A New Intelligent Intrusion Detection Method Based on Attribute Reduction and Parameters Optimization of SVM

Author

Liu, Huaping ; Jian, Yin ; Liu, Sijia

Author_Institution

Sch. of Math. & Comput. Eng., Xihua Univ., Chengdu, China

Volume

1

fYear

2010

fDate

6-7 March 2010

Firstpage

202

Lastpage

205

Abstract

Intelligent algorithms applied in intrusion detection system has become a tendency in recent years. An intelligent intrusion detection method is presented, based on rough set theory (RST) and improved binary particle swarm optimization with supported vector machine (IBPSO-SVM), which is combined attribute reduction with parameters optimization. Experiments on KDD CUP´99 dataset show this method can be an effective way for intrusion detection, not only accelerating the training time, but also improving the accuracy of test.

Keywords

particle swarm optimisation; rough set theory; security of data; support vector machines; SVM; attribute reduction; binary particle swarm optimization; intelligent intrusion detection method; parameters optimization; rough set theory; supported vector machine; Computer networks; Computer security; Intrusion detection; Mathematics; Optimization methods; Particle swarm optimization; Set theory; Support vector machine classification; Support vector machines; Testing; binary particle swarm optimization (BPSO); network intrusion detection; rough set (RST); supported vector machine (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Education Technology and Computer Science (ETCS), 2010 Second International Workshop on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-6388-6

Electronic_ISBN

978-1-4244-6389-3

Type

conf

DOI

10.1109/ETCS.2010.210

Filename

5458855