved genetic algorithm-based Modbus protocol fuzz testing method,IFSGA-Fuzzer.This approach constructs a more efficient fitness function and introduces an innovative selection strategy to optimize the individuals in the population,thereby enhancing the traditional fuzz testing process.In a comparative experiment with the open-source fuzz testing tool Peach,IFSGA-Fuzzer was tested under the same experimental conditions.The results demonstrate that IFSGA-Fuzzer not only effectively alleviates the early convergence problem common in traditional genetic algorithms but also,compared to Peach,triggers more exceptions when executing the same number of test cases.This further confirms the superiority of IFSGA-Fuzzer in generating efficient test cases.Additionally,the fitness values calculated by the IFSGA-Fuzzer method are stable and higher than those of Peach.

关 键 词：模糊测试 遗传算法 Modbus TCP 适应度函数 漏洞挖掘 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]