Intrusion risk detection method of power network based on dynamic correlation analysis
nhao Yu, Fuhua Luo, Xiang Guo · International Journal of Reasoning-based Intelligent Systems · 2024
Aiming at the low accuracy, recall rate, and F1 value of traditional intrusion risk detection methods, a dynamic association analysis based intrusion risk detection method for power networks is proposed. Firstly, the network intrusion detection data is normalised using the max-min method. Based on the data normalisation results, the power network intrusion feature dimensionality is reduced using the PCA-ReliefF method. Secondly, based on the dimensionality reduction results of network intrusion features, a dynamic association analysis method is used to calculate the specific weights of risk nodes, and the calculation results are graded to obtain network intrusion risk assessment results. Finally, based on the network intrusion risk assessment results, an artificial immune method is used to detect the power network intrusion risk. Experimental results show that the intrusion risk detection accuracy, recall rate, and F1 value of this method have been significantly improved.