Enhancing Network Intrusion Detection Systems Using Hybrid Machine Learning Models

Abstract

The increasing volume and sophistication of cyber threats in 2017 have rendered traditional security mechanisms inadequate in many modern digital environments. As attackers evolve their tactics, organizations must respond with more intelligent, adaptive security systems. This study presents a hybrid intrusion detection model that combines both signature based and anomaly based techniques through machine learning algorithms specifically Random Forest and k Nearest Neighbors (k NN). Leveraging the NSL KDD dataset, the study emphasizes preprocessing strategies such as normalization, one hot encoding, and information gain based feature selection to refine the input data for modeling. Through a comparative evaluation, the hybrid model demonstrates improved accuracy (up to 95.4%), reduced false positive rates, and superior generalization across varied attack categories such as Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R). The findings underscore the utility of ensemble learning in enhancing IDS performance. Practical implications include recommendations for integrating the hybrid IDS into real time monitoring tools, and suggestions for future work in adaptive threat intelligence systems. By addressing both known and novel threats more effectively, this hybrid approach offers a resilient solution for evolving cybersecurity landscapes.