Optimal Hybrid Model of Classifiers for Intrusion Detection
Sanaa Kholfi, Muhammad Habib, Sultan Aljahdali, Saudi Arabia · 2004
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, Support vector machine-SVM, K-nearest neighbor-K-nn, and Neural networks-NN.