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On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined Networks

On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined... Abstract We propose a concept of using Software Defined Network (SDN) technology and machine learning algorithms for monitoring and detection of malicious activities in the SDN data plane. The statistics and features of network traffic are generated by the native mechanisms of SDN technology. In order to conduct tests and a verification of the concept, it was necessary to obtain a set of network workload test data. We present virtual environment which enables generation of the SDN network traffic. The article examines the efficiency of selected machine learning methods: Self Organizing Maps and Learning Vector Quantization and their enhanced versions. The results are compared with other SDN-based IDS. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Electronics and Telecommunications de Gruyter

On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined Networks

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References (21)

Publisher
de Gruyter
Copyright
Copyright © 2016 by the
ISSN
2300-1933
eISSN
2300-1933
DOI
10.1515/eletel-2016-0033
Publisher site
See Article on Publisher Site

Abstract

Abstract We propose a concept of using Software Defined Network (SDN) technology and machine learning algorithms for monitoring and detection of malicious activities in the SDN data plane. The statistics and features of network traffic are generated by the native mechanisms of SDN technology. In order to conduct tests and a verification of the concept, it was necessary to obtain a set of network workload test data. We present virtual environment which enables generation of the SDN network traffic. The article examines the efficiency of selected machine learning methods: Self Organizing Maps and Learning Vector Quantization and their enhanced versions. The results are compared with other SDN-based IDS.

Journal

International Journal of Electronics and Telecommunicationsde Gruyter

Published: Sep 1, 2016

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