A Hybridization of Evolutionary Fuzzy Systems and Ant Colony Optimization for Intrusion Detection

A hybrid approach for intrusion detection in computer networks is presentedin this paper. the proposed approach combines an evolutionary-based fuzzysystem with an ant colony optimization procedure to generate high-quality fuzzy-classication rules. we applied our hybrid learning approach to network security and validated it using the darpa kdd-cup99 benchmark dataset. the results indicate that in comparison to several traditional and new techniques, the proposed hybrid approach achieves better classicationaccuracies. the compared classication approaches are c4.5, naffve bayes,k-nn, svm, ripper, pnrule and mogf-ids. moreover the improvementon classication accuracy has been obtained for most of the classes of the intrusion detection classication problem. in addition, the results indicate that the proposed hybrid system's total classication accuracy is 94.33% and its classication cost is 0.1675. therefore, the resultant fuzzy classication rules can be used to produce a reliable intrusion detection system.