Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection

Manjula C. Belavagi; Balachandra Muniyal

doi:10.1016/j.procs.2016.06.016

Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection

Manjula C. Belavagi^*
, Balachandra Muniyal

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

256 Citations (Scopus)

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.

Original language	English
Pages (from-to)	117-123
Number of pages	7
Journal	Procedia Computer Science
Volume	89
DOIs	https://doi.org/10.1016/j.procs.2016.06.016
Publication status	Published - 2016
Event	12th International Conference on Communication Networks, ICCN 2016, 12th International Conference on Data Mining and Warehousing, ICDMW 2016 and 12th International Conference on Image and Signal Processing, ICISP 2016 - Bangalore, India Duration: 19-08-2016 → 21-08-2016

All Science Journal Classification (ASJC) codes

General Computer Science

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10.1016/j.procs.2016.06.016

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title = "Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection",

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.",

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note = "Publisher Copyright: {\textcopyright} 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.; 12th International Conference on Communication Networks, ICCN 2016, 12th International Conference on Data Mining and Warehousing, ICDMW 2016 and 12th International Conference on Image and Signal Processing, ICISP 2016 ; Conference date: 19-08-2016 Through 21-08-2016",

year = "2016",

doi = "10.1016/j.procs.2016.06.016",

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AU - Belavagi, Manjula C.

AU - Muniyal, Balachandra

PY - 2016

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AB - 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.

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DO - 10.1016/j.procs.2016.06.016

M3 - Article

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JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 12th International Conference on Communication Networks, ICCN 2016, 12th International Conference on Data Mining and Warehousing, ICDMW 2016 and 12th International Conference on Image and Signal Processing, ICISP 2016

Y2 - 19 August 2016 through 21 August 2016

ER -

Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection

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