Comparing Single and Multiple Bayesian Classifiers Approaches for Network Intrusion Detection

Abstract

A general strategy for improving the performance of classifiers is to consider multiple classifiers approach. Previous research works have shown that combination of different types of classifiers provided a good classification results. We noticed a raising interest to incorporate single Bayesian classifier into the multiple classifiers framework. In this light, this research work explored the possibility of employing multiple classifiers approach, but limited to variations of Bayesian technique, namely Naïve Bayes Classifier, Bayesian Networks, and Expert-elicited Bayesian Network. Empirical evaluations were conducted based on a standard network intrusion dataset and the results showed that the multiple Bayesian classifiers approach gave insignificant increase of performance in detecting network intrusions as compared to a single Bayesian classifier. Naives Bayes Classifier should be considered in detecting network intrusions due to its comparable performance with multiple Bayesian classifiers approach. Moreover, time spent for building a NBC was less compared to others.