Synergizing Ensemble Algorithms for Optimal Intrusion Detection in Asymmetric Data: A Comprehensive Analysis with the NSL-KDD Framework

Abstract

Ensemble learning has emerged as a powerful method for enhancing the precision of intrusion detection systems (IDSs). In our study, we introduce two novel ensemble learning approaches for IDS: one based on a voting mechanism and the other on stacking techniques. These models were rigorously tested using the NSL-KDD dataset, demonstrating substantial accuracy improvements compared to traditional single classifier systems. Single classifiers often face challenges such as sensitivity to anomalies and noise, along with difficulties in adapting to new, unseen data. Ensemble learning effectively mitigates these issues by integrating the outputs of several classifiers, leading to more stable and accurate predictions. Our research findings reveal that our ensemble learning models can achieve up to 99% accuracy on the NSL-KDD dataset, a notable increase from the approximately 90% accuracy rates observed with single classifiers. Moreover, our models have demonstrated an impressively low false alarm rate (FAR) of under 1%. This indicates their exceptional capability in intrusion detection with minimal false positives. The outcomes of our study strongly indicate the potential of ensemble learning in refining the accuracy of IDSs. We are optimistic that our models will significantly bolster network security, and we are committed to furthering research in this promising field.