ENHANCING NETWORK INTRUSION DETECTION WITH MOUNTAIN GAZELLE OPTIMIZER-BASED FEATURE SELECTION

Abstract

Network intrusion detection systems (NIDS) play a critical role in safeguarding modern networks. Feature selection techniques are essential for optimizing NIDS performance by reducing dimensionality and enhancing classifier efficiency. This study proposes the Mountain Gazelle Optimizer (MGO), a novel nature-inspired metaheuristic, for effective feature selection within the context of network intrusion detection. MGO's performance is evaluated on the benchmark UNSW-NB15 dataset in conjunction with a diverse suite of classifiers: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest Classifiers (RFC), Decision Tree Classifiers (DTC), and Gaussian Naive Bayes (GNB). Our analysis investigates the impact of MGO-based feature selection on key NIDS performance metrics including accuracy, specificity, sensitivity, and computational runtime. Results demonstrate the potential of MGO to identify optimal feature subsets, leading to significant improvements in network intrusion detection performance across these varied algorithmic approaches. Additionally, we explore the comparative advantages of MGO-based feature selection for specific types of network attacks represented within the UNSW-NB15 dataset. Keywords: Intrusion Detection System (IDS), Mountain Gazelle Optimizer, Feature Selection, SVM, KNN, Naive Bayes, Classification, Optimization, Machine Learning.