A hybridization of evolutionary fuzzy systems and ant Colony optimization for intrusion detection

Abstract

A hybrid approach for intrusion detection in computer networks is presented in this paper. The proposed approach combines an evolutionary-based fuzzy system with an Ant Colony Optimization procedure to generate high-quality fuzzy-classification rules. We applied our hybrid learning approach to network security and validated it using the DARPA KDD-Cup99 benchmark data set. The results indicate that in comparison to several traditional and new techniques, the proposed hybrid approach achieves better classification accuracies. The compared classification approaches are C4.5, Naïve Bayes, <em>k</em>-NN, SVM, Ripper, PNrule and MOGF-IDS. Moreover the improvement on classification accuracy has been obtained for most of the classes of the intrusion detection classification problem. In addition, the results indicate that the proposed hybrid system's total classification accuracy is 94.33% and its classification cost is 0.1675. Therefore, the resultant fuzzy classification rules can be used to produce a reliable intrusion detection system.