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Barracuda SPAM filtering Best Practices?

Accepted answer
edited Sep 8, 2017 11:20AM in General Functional Discussions 5 comments



I am wondering whether anyone can suggest some "best practices" for using the Barracuda SPAM filtering?

It seems there must be something we are not doing exactly right as the tool gives us way too many false positives in the Bayesian analysis.

I understand that there has to be a minimum of 200 messages classified as both SPAM and NOT SPAM before the Bayesian works, but was wondering whether maybe having too many in either one can be confusing for the database? We have performed a reset as we had close to 90k in the SPAM category and are currently at about 2k and just reaching 200 in the NOT SPAM category.

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