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Video Flow Classification: Feature Based Classification Using the Tree-based Approach
Karlstad University.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This dissertation describes a study which aims to classify video flows from Internet network traffic. In this study, classification is done based on the characteristics of the flow, which includes features such as payload sizes and inter-arrival time. The purpose of this is to give an alternative to classifying flows based on the contents of their payload packets. Because of an increase of encrypted flows within Internet network traffic, this is a necessity. Data with known class is fed to a machine learning classifier such that a model can be created. This model can then be used for classification of new unknown data. For this study, two different classifiers are used, namely decision trees and random forest. Several tests are completed to attain the best possible models. The results of this dissertation shows that classification based on characteristics is possible and the random forest classifier in particular achieves good accuracies. However, the accuracy of classification of encrypted flows was not able to be tested within this project.

Place, publisher, year, edition, pages
2016. , 79 p.
Keyword [en]
Machine learning, Flow classification, video
National Category
Computer and Information Science
URN: urn:nbn:se:kau:diva-43012OAI: diva2:937491
Subject / course
Computer Science
Educational program
Engineering: Computer Engineering (300 ECTS credits)
Available from: 2016-06-20 Created: 2016-06-15 Last updated: 2016-06-20Bibliographically approved

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