By Topic

Application of PSO-RBF Neural Network in Network Intrusion Detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Zhifeng Chen ; Soochow Univ., Suzhou, China ; Peide Qian

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.

Published in:

Intelligent Information Technology Application, 2009. IITA 2009. Third International Symposium on  (Volume:1 )

Date of Conference:

21-22 Nov. 2009