Research on Network Security Situation Prediction Technique Based on Online Learning RBFNN

doi:10.3969/j.issn.1671-1122.2016.04.004

Abstract

Abstract:

With the network attacks increasing rapaidly, the traditional network security technologies unable to meet the demand of network security. As a new and active network security defense technology, network security situation prediction comes forth. In the majority of cases the network security situation prediction technique based on artificial neural network adopt outline learning. It demanded design network structure and parameter ahead of schedule. If input stylebook dimension begins to change, the network structure and parameter must be designed again. This will undoubtedly increase the complexity of the operation and waste a lot of time. This paper researches the method of adaptive dynamic adjustment network structure of the online learning RBFNN. A group training method is put forward to train the network. This paper proposes a new method of network security situation prediction model based on online learning RBFNN.

Key words: network security situation prediction, online learning, RBFNN

CLC Number:

TP309

Limin XUE, Zhong LI, Wanwan LAN. Research on Network Security Situation Prediction Technique Based on Online Learning RBFNN[J]. Netinfo Security, 2016, 16(4): 23-30.

Figures/Tables 11

References 31

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