The continuous development in network attack is being a difficult issue in programming industry. Intrusion detection framework is utilized to identify and break down system attack so IDS need to be updated that can screen the framework and can trigger the alert in the framework. Numerous methods have been proposed by various authors to enhance the execution of IDS yet at the same time they can't give legitimate or complete solution.In the proposed work authorsconsidered several classification techniques and selected the most suitable classifiers namely Bayesian Network, Naive bayes, JRip, MLP, IBK, PART and J48 based on the accuracy.These selected classifiers were further ensemble and experiments were performed on the combination of ensemble of classifiers. The combination giving best accuracy will be used in IDS for detection of various attacks. In additiontwo pre-processing techniques were used for the performance analysis. The outcome of these experiment shows improvement in the detection rate of U2R and R2L attack
