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The SpamBayes project is working on developing a Bayesian anti-spam filter, initially based on the work of Paul Graham. The major difference between this and other, similar projects is the emphasis on testing newer approaches to scoring messages. While most anti-spam projects are still working with the original graham algorithm, we found that a number of alternate methods yielded a more useful response.
SpamBayes is not a single application.
The core code is a message classifier, however there are several applications available as part of the SpamBayes project which use the classifier in specific contexts. For the most part, the current crop of applications all operate on the client side of things, however, a number of people have experimented with using SpamBayes on mail servers to classify incoming mail for multiple users. The table below outlines the main applications which are part of the SpamBayes distribution.
SpamBayes will attempt to classify incoming email messages as 'spam', 'ham' (good, non-spam email) or 'unsure'. This means you can have spam or unsure messages automatically filed away in a different mail folder, where it won't interrupt your email reading. First SpamBayes must be trained by each user to identify spam and ham. Essentially, you show SpamBayes a pile of email that you like (ham) and a pile you don't like (spam). SpamBayes will then analyze the piles for clues as to what makes the spam and ham different. For example; different words, differences in the mailer headers and content style. The system then uses these clues to examine new messages.
For instance, the word "Nigeria" appears often in spam, so you could use a spam filter which identifies anything with that word in it as spam. But what if your business involves writing a guidebook on Nigerian Wildlife Conservation? Clearly a more flexible approach is necessary. Additionally spammers will adapt their content over time and will no longer use the word "Nigeria" (or the words "Lose Weight Fast", or any number of other common lines). Ideally the software will be able to adapt as the spam changes.
So, that is what SpamBayes does. It compares the spam and the ham and calculates probabilities. For instance, for me, the word "weight" almost never occurs in legitimate email, but it occurs all the time in 'lose weight fast' spam. SpamBayes can then look at incoming email, extract the most significant clues and combine the probabilities to produce an overall rating of "spamminess". It flags the messages so that your mailer can handle the different message types. You might set it up so that ham goes straight through untouched, spam goes to a folder that you ignore (or delete without checking) and the unsure messages go to another folder which you can review for errors.