Network Intrusion Detection Method Based on Radial Basic Function Neural Network

Abstract

Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of radial basic function neural network (RBFNN), an intrusion detection method based on radial basic function neural network is presented in this paper. We construct the structure of RBFNN that used for detection network intrusion behavior, and adopt the K-nearest neighbor algorithm and least square method to train the network. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong function approach and fast convergence of radial basic function neural network, the network intrusion detection method based on radial basic function neural network can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.