Wrapper Based Approach for Network Intrusion Detection Model with Combination of Dual Filtering Technique of Resample and SMOTE

Abstract

Network intrusion problem poses serious threat to all users of computer systems due to the increase in size of modern networks and the convolution of large network traffic data. The problem exceeds the weighty limits of conventional technique of intrusion prevention system. Therefore, new solutions for efficient and effective intrusion detection with low false alarm rate are required. This research proposes a new methodology for intrusion detection by combining wrapper feature selection approach based on a genetic algorithm with Synthetic Minority Over Sampling (SMOTE) and Resample techniques for the balancing of the class distribution. The two selected traffic datasets (KDDCUP99 and NSL-KDD) were subjected to hybrid preprocessing of filtering technique, where SMOTE and Resample were used to recognize the oversampling of the minority samples in a bid to constructively increase the prediction accuracy of the minority class under the assumption that the overall distribution is unchanged and the information loss of majority samples. Three different decision tree classifiers were used to compute the performance of the selected subset features. Remarkable and outstanding fair comparison with other state-of-the-art detection methods was achieved with performance accuracy of 99.9873% and 99.8457% on KDDCUP99 and NSL-KDD dataset respectively.