Get Aggressive at Fighting Spam by Re-training the Bayesian Learning Process in MDaemon

By Brad.Wyro

Fight spam with Bayesian Learning in MDaemon

In certain situations, it may be necessary to retrain your Bayesian Learning database. This can be necessary when spam messages are inadvertently placed in the Bayes non-spam folder, or when non-spam messages are placed in the Bayes spam folder.

To reset your Bayesian Learning and start training it again from scratch, you can perform the following steps:

1. Stop the MDaemon service.
2. Verify that the MDaemon executables (MDaemon.exe, CFEngine.exe, MDSpamD.exe, WorldClient.exe) have all exited memory using Windows task manager.
3. Rename the folder “/MDaemon/SpamAssassin/Bayes/” to”/MDaemon/SpamAssassin/Bayes.old/”
4. Re-launch MDaemon.
5. Go to Security | Spam Filter | Bayesian Classification, then click on the Learn button.

At this point, MDaemon recognizes that the Bayes folder isn’t there when the learn process is triggered, so it builds a new Bayes folder.

You will then need to feed Bayesian learning at least 200 spam and 200 non-spam messages (although the more the better) to start the Bayesian learning process again. Here is a knowledge base article on training the Bayesian learning process in MDaemon.

The Bayesian learning engine won’t process new messages until the administrator has taught it 200 spam and 200 non-spam messages. So even if an administrator were to manually press the Learn button OR have MDaemon learn automatically at midnight, the Bayesian engine wouldn’t apply itself to new messages even though the new folder is created.

Once MDaemon recognizes that Bayesian learning has learned more than 200 spam and 200 non-spam messages, it will start applying what it has learned to new messages.

You can run a script to determine how many messages the Bayesian filter has learned from. This will come in handy for administrators who need to know how many more messages to feed the Bayesian filter. This process is explained in this knowledge base article.

Source:: alt-n