Hybrid Learning Approach For Intrusion Detection In Network Security Using Ensemble Methods

Abstract

The increasing frequency and sophistication of cyberattacks have led to a pressing need for advanced network security systems, particularly Intrusion Detection Systems (IDS). While traditional IDS models provide a baseline of protection, they often fall short in detecting novel and complex threats. This research proposes a hybrid learning approach for IDS, leveraging the strengths of ensemble machine learning methods such as Random Forest, Gradient Boosting, and Voting Classifier. The proposed system aims to enhance detection accuracy and reduce false positives by combining multiple classifiers into a cohesive model. Using the NSL-KDD dataset, the model was trained and tested, showing superior performance compared to individual learning algorithms. This paper discusses the design, implementation, and performance evaluation of the hybrid IDS model.