Ensemble Method for Network IDS Using the B-DRF Algorithm
A. Maria Vinitha · 2024
Intrusion detection systems (IDS) are crucial for safeguarding against the increasing cyber threats. This research study proposes an ensemble-based machine learning approach to effectively detect anomalies in network traffic. By employing bootstrap aggregation, random forest, and randomization techniques on the LUflow dataset, the proposed model achieves a high accuracy of 99% in identifying intrusions. This robust IDS can provide timely alerts to administrators, enabling proactive security measures.