Master Thesis Online
Alone in the Dark: Encrypted traffic classification
Sauvage, Mehdi 
Promotor(s) :
Donnet, Benoît
Date of defense : 26-Jun-2019/27-Jun-2019 • Permalink : http://hdl.handle.net/2268.2/6708
Details
| Title : | Alone in the Dark: Encrypted traffic classification |
| Translated title : | [fr] Seul dans le noir: Classification du traffic chiffré. |
| Author : | Sauvage, Mehdi
|
| Date of defense : | 26-Jun-2019/27-Jun-2019 |
| Advisor(s) : | Donnet, Benoît
|
| Committee's member(s) : | Leduc, Guy
Geurts, Pierre
|
| Language : | English |
| Number of pages : | 72 |
| Keywords : | [en] Machine Learning [en] encrypted traffic [en] classification |
| Discipline(s) : | Engineering, computing & technology > Computer science |
| Target public : | Student |
| Complementary URL : | https://github.com/BeyouGo/master_thesis |
| Institution(s) : | Université de Liège, Liège, Belgique |
| Degree: | Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems" |
| Faculty: | Master thesis of the Faculté des Sciences appliquées |
Abstract
[en] The goal of the Master thesis is to address the problem of encrypted traffic classification using semi-supervised machine learning approaches and time-related flow features. It takes advantage on easy-to-obtain unlabelled data to increase the accuracy of a model trained on a few labelled samples. The same approach is used to detect unobserved traffic types.
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The University of Liège does not guarantee the scientific quality of these students' works or the accuracy of all the information they contain.