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18 July 2023 Comparative analysis of network security vulnerability mining methods for electric power systems
Biao Bai, Emei Deng
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Proceedings Volume 12744, Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023); 127442R (2023) https://doi.org/10.1117/12.2689316
Event: Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023), 2023, Nanjing, China
Abstract
With the accelerated process of deep integration of industrialization and informatization, industrial control networks gradually move from closed to open, and the traditional information security risks are gradually introduced into industrial control networks. Gradually introduced into the industrial control network, how to accurately and efficiently mine industrial control network security vulnerabilities has gradually become a research hotspot. In this paper, we compare and analyze the false detection rate of the black-box genetic algorithm-based power system network security vulnerability mining method and the fuzzing test-based industrial control network protocol vulnerability mining method. The fuzzing test-based vulnerability mining method for industrial control network protocols revolves around the fuzzing test technology for industrial control network protocols, and a prototype system for vulnerability mining for industrial control network protocols Fuzzing test is designed and implemented for the above problems. Based on the black-box genetic algorithm is an all-round perception of the power system network posture, the overall security posture of the power system network is derived, the black-box genetic algorithm is introduced for black-box fuzzing testing, the target function is selected and test parameters are generated, the optimized samples are transmitted to the fuzzing test module, and the abnormalities are recorded in real time through the log monitoring test system. The results show that the black-box genetic algorithm-based power system network security vulnerability mining method has a low false detection rate and high reliability.
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Biao Bai and Emei Deng "Comparative analysis of network security vulnerability mining methods for electric power systems", Proc. SPIE 12744, Second International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2023), 127442R (18 July 2023); https://doi.org/10.1117/12.2689316
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KEYWORDS
Network security
Mining
Genetic algorithms
Control systems
Analytical research
Genetics
Statistical analysis
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