ViCrypt to the rescue: real-time, machine-learning-driven video-QoE monitoring for encrypted streaming traffic

Download full text files

  • 107004.pdfeng
    (1709KB)

    Postprint. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Export metadata

Statistics

Number of document requests

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Sarah Wassermann, Michael SeufertORCiDGND, Pedro Casas, Li Gang, Kuang Li
URN:urn:nbn:de:bvb:384-opus4-1070044
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/107004
ISSN:1932-4537OPAC
ISSN:2373-7379OPAC
Parent Title (English):IEEE Transactions on Network and Service Management
Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Place of publication:New York, NY
Type:Article
Language:English
Year of first Publication:2020
Publishing Institution:Universität Augsburg
Release Date:2023/09/13
Tag:Electrical and Electronic Engineering; Computer Networks and Communications
Volume:17
Issue:4
First Page:2007
Last Page:2023
DOI:https://doi.org/10.1109/tnsm.2020.3036497
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für vernetzte eingebettete Systeme und Kommunikationssysteme
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht