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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2013
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| 在線閱讀: | http://eprints.utm.my/id/eprint/33190/1/KhaledMohammedObaidMFKE2013.pdf |
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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 |