Constructing a fuzzy network intrusion classifier based on differential evolution and harmonic search

Abstract

This paper presents a method for constructing intrusion detection systems based on efficient fuzzy rulebased classifiers. The design process of a fuzzy rule-based classifier from a given input-output data set can be presented as a feature selection and parameter optimization problem. For parameter optimization of fuzzy classifiers, the differential evolution is used, while the binary harmonic search algorithm is used for selection of relevant features. The performance of the designed classifiers is evaluated using the KDD Cup 1999 intrusion detection dataset. The optimal classifier is selected based on the Akaike information criterion. The optimal intrusion detection system has a 1.21% type I error and a 0.39% type II error. A comparative study with other methods was accomplished. The results obtained showed the adequacy of the proposed method.