Procjena performansi usluge video poziva ostvarenog putem tehnologije WebRTC na osnovu analize kriptiranog prometa uporabom tehnike strojnog učenja

undergraduate thesis

• Title (english): A Machine Learning Approach to Estimating the Performance of a WebRTC-Based Video Call Based on the Analysis of Encrypted Network Traffic
• Author: Tina Knežević
• Mentor: Lea Skorin-Kapov (mentor)
• Committee member: Lea Skorin-Kapov (predsjednik povjerenstva)
• Committee member: Mirko Sužnjević (član povjerenstva)
• Committee member: Irena Oršolić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2018-07-13, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

WebRTC je projekt otvorenog koda kojim je omogućena audiovizualna komunikacija u stvarnome vremenu putem Web preglednika. Specifičnost ove tehnologije je u tome što koristi kriptiranje podataka prilikom komuniciranja između korisnika. U radu je opisana tehnologija WebRTC i protokoli koji osiguravaju enkripciju podataka, iskustvena kvaliteta te metoda strojnog učenja.
Istražen je utjecaj parametara video kodiranja na iskustvenu kvalitetu korisnika u slučaju mobilnog višekorisničkog poziva. U sklopu istraživanja provedeno je testiranje na 27 ispitanika te su prikupljeni rezultati. Pomoću dobivenih rezultata i snimljenog prometa napravljena je analiza te su isti korišteni u metodi strojnog učenja. Izvedeni su zaključci o uspješnosti predviđanja kvalitete videa s obzirom na kriptirani mrežni promet.

Abstract (english)

WebRTC is an open source project that provides Web browsers with real-time audiovisual communication. This technology uses encryption of data in communication between users. This thesis describes WebRTC technology and protocols used for data encryption, Quality of Experience, and machine learning.
The influence of video coding parameters on experiential quality of users in the case of mobile multi-party video conferencing has been elaborated. A study was conducted with 27 participants and results were analyzed. Obtained results in terms of objective video quality metrics and recorded network traffic were analyzed, and used to train machine learning-based classifers. Conclusions were drawn on the success of video quality prediction based on the analysis of encrypted network traffic.

• Keywords: WebRTC; iskustvena kvaliteta; kriptirani promet; strojno učenje
• Keywords (english): WebRTC; Quality of Experience; encrypted network traffic; machine learning
• Language: croatian
• URN:NBN: urn:nbn:hr:168:392174
• Study programme: Title: 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 resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2019-03-13 21:36:25