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Training the bayesian learning process in MDaemon Pro

Training the bayesian learning process in MDaemon Pro

Purpose & Scope

As a spam-fighting tool, Bayesian filtering ”learns” to detect junk mail and legitimate mail by analyzing the header, subject and content of received messages known for sure to be either spam or non-spam. The Bayesian process assigns a spam-probability to each word, domain name, HTML code or other ”token” in each message. Bayesian filtering then uses this data to determine if new incoming messages are likely spam or non-spam. Because Bayesian filtering analyzes messages received at each email server, the ”token” probabilities are site-specific. As part of the Bayesian process, MDaemon has tools for setting up separate folders to receive copies of messages known to be spam and known to be legitimate mail. Bayesian filtering obtains its data by analyzing the messages in these folders.By regularly adding new known spam and non-spam into the Bayesian system, spam filtering ”learns” to be more reliable in distinguishing between the two over time for each email server. MDaemon uses the Bayesian results to further refine the ”scores” it assigns to messages.

Bayesian will start scoring messages after 200 spam and non-spam messages are fed to it. How accurate it is relies on the way in which it is fed.  Your users can contribute to the spam learning by 'feeding' the bayesian process.

Procedure

WorldClient's Standard and LookOut themes have buttons that users can use to tell MDaemon that a message is spam or not spam.  This is available in the 9.x series and above.

IMAP users can drag and drop spam and non-spam messages into the appropriate folders from their mail client.  The rights for the spam and non-spam folders are set under Setup | Shared Folders | Public Folders.  The users will need at least 'LI' rights to be able to copy messages in.

POP3 users can forward messages to MDaemon.  See full instructions here: http://www.altn.com/Support/KnowledgeBase/KnowledgeBaseResults/?Number=KBA-01589