ASENIDSB: A Stacked Ensemble-based Network Intrusion Detection System Optimized with Boruta Feature Selection

Abstract

In the cyber world, network security is increasingly challenged by sophisticated attacks and vulnerabilities that exploit data and systems. Effective security solutions are crucial for real-time protection, yet existing methods often struggle with imbalanced datasets, redundant features, and limited classification accuracy. Network Intrusion Detection Systems (NIDS) play a vital role in combating these threats, but traditional ML approaches frequently fall short in detecting complex and subtle attack patterns. This research addresses these critical issues by introducing a Stacked Ensemble-Based Network Intrusion Detection System using Boruta (ASENIDSB). This model tackles the problem of class imbalance, a significant challenge in IDS, by employing the K-means SMOTE oversampling technique to improve the representation of minority classes. Additionally, the Boruta feature selection method improves computational efficiency by addressing redundancy and inefficiency brought on by irrelevant features. ASENIDSB also improves classification robustness and detection capabilities using a stacked ensemble learning technique. It integrates Extra Trees and Extreme Gradient Boosting as base classifiers, with Logistic Regression serving as the meta-model to deliver precise and reliable predictions. This combination enables the model to address the limitations of single and traditional ensemble classifiers, offering a robust, efficient, and accurate solution for network intrusion detection. The accuracy and F1-score of the proposed ASENIDSB model are evaluated using a ten-fold cross-validation method. Experimental Results have proved that the proposed model significantly surpasses some other ensemble models, standard ML classifiers, and SOTA models. The proposed ASENIDSB model achieved an impressive accuracy of 97.85% and an F1-score of 97.82%.