Peeking Through the BitTorrent Seedbox Hosting Ecosystem

In this paper, we propose a lightweight method for detecting and classifying BitTorrent content providers with a minimal amount of resources. While
heavy methodologies are typically used (which require long term observation
and data exchange with peers of the swarm and/or a semantic analysis of torrent
websites), we instead argue that such complexity can be avoided by analyzing
the correlations between peers and torrents. We apply our methodology to study
over 50K torrents injected in ThePirateBay during one month, collecting more
than 400K IPs addresses. Shortly, we find that exploiting the correlations not
only enhances the classification accuracy keeping the technique lightweight (our
methodology reliably identifies about 150 seedboxes), but also uncovers seeding behaviors that were not previously noticed (e.g., as multi-port and multi-host
seeding). Finally, we correlate the popularity of seedbox hosting in our dataset
to criteria (e.g., cost, storage space, Web popularity) that can bias the selection
process of BitTorrent content providers.