prikaz prve stranice dokumenta Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa uporabom tehnike strojnog učenja
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master's thesis
Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa uporabom tehnike strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2016. urn:nbn:hr:168:847619
Oršolić, Irena
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Oršolić, I. (2016). Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa uporabom tehnike strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:847619

Oršolić, Irena. "Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa uporabom tehnike strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2016. https://urn.nsk.hr/urn:nbn:hr:168:847619

Oršolić, Irena. "Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa uporabom tehnike strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2016. https://urn.nsk.hr/urn:nbn:hr:168:847619

Oršolić, I. (2016). 'Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa uporabom tehnike strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 23 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:847619

Oršolić I. Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa uporabom tehnike strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2016 [cited 2024 November 23] Available at: https://urn.nsk.hr/urn:nbn:hr:168:847619

I. Oršolić, "Procjena performansi usluge YouTube na osnovu analize kriptiranog prometa uporabom tehnike strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2016. Available at: https://urn.nsk.hr/urn:nbn:hr:168:847619

Metadata
TitleProcjena performansi usluge YouTube na osnovu analize kriptiranog prometa uporabom tehnike strojnog učenja
Title (english)YouTube performance estimation based on the analysis of encrypted network traffic using machine learning
AuthorIrena Oršolić
MentorLea Skorin-Kapov (mentor)
Committee memberLea Skorin-Kapov (predsjednik povjerenstva)
Committee memberMaja Matijašević (član povjerenstva)
Committee memberMirko Sužnjević (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2016-07-07, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Ovaj rad nastao je u okviru projekta koji se bavi procjenom iskustvene kvalitete usluge YouTube na osnovu analize kriptiranog mrežnog prometa. Tokom projekta, razvijen je sustav YouQ, u svrhu prikupljanja podataka na klijentskom uređaju (Android pametnom telefonu) i u mreži. Sustav čini i YouQ poslužitelj za obradu i prikaz podataka. Fokus ovoga rada je na drugoj fazi projekta: korištenju algoritama strojnog učenja nad podacima prikupljenim sustavom YouQ kako bi se procjenila klasa iskustvene kvalitete video strujanja. Analizirani skup podataka sastoji se od 343 primjera opisana s 54 značajke mrežnog prometa i klasom iskustvene kvalitete ("high", "medium", "low"). Korištenjem "wrapper" metoda u alatu WEKA odabrani su podskupovi značajki za četiri algoritma strojnog učenja: Naive Bayes, SMO, J48, i Random Forest. Izgrađeni su klasifikacijski modeli korištenjem algoritma OneR i četiri prethodno navedena algoritma. Točnost klasifikacije je procijenjena i raspravljena. Rezultati pokazuju da je najtočniji model onaj stvoren koristeći algoritam Random Forest, s točnošću 74%. Najviše pogrešaka u procjeni dogodilo se između klasa "low" i "medium", u eksperimentima s promjenama mrežne propusnosti. Osim opisa metodologije manipulacije značajkama, izgradnje i evaluacije modela, ovaj rad pruža teoretske informacije o algoritmima strojnog učenja, kratki uvod u korištenje WEKA alata i statističku analizu podataka prikupljenih na klijentskom uređaju i u mreži. Također, kako bi skup podataka korišten za učenje bio što jasniji, opisano je laboratorijsko okruženje i metodologija prikupljanja podataka.
Abstract (english)
This thesis was completed in the scope of a project focused on YouTube QoE prediction based on encrypted network traffic. During this project, a system called YouQ was developed for the purpose of collecting data both on a client device (Android smartphone) and in the network. The system also includes the YouQ server for processing and visualizing data. This thesis focuses on the second phase of the project: using machine learning algorithms on data collected by the YouQ system to predict QoE classes of each YouTube video streaming session. The dataset that was analysed consists of 343 instances described with 54 network traffic features and a QoE class label ("high", "medium", "low"). The dataset was subset using wrapper methods in the WEKA machine learning tool for four machine learning algorithms: Naive Bayes, SMO, J48, and Random Forest. Classification models were created using OneR and the four listed algorithms. Classification accuracy was assessed and discussed. The results show that the most accurate was the model created using the Random Forest algorithm, with 74% accuracy. Most of the errors happened between classes "low" and "medium" and in the experiments with bandwidth fluctuations. Besides presenting the methodology of feature manipulation, model creation, and evaluation, this thesis provides theoretical information about algorithms, a short guide to using WEKA, and statistic analysis of both application-level and network-level data. Further, to better understand the dataset that was used as an input to machine learning algorithms, the experiment setup and data collection methodology has been described.
Keywords
Keywords (english)
Languageenglish
URN:NBNurn:nbn:hr:168:847619
Study programmeTitle: Information and Communication Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije (magistar/magistra inženjer/inženjerka informacijske i komunikacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2018-02-06 11:00:34