The research on Fisher-RBF data fusion model of network security detection

Authors:
Jian Zhou;Juncheng Wang;Zhai Qun
Affiliations:
Center of Information and Network, Hefei University of Technology, Hefei, China;School of Computer & Information, Hefei University of Technology, Hefei, China;School of Foreign Studies, Hefei University of Technology, Hefei, China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Year:
2012

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Abstract

Based on the artificial neural network and means of classification, this paper puts forward the Fisher-RBF Data Fusion Model. Abandon redundant and invalid data and decrease dimensionality of feature space to attain the goal of increasing the data fusion efficiency. In the simulation, the experiment of the network intrusion detection is conducted by using KDDCUP'99_10percent data set as the data source. The result of simulation experiment shows that on a fairly large scale, Fisher-RBF model can increase detection rate and discrimination rate, and decrease missing-report rate and misstatement rate.