A method of network security situation prediction based on AC-RBF neural network
LI Fang-we · Journal of Chongqing University of Posts and Telecommunications · 2014
To grasp the trend of network security,a method of network security situation prediction( NSSP) based on adaptive clustering radical basis function( AC-RBF) neural network is proposed. The neural network hidden nodes are obtained by clustering the network security situation samples adaptively. We can train the neural network by gradient descent and find out the nonlinear relations among situation samples,to predict the future security situation. Experiment results show that,compared with the k-means RBF neural network and Support Vector Machine( SVM) prediction model,the proposed method can not only reflect the general trend of network security situation,but also can improve the prediction accuracy in the case of small-scale neural network. Finally,the proposed method can provide the network administrators with an intuitive network security situation map.