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Clustering-based separation of media transfers in DPI-classified cellular video and VoIP traffic
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0003-3461-7079
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0001-7311-9334
2018 (English)In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Identifying VoIP and video traffic is often useful in the context of managing a cellular network, and to perform such traffic classification deep packet inspection (DPI) approaches are often used. Commercial DPI classifiers do not necessarily differentiate between, for example, YouTube traffic that arises from browsing inside the YouTube app, and traffic arising from the actual viewing of a YouTube video. Here we apply unsupervised clustering methods on such cellular DPI-labeled VoIP and video traffic to identify the characteristic behavior of the two sub-groups of media-transfer and non media-transfer flows. The analysis is based on a measurement campaign performed inside the core network of a commercial cellular operator, collecting data for more than two billion packets in 40+ million flows. A specially instrumented commercial DPI appliance allows the simultaneous collection of per packet information in addition to the DPI classification output. We show that the majority of flows falls into clusters that are easily identifiable as belonging to one of the traffic sub-groups, and that a surprising majority of DPIlabeled VoIP and video traffic is non-media related.

Place, publisher, year, edition, pages
IEEE, 2018.
Series
IEEE Wireless Communications and Networking Conference. Proceedings, ISSN 1525-3511, E-ISSN 1558-2612
Keywords [en]
Media, YouTube, Clustering algorithms, Cryptography, Downlink, Engines, Uplink
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-67798DOI: 10.1109/WCNC.2018.8377027ISI: 000435542400081ISBN: 978-1-5386-1734-2 (electronic)ISBN: 978-1-5386-1735-9 (print)OAI: oai:DiVA.org:kau-67798DiVA, id: diva2:1220569
Conference
2018 IEEE Wireless Communications and Networking Conference (WCNC), 15-18 April 2018, Barcelona, Spain.
Projects
HITSAvailable from: 2018-06-19 Created: 2018-06-19 Last updated: 2019-01-11Bibliographically approved

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Garcia, JohanBrunström, Anna

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CiteExportLink to record
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Citation style
  • apa
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More styles
Language
  • de-DE
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  • en-US
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Output format
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