Detecting Spammers with SNARE: Spatio-temporal Network-level Automatic Reputation Engine Shuang Hao, Nadeem Ahmed Syed, Nick Feamster, Alexander G. Gray, Sven Krasser ∗ College of Computing, Georgia Tech ∗McAfee, Inc. {shao, nadeem, feamster, agray}@cc.gatech.edu,
[email protected] Abstract can block certain types of unwanted email messages, but they can be brittle and evadable, and they require ana- Users and network administrators need ways to filter lyzing the contents of email messages, which can be ex- email messages based primarily on the reputation of pensive. Hence, spam filters also rely on sender repu- the sender. Unfortunately, conventional mechanisms for tation to filter messages; the idea is that a mail server sender reputation—notably, IP blacklists—are cumber- may be able to reject a message purely based on the rep- some to maintain and evadable. This paper investigates utation of the sender, rather than the message contents. ways to infer the reputation of an email sender based DNS-based blacklists (DNSBLs) such as Spamhaus [7] solely on network-level features, without looking at the maintain lists of IP addresses that are known to send contents of a message. First, we study first-order prop- spam. Unfortunately, these blacklists can be both in- erties of network-level features that may help distinguish complete and slow-to-respond to new spammers [32]. spammers from legitimate senders. We examine features This unresponsiveness will only become more serious that can be ascertained without ever looking at a packet’s as both botnets and BGP route hijacking make it easier contents, such as the distance in IP space to other email for spammers to dynamically obtain new, unlisted IP ad- senders or the geographic distance between sender and dresses [33, 34].