Attribute Reduction Method Applied to IDS

Author

Cheng, Xiang ; Liu, Bing-Xiang ; Zhang, Yi-Lai

Author_Institution

Inf. Eng. Inst., Jingdezhen Ceramic Inst., Jingdezhen, China

Volume

1

fYear

2010

fDate

12-14 April 2010

Firstpage

107

Lastpage

110

Abstract

In this paper, we apply a new linear correlation attribute reduction algorithm to feature selection. The algorithm is valuable when the features are marginally unrelated but jointly related to the response variable. A new technique is introduced to remove redundant attributes and it is effective to reduce the false selection rate in the feature selection stage. We train and test the new algorithm on KDD1999 data set, and compare the experiment results to illustrate the methodology.

Keywords

correlation methods; security of data; KDD1999 data set; false selection rate; feature selection; intrusion detection system; linear correlation attribute reduction algorithm; Adaptive systems; Ceramics; Data security; Information security; Intrusion detection; Mars; Mobile communication; Mobile computing; Predictive models; Testing; Rough Set (RS); artificial immune (AI); classification; feature selection; intrusion detection system (IDS); multivariate adaptive regression splines (MARS); support vector machines (SVM);

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Mobile Computing (CMC), 2010 International Conference on

Conference_Location

Shenzhen

Print_ISBN

978-1-4244-6327-5

Electronic_ISBN

978-1-4244-6328-2

Type

conf

DOI

10.1109/CMC.2010.202

Filename

5471504