Identification the bast algorithm and features for skype traffic classification

Skype uses strong encryption to secure communications inside the whole Skype network. Clients choose communication ports randomly. Therefore traditional port based or payload based identification of Skype traffic is not feasible. In this project we used a Machine Learning identification method to di...

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Bibliographic Details
Main Author:	Obaid Bawaked, Khaled Mohammed
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/33190/1/KhaledMohammedObaidMFKE2013.pdf
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id	my-utm-ep.33190
record_format	uketd_dc
spelling	my-utm-ep.331902017-09-14T06:36:10Z Identification the bast algorithm and features for skype traffic classification 2013-01 Obaid Bawaked, Khaled Mohammed TK Electrical engineering. Electronics Nuclear engineering Skype uses strong encryption to secure communications inside the whole Skype network. Clients choose communication ports randomly. Therefore traditional port based or payload based identification of Skype traffic is not feasible. In this project we used a Machine Learning identification method to discover Skype host and voice calls as well. In this method, we test the whole algorithms in Weka application with five groups of features to show the most effective features and algorithm for Skype classification. Results indicate the Random forest and REPtree based approach perform much better than other algorithms on the identification of Skype traffic with accuracy 96.90% and 95.40% respectively. 2013-01 Thesis http://eprints.utm.my/id/eprint/33190/ http://eprints.utm.my/id/eprint/33190/1/KhaledMohammedObaidMFKE2013.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Obaid Bawaked, Khaled Mohammed Identification the bast algorithm and features for skype traffic classification
description	Skype uses strong encryption to secure communications inside the whole Skype network. Clients choose communication ports randomly. Therefore traditional port based or payload based identification of Skype traffic is not feasible. In this project we used a Machine Learning identification method to discover Skype host and voice calls as well. In this method, we test the whole algorithms in Weka application with five groups of features to show the most effective features and algorithm for Skype classification. Results indicate the Random forest and REPtree based approach perform much better than other algorithms on the identification of Skype traffic with accuracy 96.90% and 95.40% respectively.
format	Thesis
qualification_level	Master's degree
author	Obaid Bawaked, Khaled Mohammed
author_facet	Obaid Bawaked, Khaled Mohammed
author_sort	Obaid Bawaked, Khaled Mohammed
title	Identification the bast algorithm and features for skype traffic classification
title_short	Identification the bast algorithm and features for skype traffic classification
title_full	Identification the bast algorithm and features for skype traffic classification
title_fullStr	Identification the bast algorithm and features for skype traffic classification
title_full_unstemmed	Identification the bast algorithm and features for skype traffic classification
title_sort	identification the bast algorithm and features for skype traffic classification
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/33190/1/KhaledMohammedObaidMFKE2013.pdf
_version_	1747816101123719168

Identification the bast algorithm and features for skype traffic classification

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