Network Intrusion Detection Method Based on Radial Basic Function Neural Network
作者： Jingwen Tian , Meijuan Gao , Fan Zhang
DOI: 10.1109/EBISS.2009.5138016
关键词:
摘要: Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency advantages of radial basic function neural (RBFNN), an detection method based on is presented in this paper. We construct structure RBFNN that used for behavior, adopt K-nearest neighbor algorithm least square to train network. discussed analyzed impact factor behaviors. With ability strong approach fast convergence network, can detect various rapidly effectively by learning typical characteristic information. The experimental result shows feasible effective.
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