A Genetic algorithm-based method for tracing attack behaviors of power monitoring network. (1st March 2023)

• Record Type: Journal Article
• Authors: Li, Huixing; Zhai, Haibao; Ge, Minhui; Zhang, Liang; Shen, Guohui; Chen, Jing
• Is Part Of: Journal of physics. Volume 2457(2023)
• Publication Date: 2023-03-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2457/1/012051
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Power monitoring network attack detection and defense can only target known types of attacks. This simple defense detection method cannot effectively defend against complex network attacks, so it is necessary to adopt the tracing technology of actively searching for the source of attacks. This paper discusses the electric power monitoring and control network attack, attack tracing method, and genetic algorithm. Based on applications traceability method for electric power monitoring and control network attack behavior of power monitoring data collection network and the genetic algorithm, parameter settings were described. Furthermore, the process of tracing the attack behavior of the power monitoring network based on multi-level data association and genetic algorithm is designed and discussed. The success rate of tracing the attack behavior of the power monitoring network is analyzed by four operators in the genetic algorithm. The experimental data show that the success rate of the four operators in tracing the source of the three kinds of attacks in the power monitoring network is more than 97% on average. Therefore, the method of tracing the attack behavior of the power monitoring network based on the genetic algorithm has high practical value.