don't reply offlist! Am 04.03.2015 um 14:13 schrieb Filip Havlíček:
So you recommend set parameter *bayes_auto_learn* to value *0*? I had truncate tables and try set bayes_auto_learn 0 in /etc/spamassassin/local.cf but it does not work - new hundrends records of unknown email addresses occured in tables *bayes_vars*, *bayes_token* and *bayes_seen* :-(. Any ideas?
is /etc/spamassassin/local.cf really correct? if it si are the permissions correct? "/etc/mail/spamassassin/local.cf" is the correct path here how is your SA called? look for "user_prefs" in ~/.spamassassin/ no idea what "bayes_vars" is
Dne 4.3.2015 v 13:45 Reindl Harald napsal(a):Am 04.03.2015 um 13:35 schrieb Filip Havlíček:I would like to ask you, how can I *allow **only **legitimate* email addresses (existing users) for bayes learning? Table bayes_token grow up to 0,5GB right now, because there are thounsands of unknown email addresses like: a...@hotmail.com ablewi...@hotmail.com abl...@hotmail.comdon't use auto-learning or at least adjust the scores which are taken for autolearning - SpamAssassin can't know if a address exists while you could use http://www.postfix.org/ADDRESS_VERIFICATION_README.html on the MTA level *but* be careful with sender verification, you need to place a lot of DNSWL in front to not become blacklisted for your own i guess your main problem is that way too much mail makes it to SA at all instead block it by RBL scoring and other MTA restrictions long before - see below an example, all the stuff before the bayes stats never touched SpamAssassin __________________________________________________ Connections: 314179 Postscreen: 171577 Helo: 1435 Subject: 187 Attachment: 29 Header Length: 8 Sender Regex: 263 Sender Blocked: 174 Sender Verify: 301 Sender Invalid: 1622 Sender Spoofed: 10 Sender Parked: 10 PTR Missing: 227 PTR Generic: 447 SPF: 709 __________________________________________________ BAYES_00 46223 77.63 % BAYES_05 733 1.23 % BAYES_20 894 1.50 % BAYES_40 957 1.60 % BAYES_50 6463 10.85 % BAYES_60 641 1.07 % BAYES_80 472 0.79 % BAYES_95 344 0.57 % BAYES_99 2814 4.72 % BAYES_999 2452 4.11 %
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