Analyzing peer-to-peer traffic across large networks
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Tracing a Large-Scale Peer to Peer System: An Hour in the Life of Gnutella
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Transport layer identification of P2P traffic
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Stochastic Fluid Model for P2P Caching Evaluation
WCW '05 Proceedings of the 10th International Workshop on Web Content Caching and Distribution
Should internet service providers fear peer-assisted content distribution?
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
A survey and comparison of peer-to-peer overlay network schemes
IEEE Communications Surveys & Tutorials
A bird's eye view on the I2P anonymous file-sharing environment
NSS'12 Proceedings of the 6th international conference on Network and System Security
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In an effort to prosecute P2P users, RIAA and MPAA have reportedly started to create decoy users: they participate in P2P networks in order to identify illegal sharing of content. This has reportedly scared some users who are afraid of being caught. The question we attempt to answer is how prevalent is this phenomenon: how likely is it that a user will run into such a "fake user" and thus run the risk of a lawsuit? The first challenge is identifying these "fake users". We collect this information from a number of free open source software projects which are trying to identify such IP address ranges by forming the so-called blocklists. The second challenge is running a large scale experiment in order to obtain reliable and diverse statistics. Using Planetlab, we conduct active measurements, spanning a period of 90 days, from January to March 2006, spread over 3 continents. Analyzing over a 100 GB of TCP header data, we quantify the probability of a P2P user of being contacted by such entities. We observe that 100% of our nodes run into entities in these lists. In fact, 12 to 17% of all distinct IPs contacted by any node were listed on blocklists. Interestingly, a little caution can have significant effect: the top five most prevalent blocklisted IP ranges contribute to nearly 94% of all blocklisted IPs and avoiding these can reduce the probability of encountering blocklisted IPs to about 1%. In addition, we examine other factors that affect the probability of encountering blocklisted IPs, such as the geographical location of the users. Finally, we find another surprising result: less than 0.5% of all unique blocklisted IPs contacted are owned explicitly by media companies.