LENS: LEveraging anti-social Networking against Spam

نویسندگان

  • Sufian Hameed
  • Pan Hui
  • Xiaoming Fu
چکیده

Spam is still an open problem from the network operator’s perspective. The common state-of-the-art strategy to place filters against spam is at the recipient’s edge. Although this strategy largely solves the spam problem from the user’s perspective – false positives/negatives may still exist – it cannot prevent spam from traversing the Internet. Consequently, with nowadays around 200 billion spam/day, spam continues to consume large amounts of Internet bandwidth and provokes non-negligible financial loss to network operators. Therefore it becomes imperative to mitigate spam much earlier than at the recipient’s edge. This goal has been recently accomplished only partially by placing filters at the edge of a social circle within a social network. In this paper we introduce LENS, a novel spam protection system based on the anti-social networking paradigm, which further mitigates spam beyond social circles. The key idea of this paradigm in LENS is to let users select legitimate and authentic users, called Gatekeepers (GKs), from outside their social circle and within pre-defined social distances. Unless a GK vouches for the emails of potential senders from outside the social circle of a particular recipient, those e-mails are prevented from transmission. This way LENS drastically reduces the consumption of Internet bandwidth by spam to control messages only. To evaluate the scalability of LENS we use publicly available online social network (OSN) datasets and demonstrate that it is feasible to use GKs in the order of hundreds to provide reliable email delivery from millions of potential users. Using real email traces from large commercial and academic units, we demonstrate that LENS is very effective in accepting all inbound legitimate emails.

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تاریخ انتشار 2010