<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dario Rossi</style></author><author><style face="normal" font="default" size="100%">Guilhem Pujol</style></author><author><style face="normal" font="default" size="100%">Xiao Wang</style></author><author><style face="normal" font="default" size="100%">Fabien Mathieu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Peeking Through the BitTorrent Seedbox Hosting Ecosystem</style></title><secondary-title><style face="normal" font="default" size="100%">Traffic Monitoring and Analysis (TMA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.enst.fr/ drossi/paper/rossi14tma-c.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper, we propose a lightweight method for detecting and classifying BitTorrent content providers with a minimal amount of resources. While&lt;br /&gt;heavy methodologies are typically used (which require long term observation&lt;br /&gt;and data exchange with peers of the swarm and/or a semantic analysis of torrent&lt;br /&gt;websites), we instead argue that such complexity can be avoided by analyzing&lt;br /&gt;the correlations between peers and torrents. We apply our methodology to study&lt;br /&gt;over 50K torrents injected in ThePirateBay during one month, collecting more&lt;br /&gt;than 400K IPs addresses. Shortly, we find that exploiting the correlations not&lt;br /&gt;only enhances the classification accuracy keeping the technique lightweight (our&lt;br /&gt;methodology reliably identifies about 150 seedboxes), but also uncovers seeding behaviors that were not previously noticed (e.g., as multi-port and multi-host&lt;br /&gt;seeding). Finally, we correlate the popularity of seedbox hosting in our dataset&lt;br /&gt;to criteria (e.g., cost, storage space, Web popularity) that can bias the selection&lt;br /&gt;process of BitTorrent content providers.&lt;/p&gt;</style></abstract></record></records></xml>