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Analysis and modelling of resources shared in the BitTorrent network
BitTorrent is nowadays the most common protocol to distribute contents through peer‐to‐peer networks. Because of its relevance, it is important to study and understand its behaviour and impact from several points of view, including those related to communication efficiency and security risks. In thi...
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Published in: | Transactions on emerging telecommunications technologies 2015-10, Vol.26 (10), p.1189-1200 |
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Main Authors: | , , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | BitTorrent is nowadays the most common protocol to distribute contents through peer‐to‐peer networks. Because of its relevance, it is important to study and understand its behaviour and impact from several points of view, including those related to communication efficiency and security risks. In this paper, we analyse BitTorrent network focusing on the behaviour of shared resources. For that, we develop a monitoring methodology that allows to extract the time evolution of a sample of 1/256 of all the resources shared in the network. As a main difference with previous approaches, our methodology allows to monitor distributed hash table‐based BitTorrent networks, that is, without considering trackers. We discuss the data obtained from a monitoring process carried out during 3 months, and as a result, we analyse the information of more than 70 000 resources. This analysis is based on four features: geographic dispersion, popularity, sharing duration and availability. Finally, we show the potential of our methodology by outlining an example application that consists of a detection system intended to identify anomalous behaviours in the sharing of BitTorrent resources. Copyright © 2014 John Wiley & Sons, Ltd.
We use a monitoring methodology to extract the time evolution of a sample of 1/256 of all the resources shared in BitTorrent network. We discuss the data obtained from a monitoring process carried out during 3 months, and as a result, we analyse the information of more than 70 000 resources. This analysis is based on four features: geographic dispersion, popularity, sharing duration and availability. Finally, we outline an example application to identify anomalous BitTorrent resources. |
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ISSN: | 2161-3915 2161-3915 |
DOI: | 10.1002/ett.2859 |