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dc.contributor.authorSingh, P.
dc.contributor.authorVenkatesan, M.
dc.date.accessioned2020-03-30T10:18:13Z-
dc.date.available2020-03-30T10:18:13Z-
dc.date.issued2018
dc.identifier.citationProceedings of the 2018 International Conference on Current Trends towards Converging Technologies, ICCTCT 2018, 2018, Vol., , pp.-en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8218-
dc.description.abstractIn 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. � 2018 IEEE.en_US
dc.titleHybrid Approach for Intrusion Detection Systemen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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