DocumentCode
2192515
Title
Intrusion Detection Model Based on Improved Support Vector Machine
Author
Yuan, Jingbo ; Li, Haixiao ; Ding, Shunli ; Cao, Limin
Author_Institution
Inst. of Inf. Manage. Technol. & Applic., Northeastern Univ. at Qinhuangdao, Qinhuangdao, China
fYear
2010
fDate
2-4 April 2010
Firstpage
465
Lastpage
469
Abstract
With development and popularization of computer network, network security problems increasingly bring into prominence. Intrusion detection technique can effectively enlarge the scope of protection on network and system. An intrusion detection method based on support vector machine (SVM) is studied. Aiming at the shortcoming of SVM on detecting precision, an intrusion detection model based on improved SVM is put forward according to hypothesis test theory. To confirm the effectiveness of this approach, a simulation testing is done. The experiment results show that the improved SVM has stronger learning ability and higher accuracy and lower false positive rate.
Keywords
security of data; support vector machines; SVM; hypothesis test theory; improved support vector machine; intrusion detection model; network security; Computer security; Data mining; Data security; Information security; Intrusion detection; Operating systems; Support vector machine classification; Support vector machines; Testing; Training data; hypothesis test theory; intrusion detection; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Information Technology and Security Informatics (IITSI), 2010 Third International Symposium on
Conference_Location
Jinggangshan
Print_ISBN
978-1-4244-6730-3
Electronic_ISBN
978-1-4244-6743-3
Type
conf
DOI
10.1109/IITSI.2010.72
Filename
5453618
Link To Document