The Research of Intrusion Detection Based on Support Vector Machine

Author

Bo, Li ; Yuan, Chen Yuan

Author_Institution

Network Inf. Center, Chongqing Univ. of Technol., Chongqing, China

fYear

2009

fDate

5-6 Dec. 2009

Firstpage

21

Lastpage

23

Abstract

Intrusion detection is developed quickly because which has important position in network security. The method of SVM based on statistics learning theory is used in the intrusion detection system, which classifies detecting data efficiently, and achieves the aim that SVM can accurately predict the abnormal state of system. By the use of this method, the limitation of traditional machine learning method is avoided and ensures the stronger extension ability which makes intrusion detection system to have the better detecting performance.

Keywords

computer network security; learning (artificial intelligence); pattern classification; statistical analysis; support vector machines; computer network security; detection data classification; intrusion detection system; machine learning method; statistics learning theory; support vector machine; Computer networks; Computer security; Data security; Information security; Intrusion detection; Leak detection; Learning systems; Protection; Support vector machine classification; Support vector machines; abnormal action; computer network; distort rate; intrusion detection; miss probability; normal action;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer and Communications Security, 2009. ICCCS '09. International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-0-7695-3906-5

Electronic_ISBN

978-1-4244-5408-2

Type

conf

DOI

10.1109/ICCCS.2009.43

Filename

5380372