Supervised Machine Learning based Classification of Video Traffic Types

2020 24th International Conference "Electronics": Proceedings 2020
Elans Grabs, Ernests Pētersons, Aleksandrs Ipatovs, Dmitrijs Čulkovs

The main topic of the article is accuracy evaluation of supervised machine learning algorithms performance applied to real network traffic data. The main task to be solved by supervised learning is classification of video traffic type - streaming (real-time) video or on-demand video (a record). The experiment has been performed for the same video fragment with data filtering and without it. The results have been summarized in form of tables with accuracy assessment for multiple commonly used supervised machine learning algorithms.

Keywords
Supervised Learning, Internet, Multimedia, Network Traffic
DOI
10.1109/IEEECONF49502.2020.9141625
Hyperlink
https://ieeexplore.ieee.org/document/9141625

Grabs, E., Pētersons, E., Ipatovs, A., Čulkovs, D. Supervised Machine Learning based Classification of Video Traffic Types. In: 2020 24th International Conference "Electronics": Proceedings, Lithuania, Palanga, 15-17 June, 2020. Piscataway: IEEE, 2020, pp.83-86. ISBN 978-1-7281-5869-3. e-ISBN 978-1-7281-5868-6. Available from: doi:10.1109/IEEECONF49502.2020.9141625

Publication language
English (en)