Hybrid Approach for Intrusion Detection System

Abstract

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In the recent research, Intrusion Detection sys- tem in Machine Learning has been giving good detection and high accuracy on novel attacks. The major purpose of this study is implementing a method that combines Random-Forest classification technique and K-Means clustering Algorithms. In misuse-detection, random-forest algorithm will build a patterns of intrusion over a training data. And in anomaly-detection, intrusions will be identified by the outlier-detection mechanism in the random-forest algorithm. This hybrid-detection system will combine the advantage of anomaly and mis-use detection and improves the performance of detection. This paper mainly focused on evaluating the performance of hybrid approaches namely Gaussian Mixture clustering with Random Forest Classifiers and K-Means clustering with Random Forest Classifiers in-order to detect intrusion. These algorithms were evaluated for the four categories of attacks based on accuracy, false-alarm-rate, and detection-rate. From our experiments conducted, K-Means clustering with Random Forest Classifiers outperformed over the Gaussian Mixture clustering with Random Forest Classifiers.