Advanced Classification Techniques for Improving Networks’ Intrusion Detection System Efficiency

Abstract This research aims to enhance the accuracy and speed of the intrusion detection process by using the feature selection method to reduce the feature space dimensions that eliminate irrelevant features. Further, we employed ensemble learning in the UNSW-NB15 dataset, by using a classifier of the Stacking method, to prevent the intrusion detection system (IDS) from becoming archaic, to adjust it with a modern attack resistance feature, and to make it less costly. We used logistic regression as a meta-classifier and combined random forests, sequential minimal optimization (SMO), and naïve Bayes methods. Our approach allowed us to achieve 97.88% accuracy in intrusion detection.