Intrusion Detection using Ensemble Machine Learning

Abstract

Now a days intrusion detection systems are essential for defending computer networking toward hostile activity. With the increasing complexity and diversity of modern cyber threats, traditional single-classifier-based IDS approaches often struggle to achieve optimal detection performance. To address this challenge, this study proposes an Intrusion Detection System using Ensemble Machine Learning. The methodology combines the strengths of multiple machine learning algorithms in an ensemble framework to enhance the accuracy, robustness, and efficiency of intrusion detection. The system incorporates steps such as data preprocessing, feature selection, ensemble model construction, and model performance. Techniques like data balancing, attribute encoding, and feature selection based on correlation are applied to optimize the IDS performance. The ensemble model benefits from the collective intelligence and diverse decision-making of multiple classifiers, improving the system's ability to accurately identify and respond to network intrusions. Through comprehensive result analysis, the study validates the effectiveness of the proposed IDS in terms of evaluation metrics, feature importance, robustness, and real- world impact. The proposed IDS using Ensemble Machine Learning offers a promising approach to tackle the dynamic and evolving nature of cyber threats, enhancing the security and resilience of computer networks. Keywords - Intrusion Detection System, Ensemble Machine Learning, Data Balancing, Feature Selection, Cyber Security.