Title :
Comparison of Two Feature Selection Methods in Intrusion Detection Systems
Author :
Fadaeieslam, M.J. ; Minaei-Bidgoli, B. ; Fathy, M. ; Soryani, M.
Abstract :
The quality of features directly affects the performance of classification. Many feature selection methods introduced to remove redundant and irrelevant features, because raw features may reduce accuracy or robustness of classification. In this paper we proposed a new method for feature selection based on Decision Dependent Correlation (DDC). We have used SVM classifier and the results on DARPA KDD99 benchmark dataset indicate that the proposed method outperforms Principal Component Analysis (PCA).
Keywords :
pattern classification; principal component analysis; security of data; support vector machines; SVM classifier; classification; decision dependent correlation; feature selection method; intrusion detection system; principal component analysis; support vector machine; Information technology; Intrusion detection; Mutual information; Principal component analysis; Robustness; Support vector machine classification; Support vector machines; Telecommunication traffic; Testing; Training data;
Conference_Titel :
Computer and Information Technology, 2007. CIT 2007. 7th IEEE International Conference on
Conference_Location :
Aizu-Wakamatsu, Fukushima
Print_ISBN :
978-0-7695-2983-7
DOI :
10.1109/CIT.2007.99
