Research on intrusion detection of SVM based on PSO

Author

Zhou, Tie-jun ; Li, Yang ; Li, Jia

Author_Institution

Sch. of Comput. & Commun., Hunan Univ., Changsha, China

Volume

2

fYear

2009

fDate

12-15 July 2009

Firstpage

1205

Lastpage

1209

Abstract

Intrusion detection plays more important role in network security today. This paper introduces a method, particle swarm optimization and support vector machine, to intrusion detection system, and presents a new design of ID Based on particle swarm optimization and support vector machine. This paper presents an optimal selection approach of the SVM parameters (regulation parameter C and the radial basis function width parameter sigma ) based on particle swarm optimization algorithm. The experiments show that the optimal parameter selection approach based on PSO is available and the research of intrusion detection based on particle swarm optimization and support vector machine is effective in reducing the number of alerts, false positive, false negative better.

Keywords

computer networks; particle swarm optimisation; radial basis function networks; security of data; support vector machines; telecommunication security; PSO; SVM; intrusion detection; network security; optimal parameter selection; particle swarm optimization; radial basis function width parameter; regulation parameter; support vector machine; Computer networks; Computer security; Cybernetics; Decision making; Intrusion detection; Machine learning; Particle swarm optimization; Risk management; Support vector machine classification; Support vector machines; Intrusion detection; Parameter optimization; Particle swarm optimization algorithm; Support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2009 International Conference on

Conference_Location

Baoding

Print_ISBN

978-1-4244-3702-3

Electronic_ISBN

978-1-4244-3703-0

Type

conf

DOI

10.1109/ICMLC.2009.5212467

Filename

5212467