Network Intrusion Detection in Computer Networks Using an Efficacious Combined Feature Selection Technique Based on the Intersection Method of Mutual Information, Anova F-test and Genetic Algorithm

The Intrusion detection system (IDS) manages a massive volume of data that comprises irrelevant and redundant features, leading to more significant resource consumption, long-time training and testing procedures, and lower detection rate. Hence, Feature selection is a crucial phase in intrusion detection. The aim of this paper is to introduce an intersection-based strategy that optimally selects the features for classification. This feature selection involves an intersection of simultaneous mutual information based on the transductive model (MIT-MIT), Anova F-value, and Genetic Algorithm (GA) methods. A benchmark dataset, named NSL-KDD, is applied to evaluate the effectiveness of the proposed approach. This study includes accuracy, precision, recall, and F1 score as evaluation metrics for IDS, which analyzes the proposed method with state-of-the-art classifiers. The evaluation results confirm that our feature selection algorithm provides more essential features for IDS to achieve high accuracy, overwhelming the other comparative algorithms.