Ensemble of classifiers for detecting network intrusion

Intrusion detection technology is an effective approach to deal with problems of malicious attacks on computer networks. In this paper, we present an intrusion detection model based on Ensemble of classifiers such as AdaBoost, MultiBoosting and Bagging to gain more opportunity of training misclassified samples and reduce the error rate by the majority voting of involved classifiers. Our main goal is to build an efficient intrusion detection model based on the error rate of classifiers if unfair distribution exists either within or between data sets. We employ data from the third international knowledge discovery and data mining tools competition (KDDCup'99) to train and test feasibility of our proposed model. From our experimental results with KDDCUP'99 benchmarking dataset, the proposed ensemble of classifiers with REP tree as base classifier outperforms others in building a network intrusion detection model with high detection rate, low overall error rate with low false positive rate.