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
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;
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
DOI :
10.1109/IITSI.2010.72