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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محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Obaid Bawaked, Khaled Mohammed
التنسيق: أطروحة
اللغة:English
منشور في: 2013
الموضوعات:
الوصول للمادة أونلاين: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
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