Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection

Belavagi, Manjula C and Muniyal, Balachandra (2016) Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection. Procedia Computer Science, 89 (1). pp. 117-123. ISSN 1877-0509

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Abstract

Intrusion detection system plays an important role in network security. Intrusion detection model is a predictive model used to predict the network data traffic as normal or intrusion. Machine Learning algorithms are used to build accurate models for clustering, classification and prediction. In this paper classification and predictive models for intrusion detection are built by using machine learning classification algorithms namely Logistic Regression, Gaussian Naive Bayes, Support Vector Machine and Random Forest. These algorithms are tested with NSL-KDD data set. Experimental results shows that Random Forest Classifier out performs the other methods in identifying whether the data traffic is normal or an attack

Item Type: Article
Uncontrolled Keywords: Classification Algorithms; Intrusion Detection; Machine Learning; Network Security; Supervised Learning
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 27 Mar 2017 10:59
Last Modified: 27 Mar 2017 10:59
URI: http://eprints.manipal.edu/id/eprint/148579

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