DocumentCode :
3444928
Title :
Minimax Probability Machine Classifier with Feature Extraction by Kernel Pca for Intrusion Detection
Author :
Chen, Zhenguo ; Ren, Hongde ; Du, Xingjing
Author_Institution :
Dept. of Comput. Sci. & Technol., North China Inst. of Sci. & Technol., Beijing
fYear :
2008
fDate :
12-14 Oct. 2008
Firstpage :
1
Lastpage :
4
Abstract :
In this paper, we synthetically applied kernel principal component analysis method and minimax probability machine to the intrusion detection. The intrusion features were extracted by kernel principal component analysis (KPCA) and followed by minimax probability machine (MPM) for classification. The dataset kddcup99 is our experiment data, our algorithm is compared with the algorithm which applies the support vector machine and minimax probability machine without application of feature extraction to intrusion detection. The experiment result has shown that the minimax probability machine classifier with feature extraction by KPCA can greatly reduce the training time and will not degrade the classifiers´ performance.
Keywords :
feature extraction; minimax techniques; pattern classification; principal component analysis; security of data; support vector machines; feature extraction; intrusion detection; kernel principal component analysis; minimax probability machine classifier; support vector machine; Computer vision; Data mining; Degradation; Feature extraction; Intrusion detection; Kernel; Minimax techniques; Principal component analysis; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
Type :
conf
DOI :
10.1109/WiCom.2008.1101
Filename :
4679009
Link To Document :
بازگشت