关键词: 网络安全态势感知, 电力信息网络, 网络攻击, 线性判别分析(LDA), RBF神经网络

Abstract:

To accurately predict the security situation of power information network,a network security situation awareness method based on machine learning is proposed.In this method,the network security situation awareness is abstracted as a numerical quantization problem,and a large number of test samples are used as data sources to input the situational awareness model to characterize the perceived results.Based on the linear discriminant analysis (LDA),the test data is preprocessed to optimize the sample data to obtain combined features and find out the best projection.Then the processed data are used as input of RBF neural network to find the nonlinear mapping relation of the network situation value,and the network security situation is quantified.Finally,the effectiveness of the proposed method in the security situation analysis is verified through KDD Cup99 dataset and the cyber attack data in the power information network.

Key words: network security situation awareness, power information network, cyber attack, linear discriminant analysis(LDA), RBF neural network