Best hybrid classifiers for intrusion detection

Authors:
Sanaa Kholfi;Muhammad Habib;Sultan Aljahdali
Affiliations:
(Corresponding author. E-mail: skholfi@gmu.edu) School of Information Technology, George Mason University, Fairfax, VA 22033, USA;School of Information Technology, George Mason University, Fairfax, VA 22033, USA;College of Business Administration (CBA), P.O. Box 110200, Jeddah 21361, Saudi Arabia
Venue:
Journal of Computational Methods in Sciences and Engineering - Selected papers from the International Conference on Computer Science, Software Engineering, Information Technology, e-Business, and Applications, 2004
Year:
2006

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Abstract

We present in this paper an intrusion detection software-system that we have built based on combined statistical and computational models to detect intrusions and classify them as attack or non-attack. More specifically, we build a computational machine to derive optimal parsimonious hybrid model of classifiers in intrusion detection. The classifiers are based on the following classification methods, Naïve Bayes-NB, K-nearest neighbor-K-nn, and Neural networks-NN.