DocumentCode
1716198
Title
Network intrusion detection by rough set and least squares support vector machine
Author
Xianhui, Duan ; Zhiguo, Liu ; Hua, Liu
Author_Institution
ShiJiaZhuang Coll., Shijiazhuang, China
Volume
1
fYear
2010
Abstract
The hybrid method of rough set and least squares support vector machine is presented to network intrusion detection in the paper. The 460 experimental data in KDDCUP99 are employed to research the proposed detection model. In the experimental data, 300 is the number of normal data, and the number of four fault types: DoS, R2L, U2R and Probe is 40 respectively. The experimental results show that the detection accuracy of RS-LSSVM is superior to SVM and BPNN.
Keywords
computer network security; least squares approximations; rough set theory; support vector machines; BPNN; SVM; least squares support vector machine; network intrusion detection; rough set; Accuracy; Data models; Intrusion detection; Probes; Signal processing; Support vector machines; Training; classifier; detection; least squares support vector machine; network intrusion;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Systems (ICSPS), 2010 2nd International Conference on
Conference_Location
Dalian
Print_ISBN
978-1-4244-6892-8
Electronic_ISBN
978-1-4244-6893-5
Type
conf
DOI
10.1109/ICSPS.2010.5555559
Filename
5555559
Link To Document