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
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;
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
DOI :
10.1109/ICSPS.2010.5555559
