Name
Title | ||
|---|---|---|
Classification Of Content And Users In Bittorrent By Semi-Supervised Learning Methods |
Abstract | ||
|---|---|---|
P2P downloads still represent a large portion of today's Internet traffic. More than 100 million users operate BitTorrent and generate more than 30% of the total Internet traffic. Recently, a significant research effort has been done to develop tools for automatic classification of Internet traffic by application. The purpose of the present work is to provide a framework for subclassification of P2P traffic generated by the BitTorrent protocol. The general intuition is that the users with similar interests download similar contents. This intuition can be rigorously formalized with the help of graph based semi-supervised learning approach. We have chosen to work with a PageRank based semi-supervised learning method, which scales well with very large volumes of data. We provide recommendations for the choice of parameters in the PageRank based semi-supervised learning method. In particular, we show that it is advantageous to choose labelled points with large PageRank score. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IWCMC.2012.6314276 | 2012 8TH INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC) |
Keywords | Field | DocType |
machine learning,protocols,learning artificial intelligence,graph theory,networks,internet | Graph theory,PageRank,Semi-supervised learning,Computer science,Computer network,Download,Unsupervised learning,BitTorrent,Artificial intelligence,Machine learning,Internet traffic,The Internet | Conference |
ISSN | Citations | PageRank |
2376-6492 | 4 | 0.53 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantin Avrachenkov | 1 | 1250 | 126.17 |
| Paulo Gonçalves | 2 | 108 | 12.35 |
| Arnaud Legout | 3 | 781 | 49.30 |
| Marina Sokol | 4 | 67 | 4.39 |