A Study of Supervised Spam Detection applied to Eight Months of Personal E-Mail
Gordon V. Cormack, Thomas R. Lynam · 2004
In the last year or two, unwelcome email has grown to the extent that it is inconvenient, annoying and wasteful of computer resources. More significantly, its volume threatens to overwhelm our ability to recognize welcome messages, and hence to destroy our trust in email as a reliable communication medium. An automatic spam filter can mitigate these problems, provided that it acts in a reliable and predictable manner. We evaluate ten spam detection methods embodied in six popular open-source spam filters by applying each method sequentially to all of the e-mail received by one individual (X) from August 2003 through March 2004. These 49,086 messages were originally judged in real-time by X. The messages and judgements were recorded, and reproduced so as to provide the same evaluation suite for all the methods. Five of the methods are derived from Spamassassin [spamassassin.org 2004], a hybrid system which includes both static spam-detection rules and a Bayesian statistic