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Video Traffic Classification: A Machine Learning approach with Packet Based Features using Support Vector Machine
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Videotrafikklassificering : En Maskininlärningslösning med Paketbasereade Features och Supportvektormaskin (Swedish)
Abstract [en]

Internet traffic classification is an important field which several stakeholders are dependent on for a number of different reasons. Internet Service Providers (ISPs) and network operators benefit from knowing what type of traffic that propagates over their network in order to correctly treat different applications. Today Deep Packet Inspection (DPI) and port based classification are two of the more commonly used methods in order to classify Internet traffic. However, both of these techniques fail when the traffic is encrypted. This study explores a third method, classifying Internet traffic by machine learning in which the classification is realized by looking at Internet traffic flow characteristics instead of actual payloads. Machine learning can solve the inherent limitations that DPI and port based classification suffers from. In this study the Internet traffic is divided into two classes of interest: Video and Other. There exist several machine learning methods for classification, and this study focuses on Support Vector Machine (SVM) to classify traffic. Several traffic characteristics are extracted, such as individual payload sizes and the longest consecutive run of payload packets in the downward direction. Several experiments using different approaches are conducted and the achieved results show that overall accuracies above 90% are achievable.

Place, publisher, year, edition, pages
2016. , 88 p.
Keyword [en]
Supervised Machine Learning, SVM, Video traffic classification
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-43011OAI: oai:DiVA.org:kau-43011DiVA: diva2:937492
External cooperation
Procera Networks
Subject / course
Computer Science
Educational program
Engineering: Computer Engineering (300 ECTS credits)
Supervisors
Examiners
Available from: 2016-06-20 Created: 2016-06-15 Last updated: 2016-06-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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