An improved genetic algorithm based on quantitative data-network vulnerability testing as a sample

He Li; Yulin Yan; Pu Fan; JieYin Huang; Tao Wan

doi:10.1117/12.3012638

21 December 2023 An improved genetic algorithm based on quantitative data-network vulnerability testing as a sample

He Li, Yulin Yan, Pu Fan, JieYin Huang, Tao Wan

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129703M (2023) https://doi.org/10.1117/12.3012638
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

The application of Internet provides a large amount of data basis for network vulnerability testing, but there are problemsof poor data quality and low accuracy. In order to fix this problem, this paper designs a network vulnerabilitytest method based on improved genetic algorithm. By improving and optimizing the encoding mode and fitness function, thetest data mutation library is integrated when genetic operation is carried out on the variable data segment of protocol message, which improves the efficiency of vulnerability mining. With the help of global search ability andcycliciteration of genetic algorithm, the test data with high code coverage is finally generated. Experimental results showthat this method is easier to trigger program anomalies than random search method, and improves the computational efficiency of genetic algorithm.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

He Li, Yulin Yan, Pu Fan, JieYin Huang, and Tao Wan "An improved genetic algorithm based on quantitative data-network vulnerability testing as a sample", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129703M (21 December 2023); https://doi.org/10.1117/12.3012638

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