Computer Science ›› 2016, Vol. 43 ›› Issue (Z11): 388-392.doi: 10.11896/j.issn.1002-137X.2016.11A.089

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Network Security Situation Prediction Model Based on RAN-RBF Neural Network

GAN Wen-dao, ZHOU Cheng and SONG Bo   

  • Online:2018-12-01 Published:2018-12-01

Abstract: In order to know the development of network security situation more accurately,a model of network security situation predicition (NSSP) based on resource allocating network radical basis function (RAN-RBF) neural network was proposed.The model uses the algorithm of resource allocating network to cluster the samples of network security situation,and get the number of the hidden layer nodes of neural network,introducing pruning strategies to remove nodes that contribute little to the network,the neural network of centers,widths and the weights are optimized by modified particle swarm optimization (MPSO) algorithm,to predict the future network security situation.Using the data provided by the network management department of campus network simulation experiments show that compared with K-means clustering RBF neural network prediction model,the model can get more appropriate RBF neural network structure and control parameters,to improve the accuracy of the predictions,more directly reflects the overall situation of the network security situation and provide situation map for the network security administrators.

Key words: Resource allocating network radical basis function (RAN-RBF) neural network,Network security situation prediction (NSSP),Modified particle swarm optimization (MPSO),Situation map

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