Network intrusion detection: Evaluating cluster, discriminant, and logit analysis

This source preferred by Vasilis Katos

This data was imported from Scopus:

Authors: Katos, V.

Journal: Information Sciences

Volume: 177

Issue: 15

Pages: 3060-3073

ISSN: 0020-0255

DOI: 10.1016/j.ins.2007.02.034

This paper evaluates the statistical methodologies of cluster analysis, discriminant analysis, and Logit analysis used in the examination of intrusion detection data. The research is based on a sample of 1200 random observations for 42 variables of the KDD-99 database, that contains 'normal' and 'bad' connections. The results indicate that Logit analysis is more effective than cluster or discriminant analysis in intrusion detection. Specifically, according to the Kappa statistic that makes full use of all the information contained in a confusion matrix, Logit analysis (K = 0.629) has been ranked first, with second discriminant analysis (K = 0.583), and third cluster analysis (K = 0.460). © 2007 Elsevier Inc. All rights reserved.

The data on this page was last updated at 04:57 on March 19, 2018.