A New Feature Extraction Method of Intrusion Detection

Author

Xiaorong, Zhu ; Dianchun, Wang ; Changguo, Ye

Author_Institution

Dept. of Inf. Sci. & Technol., Taishan Coll., Taian

Volume

2

fYear

2009

fDate

7-8 March 2009

Firstpage

504

Lastpage

507

Abstract

The paper uses kernel principal component analysis to extract features from the intrusion detection training samples. The method extracts features and reduces the dimensions very effectively. In addition, we make use of RSVM method into nonlinear proximal SVM. It can reduce the computation requirements of the kernel matrix. The combination of the above two methods improve the training speed and classification effect.

Keywords

data mining; feature extraction; learning (artificial intelligence); matrix algebra; pattern classification; principal component analysis; security of data; support vector machines; RSVM method; classification method; data mining; feature extraction method; intrusion detection; kernel matrix; kernel principal component analysis; nonlinear proximal SVM; training sample; Data mining; Educational institutions; Educational technology; Feature extraction; Intrusion detection; Kernel; Paper technology; Principal component analysis; Support vector machine classification; Support vector machines; KPCA; PSVM; RSVM; intrusion detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on

Conference_Location

Wuhan, Hubei

Print_ISBN

978-1-4244-3581-4

Type

conf

DOI

10.1109/ETCS.2009.373

Filename

4959088