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Analysis of features selection for p2p traffic detection using support vector machine

Network traffic classification plays a vital role in various network activities. Network traffic data include a large number of relevant and redundant features, which increase the flow classifier computational complexity and affect the classification results. This paper focuses on the analysis of di...

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Main Authors:	Jamil, Haitham A., Zarei, Roozbeh, Fadlelssied, Nadir O., Aliyu, M., Nor, Sulaiman M., Marsono, Muhammad N.
Format:	Conference or Workshop Item
Published:	2013
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://eprints.utm.my/id/eprint/50895/ http://apps.webofknowledge.com.ezproxy.utm.my/full_record.do?product=WOS&search_mode=GeneralSearch&qid=13&SID=N2huBv5qmbZvAuY37Y4&page=1&doc=1
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id	my.utm.50895
record_format	eprints
spelling	my.utm.508952017-07-24T07:36:00Z http://eprints.utm.my/id/eprint/50895/ Analysis of features selection for p2p traffic detection using support vector machine Jamil, Haitham A. Zarei, Roozbeh Fadlelssied, Nadir O. Aliyu, M. Nor, Sulaiman M. Marsono, Muhammad N. TJ Mechanical engineering and machinery Network traffic classification plays a vital role in various network activities. Network traffic data include a large number of relevant and redundant features, which increase the flow classifier computational complexity and affect the classification results. This paper focuses on the analysis of different type of features selection algorithms in order to propose a set of flow features that are robust and stable to classify Peer-to-Peer (P2P) traffic. The process of validation and evaluation were done through experimentation on the traffic traces from special shared resources. The classification of P2P traffic is using Support Vector Machine (SVM) measurable in terms of its accuracy and speed. The experimental results indicate that P2P SVM classifier with reduced feature sets not only results in a higher computing performance (0.14 second for testing time), but also achieves high accuracy (92.6%). 2013 Conference or Workshop Item PeerReviewed Jamil, Haitham A. and Zarei, Roozbeh and Fadlelssied, Nadir O. and Aliyu, M. and Nor, Sulaiman M. and Marsono, Muhammad N. (2013) Analysis of features selection for p2p traffic detection using support vector machine. In: International Conference of Information and Communication Technology (ICoICT), MAR 20-22, 2013, Bandung, Indonesia. http://apps.webofknowledge.com.ezproxy.utm.my/full_record.do?product=WOS&search_mode=GeneralSearch&qid=13&SID=N2huBv5qmbZvAuY37Y4&page=1&doc=1
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Jamil, Haitham A. Zarei, Roozbeh Fadlelssied, Nadir O. Aliyu, M. Nor, Sulaiman M. Marsono, Muhammad N. Analysis of features selection for p2p traffic detection using support vector machine
description	Network traffic classification plays a vital role in various network activities. Network traffic data include a large number of relevant and redundant features, which increase the flow classifier computational complexity and affect the classification results. This paper focuses on the analysis of different type of features selection algorithms in order to propose a set of flow features that are robust and stable to classify Peer-to-Peer (P2P) traffic. The process of validation and evaluation were done through experimentation on the traffic traces from special shared resources. The classification of P2P traffic is using Support Vector Machine (SVM) measurable in terms of its accuracy and speed. The experimental results indicate that P2P SVM classifier with reduced feature sets not only results in a higher computing performance (0.14 second for testing time), but also achieves high accuracy (92.6%).
format	Conference or Workshop Item
author	Jamil, Haitham A. Zarei, Roozbeh Fadlelssied, Nadir O. Aliyu, M. Nor, Sulaiman M. Marsono, Muhammad N.
author_facet	Jamil, Haitham A. Zarei, Roozbeh Fadlelssied, Nadir O. Aliyu, M. Nor, Sulaiman M. Marsono, Muhammad N.
author_sort	Jamil, Haitham A.
title	Analysis of features selection for p2p traffic detection using support vector machine
title_short	Analysis of features selection for p2p traffic detection using support vector machine
title_full	Analysis of features selection for p2p traffic detection using support vector machine
title_fullStr	Analysis of features selection for p2p traffic detection using support vector machine
title_full_unstemmed	Analysis of features selection for p2p traffic detection using support vector machine
title_sort	analysis of features selection for p2p traffic detection using support vector machine
publishDate	2013
url	http://eprints.utm.my/id/eprint/50895/ http://apps.webofknowledge.com.ezproxy.utm.my/full_record.do?product=WOS&search_mode=GeneralSearch&qid=13&SID=N2huBv5qmbZvAuY37Y4&page=1&doc=1
_version_	1643652878058913792
score	13.211869

Analysis of features selection for p2p traffic detection using support vector machine

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