Clustering network traffic utilization

Classification of network traffic using distinctive characteristic application is not ideal for P2P and HTTP protocols. This is for the case when a user intercepts the application from other proxy or dynamic port, then the bytes utilization can be manipulated. In this paper, we present a clustering...

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Main Authors: Mohd Khairudin, Nazli, Muda, Zaiton, Mustapha, Aida, Nagarathinam, Yogeswaran, Salleh, Mohd. Sidek
Format: Article
Language:English
Published: Praise Worthy Prize 2013
Online Access:http://psasir.upm.edu.my/id/eprint/30577/
http://psasir.upm.edu.my/id/eprint/30577/1/Clustering%20network%20traffic%20utilization.pdf
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author Mohd Khairudin, Nazli
Muda, Zaiton
Mustapha, Aida
Nagarathinam, Yogeswaran
Salleh, Mohd. Sidek
author_facet Mohd Khairudin, Nazli
Muda, Zaiton
Mustapha, Aida
Nagarathinam, Yogeswaran
Salleh, Mohd. Sidek
author_sort Mohd Khairudin, Nazli
building UPM Institutional Repository
collection Online Access
description Classification of network traffic using distinctive characteristic application is not ideal for P2P and HTTP protocols. This is for the case when a user intercepts the application from other proxy or dynamic port, then the bytes utilization can be manipulated. In this paper, we present a clustering approach for network traffic classification using information from one particular port. The clustering experiments were conducted using three different clustering algorithms, which are K-Means, DBScan and AutoClass. The analysis discussed on the quality of resulting clusters from all the algorithms.
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spelling upm-305772015-10-07T07:59:49Z http://psasir.upm.edu.my/id/eprint/30577/ Clustering network traffic utilization Mohd Khairudin, Nazli Muda, Zaiton Mustapha, Aida Nagarathinam, Yogeswaran Salleh, Mohd. Sidek Classification of network traffic using distinctive characteristic application is not ideal for P2P and HTTP protocols. This is for the case when a user intercepts the application from other proxy or dynamic port, then the bytes utilization can be manipulated. In this paper, we present a clustering approach for network traffic classification using information from one particular port. The clustering experiments were conducted using three different clustering algorithms, which are K-Means, DBScan and AutoClass. The analysis discussed on the quality of resulting clusters from all the algorithms. Praise Worthy Prize 2013-05 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30577/1/Clustering%20network%20traffic%20utilization.pdf Mohd Khairudin, Nazli and Muda, Zaiton and Mustapha, Aida and Nagarathinam, Yogeswaran and Salleh, Mohd. Sidek (2013) Clustering network traffic utilization. International Review on Computers and Software, 8 (5). pp. 1076-1081. ISSN 1828-6003; ESSN: 1828-6011
spellingShingle Mohd Khairudin, Nazli
Muda, Zaiton
Mustapha, Aida
Nagarathinam, Yogeswaran
Salleh, Mohd. Sidek
Clustering network traffic utilization
title Clustering network traffic utilization
title_full Clustering network traffic utilization
title_fullStr Clustering network traffic utilization
title_full_unstemmed Clustering network traffic utilization
title_short Clustering network traffic utilization
title_sort clustering network traffic utilization
url http://psasir.upm.edu.my/id/eprint/30577/
http://psasir.upm.edu.my/id/eprint/30577/1/Clustering%20network%20traffic%20utilization.pdf