%0 Generic %1 info3-presentation-2020-3 %A Wehner, Nikolas %C 2nd KuVS Workshop on Machine Learning for Networking, Würzburg, Germany %D 2020 %K myown %T Inferring Web QoE with Machine Learning from Encrypted Network Traffic %X In this work, the problem of Quality of Experience (QoE) monitoring of web browsing is addressed. In particular, the inference of common Web QoE metrics like RUMSI with machine learning is investigated. Based on a large dataset collected with WebPageTest on three different devices, a unique feature set is used to approximate Web QoE metrics with regression and classification approaches. This work highlights work in progress.

@presentation{info3-presentation-2020-3, abstract = {In this work, the problem of Quality of Experience (QoE) monitoring of web browsing is addressed. In particular, the inference of common Web QoE metrics like RUMSI with machine learning is investigated. Based on a large dataset collected with WebPageTest on three different devices, a unique feature set is used to approximate Web QoE metrics with regression and classification approaches. This work highlights work in progress. }, added-at = {2020-10-20T12:02:48.000+0200}, address = {2nd KuVS Workshop on Machine Learning for Networking, Würzburg, Germany}, author = {Wehner, Nikolas}, biburl = {https://www.bibsonomy.org/bibtex/212135885a35543245bd871d063c55854/uniwue_info3}, day = 8, interhash = {97c9c9574774a6de0e4beb6a57c63cf1}, intrahash = {12135885a35543245bd871d063c55854}, keywords = {myown}, month = {10}, timestamp = {2022-03-14T00:08:45.000+0100}, title = {Inferring Web QoE with Machine Learning from Encrypted Network Traffic}, year = 2020 }