SpamAssassin trains the Bayes database as follows:

The lower the SpamAssassin score, the more probable the message is a ham.The higher the SpamAssassin score, the more probable the message is a spam.SpamAssassin and additional Kerio Connect antispam features can help the Bayesian self-learning: End-users must train the Bayes database enough to effectively fight mutating spam. In general, the Bayes database begins to work after it has learned at least 200 spams and 200 hams. The Bayes database must learn a lot of emails before it can function effectively. The filter breaks messages into small pieces called tokens and determines which tokens occur mostly in spam messages, and which tokens occur mostly in ham messages. Recipients can train the Bayes database to recognize messages as spam or ham. Therefore, constant upgrades are necessary to maximize the spam blocking capabilities. Unless the rules in SpamAssassin change, too, more and more spam gets in. It may result in SpamAssassin capturing most spam, however, it will always have some false positives and false negatives.Īlso, the content in spam changes over time and the spam mutates. SpamAssassin uses static rule sets to determine if a message is spam.įixed set of rules cannot accurately define spam for everybody. False Negative is a message that is incorrectly marked as ham.False Positive is a message that is incorrectly marked as spam.Ham is a message the recipient considers to be not spam.Spam is a message the recipient considers an unsolicited junk email.It is important to understand these problems in order to understand what Bayesian self-learning is and how it fits into Kerio's solution for spam protection. There are many problems associated with detecting spam for the final recipient of an email.

Spamassassin bayesian