Efficient blacklisting and pollution-level estimation in P2P file-sharing systems

Abstract. P2P file-sharing systems are susceptible to pollution attacks, whereby corrupted copies of content are aggressively introduced into the system. Recent research indicates that pollution is extensive in several file sharing systems. In this paper we propose an efficient measurement methodology for identifying the sources of pollution and estimating the levels of polluted content. The methodology can be used to efficiently blacklist polluters, evaluate the success of a pollution campaign, to reduce wasted bandwidth due to the transmission of polluted content, and to remove the noise from content measurement data. The proposed methodology is efficient in that it does not involve the downloading and analysis of binary content, which would be expensive in bandwidth and in computation/human resources. The methodology is based on harvesting metadata from the file sharing system and then processing off-line the harvested meta-data. We apply the technique to the FastTrack/Kazaa file-sharing network. Analyzing the false positives and false negatives, we conclude that the methodology is efficient and accurate.