Bayesian scans analyze the words (or “tokens”) in an message header and message body of an email to determine the probability that it is spam. For every token, the FortiMail unit calculates the probability that the email is spam based on the percentage of times that the word has previously been associated with spam or non-spam email. If a Bayesian database has not yet been trained, the Bayesian scan does not yet know the spam or non-spam association of many tokens, and does not have enough information to determine the statistical likelihood of an email being spam. By training a Bayesian database to recognize words that are and are not likely to be associated with spam, Bayesian scans become increasingly accurate.

However, spammers are constantly trying to invent new ways to defeat antispam filters. In one technique commonly used in attempt to avoid antispam filters, spammers alter words commonly identified as characteristic of spam, inserting symbols such as periods ( . ), or using nonstandard but human-readable spellings, such as substituting Â, Ç, Ë, or Í for A, C, E or I. These altered words are technically different tokens to a Bayesian database, so mature Bayesian databases may require some ongoing training to recognize new spam tokens.

You generally will not want to enable Bayesian scans until you have performed initial training of your Bayesian databases, as using untrained Bayesian databases can increase your rate of spam false positives and false negatives.