Administrator's Guide - Kerio Software Archive

16.4 SpamAssassinFigure 16.7SpamAssassinContent evaluationContent evaluation is based on statistical filtering using the message’s contents (keywords,number of capital letters, message format, etc.). Each incoming message is assigned a numericscore according to the number of characters significant for spam messages. A higher scoreindicates a higher probability of spam.Bayesian filterAnother module involved is the Bayesian filter. It is a special antispam filter which is able to“learn” to recognize spam messages. This filter compares the individual spam characteristicswith actual messages. The method consists of two concurrent modes:• “Autolearn” — the filter learns by itself.• “Learn” — users are involved in the learning process. Users have to reassign the incorrectlyevaluated messages to correct types (spam / non-spam) so that the filter learns to recognizethem in the future.200 unique spams and 200 unique hams (legitimate messages) must be collected to make thefilter work. This means that such messages must vary. Each spam message is involved onlyonce. Other occurrences of an identical message will be ignored.Bayesian filter sums spams and hams learned by the learn and autolearn methods. TheSpamAssassin tab contains statistics that monitor how many messages have been markedas spam or ham and whether the filter is already active or has not learn enough spam andham messages yet. Once activated, the learning process keeps on introducing new items inthe database.183