Author: Zhang, Qi
Title: Online game traffic classification
Degree: M.Sc.
Year: 2009
Subject: Hong Kong Polytechnic University -- Dissertations.
Computer games.
Internetworking (Telecommunication)
Machine learning.
Department: Department of Computing
Pages: ix, 73 leaves : ill. ; 30 cm.
Language: English
Abstract: Online games are increasing becoming popular in recent years. Especially, multiplayer online games have received much attention in the last few years. There are, however, the characteristics of the online game traffic including browser game, massively multiplayer online game (MMOG), and other type games are understood poorly. The dissertation presents a study on the characteristics of online browser-based game traffics and compares their traffic behavior with web surfing traffic. Furthermore, the identification of game traffic in the Internet is very useful for network to meet Quality of Service (QoS) requirements. Previous traffic identification techniques, such as port-based classification and payload-based classification, are all no longer accurate. Therefore, the newest approach is to use Machine Learning (ML) to classify network applications automatically. In this work, I choose several classifier and clustering machine learning algorithms to identify sixe different types game traffics. They are Mahjong, FashsionDash, ClubMarian, Globulos, Spine World and Card games. Both chosen classifier and cluster algorithms have good performance on identifying traffics. In particular, the highest accuracy is 87% by using C4.5 decision tree algorithm and other algorithms accuracy are all over 77%. And k-means clustering algorithm also has a higher accuracy, about over 81%.
Rights: All rights reserved
Access: restricted access

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