Majority Voting-Based Heterogeneous Ensemble Method for Intrusion Detection System

Given cyber threats are always changing in today's digital world, it is pertinent that the intrusion detection system (IDS) maintains a high level of success while remaining dynamic to handle new threats. IDS is not only about these issues, in fact, we propose a heterogeneous ensemble approach based on majority voting to improve the accuracy and reliability of IDS. This model is set up in ensemble context to fuse multiple machine learning techniques in one system for high confidence detections trust score improvements. This framework consolidates all model types predictions. To evaluate our method, we employed the NSL-KDD dataset that includes normal network traffic as well as different types of attacks. Simulation tests demonstrate that our strategy is better resilient to noise and data volatile with higher pick at low-frequency assaults. The system assessment team of the accuracy, precision, recall, and F1-score was 98.09%. Results are compared to those achieved by Random Forest and Support Vector Machines, which have previously reached accuracies up to 93%, demonstrating that the proposed ensemble approach performs well. The development of majority voting technique is the basis on which future advancements can build to make ensemble models more robust to modern day cyber-attacks, and also underscores the importance of employing majority voting as a security component in majority security systems.