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These days, the choice of spam filters comes down to and. Other choices, like, are no longer in development.

Although a few other choices (e.g., ) are available, when an email reader offers a plugin, it is almost always for either Bogofilter or SpamAssassin. However, what is less often discussed is which filter is the best to use in which circumstances. Instead, most users simply nod solemnly when they read that both involve “Bayesian filtering.” Most of us – including many who use the phrase – have no idea what Bayesian filtering is, but it sounds scientific and reassures us that either choice is acceptable. In fact, learning that Bogofilter and SpamAssassin are “Bayesian” is useless for choosing between them. To call them Bayesian means nothing more than their structure is based on the the 18th century work of in statistics and probability. More specifically, both apply Bayes’ work by collecting words and assigning a probability that each word indicates spam. The more suspect words contained in an email, the greater the chances it is spam.

However, to make an informed choice between spam filters requires considerably more detail. Bogofilter Bogofilter has its roots in “,” a 2002 essay by English developer Paul Graham. After trying to develop filters based on the identifying characteristics of spam, Graham concluded that beyond a certain point, the more rules he added, the more false positives he obtained – that is, the more email messages that were incorrectly identified as spam. Graham’s solution was to parse his samples of spam and non-spam into tokens, or individual words, and use Bayesian tools to assign each token the possibility that it indicates spam, biasing them slightly in favor of not being spam to minimize false positives. By examining the top 15 tokens in the header and body of each new email message, he calculated the possibility that it was spam.

If the probability was greater than 0.9, the message was considered spam. According to Graham, the advantage of this statistical approach is that it refers to something real – the probability of being spam – and worked with both neutral and spam-indicating words. However, he also recognized that the more personalized the filter was, the more accurate it would be.

For this reason, he also included the possibility of using white lists to indicate non-spam, or “ham,” and black lists to indicate spam. After reading Graham’s essay, Eric S. Raymond founded the Bogofilter project. Save Game Yugioh Forbidden Memories Full Cardigan.

Today, Bogofilter is maintained by other developers,and has refined Graham’s calculations based on. The modern refinements include recognizing MIME types, treating each hostname and IP address as a separate token (rather than dividing them up into separate words), and ignoring dates and Message-IDs as irrelevant. However, the basic approach remains that advocated by Graham. The mathematically inclined can learn more about how Bogofilter assigns the probability of an email being spam by following the links and reading the man page for the filter.

However, the most important point for the average user is that Bogofilter relies on statistical probability, supplemented by each user’s list of spam and ham. Advocates of this approach emphasize its simplicity, as well as its lower number of false positives once it is trained – that is, once the white and black lists are produced. These lists are contained in the.bogofilter folder in your home directory. SpamAssassin SpamAssassin takes a different approach from Bogofilter. SpamAssassin’s main approach is to identify the characteristics of spam and then run tests to locate them.

Many tests, although not all, rely heavily on regular expressions to catch variations of words and phrases. You can view the Perl scripts used by SpamAssassin in /usr/share/spamassassin. More than 50 are listed in my current installation of Debian Stable. From their number alone, you can tell they are a varied lot, but they include tests for the common indicators of spam in headings, in the bodies of email, and in HTML code, as well as tests for recognizing offers for anti-viruses, drugs, and pornography. In the English version, some basic tests for French, German, and Italian are also included. They also include a Bayesian probability test similar to Bogofilter’s, as well as white and black lists for individual customization. Figure 2: Debian adds its own SpamAssassin tests to the already comprehensive list.