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    Case Optimization Using Improved Genetic Algorithm for Industrial Fuzzing Test

    Ming Wan1, Shiyan Zhang1, Yan Song2, Jiangyuan Yao3,*, Hao Luo1, Xingcan Cao4

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 857-871, 2021, DOI:10.32604/iasc.2021.017214

    Abstract Due to the lack of security consideration in the original design of industrial communication protocols, industrial fuzzing test which can successfully exploit various potential security vulnerabilities has become one new research hotspot. However, one critical issue is how to improve its testing efficiency. From this point of view, this paper proposes a novel fuzzing test case optimization approach based on improved genetic algorithm for industrial communication protocols. Moreover, a new individual selection strategy is designed as the selection operator in this genetic algorithm, which can be actively engaged in the fuzzing test case optimization process. In this individual selection strategy,… More >

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