Random Forest-Based NIDS: Advancing Network Threat Detection

Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signatures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data is preprocessed to enhance its quality by removing redundancies. The methodology involves rigorous testing and analysis of the random forest classifier's performance, focusing on accuracy and detection rates compared to other machine learning models. Results demonstrate that by optimizing class weights and leveraging 15 key features, the random forest classifier achieves an outstanding 99.8% accuracy across various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.