A Risk Assessment Model Based on Niche Genetic Algorithm for Power System Information Security

doi:10.12204/j.issn.1000-7229.2021.03.011

Abstract

Abstract:

Information security assurance is essential for the safe and stable operation of power cyber-physical systems. The key is to conduct all-round real-time monitoring of power cyber-physical systems and analyze the collected massive monitoring data to make accurate security risk assessments. As an evolutionary algorithm for pattern classification, the gene expression programming (GEP) has received widespread attention due to its ability to perform global search, but its operation on high-dimensional data sets is extremely time-consuming. This paper proposes an enhanced GEP algorithm for power grid information security risk assessment. The algorithm first uses the idea of rough set, solves the optimal attribute through the discrimination function to reduce the data sample, and then uses the niche genetic algorithm improves the diversity of the reduced sample individuals to accelerate the convergence speed of the GEP algorithm, and then realizes the global search through the genetic algorithm and obtains the level assessment of the security risk. Simulation results show that, compared with traditional security risk assessment algorithms, the enhanced GEP algorithm proposed in this paper has a higher attribute reduction rate and global convergence rate, and can quickly implement risk assessment from massive monitoring data of grid security information.

Key words: power grid information system, security risk assessment, gene expression programming, niche, attribute reduction

CLC Number:

TM769

LI Jiawei, WU Kehe, ZHANG Bo. A Risk Assessment Model Based on Niche Genetic Algorithm for Power System Information Security[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(3): 89-96.

Figures/Tables 10

Fig.1 Algorithm flowchart of security risk assessment model

Fig.2 Relationship between niche algorithm fitness loss rate and parameter selection R, V

Fig.3 Comparison of fitness loss rate between original GEP algorithm and proposed algorithm

Fig.4 Comparison of attribute reduction rate among AR-DF, PCA and SVD

Fig.5 Time-consumption comparison among AR-DF, PCA and SVD

Fig.6 Comparison of convergence rates between proposed and traditional algorithm and T_GEP

Fig.7 Comparison of risk assessment time-consumption of proposed algorithm before and after attribute reduction

Fig.8 Comparison of the real value of risk assessment and model value based on proposed algorithm fortraining data set

Fig.9 Comparison between real value and model value of risk assessment based on proposed algorithm for test data set

Fig.10 Comparison of the evaluation results among three genetic algorithms

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