Title :
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
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;
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
DOI :
10.1109/ICMLC.2009.5212467
