Here is an example header:
X-Spam-Status: No, score=3.0 required=5.0 tests=AWL,CELL_PHONE_FREE, HTML_90_100,HTML_MESSAGE,HTML_TAG_EXIST_TBODY,HTML_TEXT_AFTER_BODY, HTML_TEXT_AFTER_HTML,HTML_WEB_BUGS,MIME_HTML_ONLY autolearn=no version=3.0.2
On Jan 26, 2005, at 10:59 AM, Matt Kettler wrote:
At 11:47 AM 1/26/2005, Jeffrey Lee wrote:I have been using sa-learn religiously with ALL spam and ham on my server. However, I keep getting repeat spam with low scores. How can I increase the sa-learn "points"? So that when I learn a message instead of increasing some point by .1 or .2 it will increase by .5 or .6?
Well, sa-learning a message doesn't really work by increasing the "points" of a message, although that's more-or-less the net effect.
In short, you'll want to make sure your inbound messages are hitting BAYES_90 or higher, and increase the scores of those rules in your local.cf.
Also, while you're at it, check for spam messages matching ALL_TRUSTED. If that's happening, check the archives on setting trusted_networks manually. That rule should *never* match spam but will if SA gets confused by your MTA config.
If the spam messages are consistently hitting BAYES_99, sa-learning won't increase the score of that message further, but it does help SA recognize subtle changes over time in spam. So keep up the training as it will keep slight deviations from driving the bayes scores down and causing FN problems that way.
When you sa-learn a message, SA learns that the words in that message are more likely to be in spam or ham than it previously new. When new messages come in, SA looks at it's database of words and calculates a spam probability based on the words in that message. It then matches that probability to one of the BAYES_* rules and that causes the score impact.
