An investigation of classification algorithms for intrusion detection system - A quantitative approach

Josy Elsa Varghese; Balachandra Muniyal

doi:10.1109/ICACCI.2017.8126146

An investigation of classification algorithms for intrusion detection system - A quantitative approach

Josy Elsa Varghese, Balachandra Muniyal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

Nowadays security issues are growing in a tremendous rate. So it is expedient to have a mechanism to keep track of its security issues in the network or host. The Intrusion Detection System (IDS) has a critical part for supervising the networks. The false alarm rate and accuracy are the two important factors to be considered in the design of competent IDS. The role of classification algorithms is indispensable in the decision making of IDS. The redundant and irrelevant features of dataset also affects the performance of classifiers which in turn degrading the evaluation of anomaly detection. The proposed work focuses on the detailed study of different classifiers on two feature selection techniques using NSL-KDD dataset, where Random Forest on Principal Component Analysis (PCA) gives the accuracy rate of 99.52% and false alarm rate is 0.48%.

Original language	English
Title of host publication	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2045-2051
Number of pages	7
Volume	2017-January
ISBN (Electronic)	9781509063673
DOIs	https://doi.org/10.1109/ICACCI.2017.8126146
Publication status	Published - 30-11-2017
Event	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India Duration: 13-09-2017 → 16-09-2017

Conference

Conference	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Country/Territory	India
City	Manipal, Mangalore
Period	13-09-17 → 16-09-17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems

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10.1109/ICACCI.2017.8126146

Cite this

Varghese, J. E., & Muniyal, B. (2017). An investigation of classification algorithms for intrusion detection system - A quantitative approach. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 (Vol. 2017-January, pp. 2045-2051). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2017.8126146

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Varghese, JE & Muniyal, B 2017, An investigation of classification algorithms for intrusion detection system - A quantitative approach. in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 2045-2051, 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 13-09-17. https://doi.org/10.1109/ICACCI.2017.8126146

An investigation of classification algorithms for intrusion detection system - A quantitative approach. / Varghese, Josy Elsa; Muniyal, Balachandra.
2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 2045-2051.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Nowadays security issues are growing in a tremendous rate. So it is expedient to have a mechanism to keep track of its security issues in the network or host. The Intrusion Detection System (IDS) has a critical part for supervising the networks. The false alarm rate and accuracy are the two important factors to be considered in the design of competent IDS. The role of classification algorithms is indispensable in the decision making of IDS. The redundant and irrelevant features of dataset also affects the performance of classifiers which in turn degrading the evaluation of anomaly detection. The proposed work focuses on the detailed study of different classifiers on two feature selection techniques using NSL-KDD dataset, where Random Forest on Principal Component Analysis (PCA) gives the accuracy rate of 99.52% and false alarm rate is 0.48%.

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An investigation of classification algorithms for intrusion detection system - A quantitative approach

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