DocumentCode :
1868515
Title :
Intrusion detection research based on improved PSO and SVM
Author :
Liu Ning ; Zhao Jianhua
Author_Institution :
Department of Computer Science, ShangLuo University, 726000, China
fYear :
2012
fDate :
3-5 March 2012
Firstpage :
1263
Lastpage :
1266
Abstract :
Based on the thought of crossover operation and mutation operation in genetic algorithm, this paper improves particle swarm optimization algorithm. The improved particle swarm optimization algorithm is used to optimize penalty parameter c and kernel function parameters g of SVM and the optimized model named new-PSO-SVM is established. KDD Cup 99 intrusion detection data set is used to carry out experiment. The results show that PSO optimization improves the classification accuracy rate of SVM.
Keywords :
Intrusion detection; Particle swarm optimization algorithm; Support vector machine;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Automatic Control and Artificial Intelligence (ACAI 2012), International Conference on
Conference_Location :
Xiamen
Electronic_ISBN :
978-1-84919-537-9
Type :
conf
DOI :
10.1049/cp.2012.1209
Filename :
6492816
Link To Document :
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