Bootstrap-based homogeneous ensemble feature selection for network intrusion detection system

Machine learning-based intrusion detection systems are suffering from high dimensionality of network traffics. The presence of high dimensional data is resulting in low classification accuracy. In this work, we propose a bootstrap-based homogeneous ensemble feature selection (BHmEFS) method to select a subset of relevant and non-redundant features that improves classification accuracy. In this method, three sample data have been generated from the original dataset during the bootstrapping process. From each of the three acquired sample data, the Chi-square method selects essential features subsets. The intersection method combines these three output subsets employing a homogeneous approach to obtain the ensemble features subset. The performance of BHmEFS and the Chi-square method is evaluated by using the J48 classifier. We use each of the three bootstrap samples and the original dataset to evaluate the Chi-square method. The experimental results in a multi-class NSL-KDD dataset show that the BHmEFS achieves better classification accuracy when compared with the Chi-square and other methods.