Advanced Threat Detection Using Soft and Hard Voting Techniques in Ensemble Learning

Abstract

This study addresses the challenge of detecting network intrusions by exploring the efficacy of ensemble learning methods over traditional machine learning models. The problem of network security is exacerbated by sophisticated cyber-attack techniques that standard single model approaches often fail to counter effectively. Our solution employs a robust ensemble methodology to improve detection rates. The research contribution lies in the comparative analysis of individual machine learning models—K-Nearest Neighbors (KNN), Decision Trees (DT), and Gradient Boosting (GB)—against ensemble methods employing soft and hard voting classifiers. This study is one of the first to quantify the performance gains of ensemble methods in the context of network intrusion detection. Our methodological approach involves applying these models to the WSNBFSF dataset, which consists of traffic types including normal operations and various attacks. Performance metrics such as accuracy, precision, recall, and F1-score are calculated to assess the effectiveness of each model. The ensemble methods combine the strengths of individual models using voting systems, which are tested against the same metrics. Results indicate that while individual models like DT and GB achieved near-perfect accuracy scores (99.95% and 99.9%, respectively), the ensemble models performed even better. The soft voting classifier achieved an accuracy of 99.967%, and the hard voting classifier reached 100%, demonstrating their superior capability in network traffic classification and intrusion detection. In conclusion, the integration of ensemble methods significantly enhances the detection accuracy and reliability of network intrusion systems. Future research should explore additional ensemble techniques and consider scalability and class imbalance issues to further refine the efficacy of intrusion detection systems.