On the Use of Belief Functions to Improve High Performance Intrusion Detection System

Abstract

Dempster-Shafer theory is a very powerful tool for data fusion, which provides a good estimation of imprecision, conflict from different sources and deal with any unions of hypotheses. In this paper, we propose to develop a high-performance hybrid Network Intrusion Detection System, based on belief functions. This system contains three levels, the first one includes two fast classifiers: Naïve Bayes and Support Vector Machine (SVM) Bused for their performance on classification. In the second level outputs of both SVM and Naïve Bayes are fuzzified using fuzzy logic. Third, the overall decision of the system is performed using Dempster's rule of combination. The experimentation on a recent benchmark dataset shows that our approach achieves a higher detection rate with low false alarm rates compared to some existing classifiers.