DocumentCode :
2921035
Title :
Application of PSO-RBF Neural Network in Network Intrusion Detection
Author :
Chen, Zhifeng ; Qian, Peide
Author_Institution :
Soochow Univ., Suzhou, China
Volume :
1
fYear :
2009
fDate :
21-22 Nov. 2009
Firstpage :
362
Lastpage :
364
Abstract :
Detecting all kinds of intrusions efficiently is significant to network security. Radial basis function (RBF) neural network is a kind of feed forward neural network, which is widely employed as a real-time pattern classification. In RBF neural network, the center of radial basis function, the variance of radial basis of function and the weight have to be chosen. If they are not appropriately chosen, the RBF neural network may degrade validity and accuracy of modeling. Particle swarm optimization algorithm (PSO) is a member of the wide category of swarm intelligence methods to solve non-linear programming problems. PSO has proved to be competitive with genetic algorithm (GA) in parameter optimization. So PSO is used to optimize the RBF neural network parameters in this work. Therefore, the novel combination method based on RBF neural network and PSO (PSO-RBFNN) is adapted to network intrusion detection. The experimental results show that the proposed model is superior to the conventional RBF neural network.
Keywords :
nonlinear programming; particle swarm optimisation; pattern classification; radial basis function networks; security of data; PSO-RBF neural network; feedforward neural network; network intrusion detection; network security; nonlinear programming problems; particle swarm optimization algorithm; radial basis function neural network; real-time pattern classification; Artificial neural networks; Degradation; Feedforward neural networks; Feeds; Genetic algorithms; Information technology; Intelligent networks; Intrusion detection; Neural networks; Particle swarm optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-0-7695-3859-4
Type :
conf
DOI :
10.1109/IITA.2009.154
Filename :
5369634
Link To Document :
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