期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：1
页码：332-335
语种：English
出版社：Ayushmaan Technologies
摘要：The network traffc data provided for the design of intrusiondetection system always are large with ineffective information,thus we need to remove the worthless information from theoriginal high dimensional database. In this paper, we comparethe performance of two features reduction techniques on NSLKDD dataset, which is now publicly available for the evaluationof Intrusion Detection System. These feature reduction techniquesinclude Principal Component Analysis, Linear DiscriminantAnalysis. After reduction Error Back-Propagation Algorithmis used for classifcation. Our results shows that PCA performsbetter than LDA with small data set and with large data set LDAis superior than PCA.
