prikaz prve stranice dokumenta Procjena performansi usluga strujanja igara uživo na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja
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undergraduate thesis
Procjena performansi usluga strujanja igara uživo na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. urn:nbn:hr:168:375012
Planinić, Blaž
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Planinić, B. (2018). Procjena performansi usluga strujanja igara uživo na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:375012

Planinić, Blaž. "Procjena performansi usluga strujanja igara uživo na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:375012

Planinić, Blaž. "Procjena performansi usluga strujanja igara uživo na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:375012

Planinić, B. (2018). 'Procjena performansi usluga strujanja igara uživo na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 29 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:375012

Planinić B. Procjena performansi usluga strujanja igara uživo na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 November 29] Available at: https://urn.nsk.hr/urn:nbn:hr:168:375012

B. Planinić, "Procjena performansi usluga strujanja igara uživo na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:375012

Metadata
TitleProcjena performansi usluga strujanja igara uživo na osnovu analize kriptiranog prometa uporabom metoda strojnog učenja
Title (english)A Machine Learning Approach for Performance Estimation of Live Game Streaming Based on the Analysis of Encrypted Network Traffic
AuthorBlaž Planinić
MentorLea Skorin-Kapov (mentor)
Committee memberLea Skorin-Kapov (predsjednik povjerenstva)
Committee memberIvan Slivar (član povjerenstva)
Committee memberIrena Oršolić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Strujanje video igara uživo putem platforme Twitch.tv jedna je od najpopularnijih aktivnosti na Internetu danas. Stoga je bitno da ta aktivnost i usluga budu što bolji. Često se zna dogoditi da te usluge imaju problema, odnosno da je iskustvena kvaliteta korisnika koji gleda video zapis uživo loša. Ti podaci koji utječu na iskustvenu kvalitetu su u mrežnom prometu kripitirani, te ne postoji mogućnost njihovog pregleda u mreži, već samo na aplikacijskoj razini. Stoga, u ovom radu je u laboratorijskom okruženju ispitano do koje je mjere podatke s aplikacijske razine moguće procijeniti na temelju podataka s mrežne razine pomoću metoda strojnog učenja. Dobiveni rezultati govore da je podatke s aplikacijske razine moguće procijeniti u relativno velikoj točnosti pomoću određenih mrežnih atributa generiranjem modela strojnog učenja.
Abstract (english)
Nowadays, watching live video gaming using the Twitch.tv platform is one of the most popular activities on the Internet. For that reason, it is important that services on this platform are delivered on a high professional level. Often these services experience difficulties. In other words, the Quality of Experience (QoE) of the user watching live video can be low. Since traffic is encrypted, it is not possible to directly detect problems related to KPIs by inspecting application-level packet headers. The aim of this study was to investigate whether it is possible to estimate KPIs only from analyzing encrypted traffic using machine learning techniques. As a result of the analysis, classification accuracy was found to be about 70%.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:375012
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2019-03-14 00:01:29