Title :
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
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);
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
DOI :
10.1109/CMC.2010.202
