>On 08/08/2018 15:04, Matus UHLAR - fantomas wrote:
>>...of last 40 mail in my spambox, 14 matches MAILING_LIST_MULTI
>>...of last 100 mail in spambox, 27 matches MAILING_LIST_MULTI
On 09.08.18 08:54, Daniele Duca wrote:
>I practically zeroed MAILING_LIST_MULTI the day it came in the
>ruleset.
On 09.08.18 23:52, RW wrote:
MAILING_LIST_MULTI has the default "nice" score of -1.0 rather than an
explicit score. I'm wondering if this is deliberate.
I would guess so.
... and so I had to enlarge (-1 => -0.1) the score on another host.
seems more and more mailing lists are being abused (or deliberately used) to
spread spam.
>>but not possible to put:
>>
>>tflags BAYES_99 learn dothefuckingautolearn
>Personally I'll never trust BAYES_* with autolearn_force. I saw some
>FPs sometimes and I fear that autolearning would quickly lead to
>poisoning
I would advise against using auto-training where it's possible to
train manually. It's not just a matter of mistraining, autolearning may
also bias the database in favour of types of spam that are easily
caught, thereby diluting the frequencies of tokens needed to catch the
difficult spam.
the same applies about ham, however
with autolearn_force yes, it could apparently lead to poisoning.
However, if "learn" only did its job (whatever it is) and only
"noautolearn" would ignore the score, it would be just enough.
Currently, as docs say, "learn" in fact implicates "noautolearn".
As does userconf.
So, both "learn" and "userconf" explicitly implicate "noautolearn"?
I wonder why we have them at all. And what is
I just don't understand why. Simply use both flags and that's it.
If you really must do this just create a new rule without tflags and
then score it something like this:
3.0 3.0 0.001 0.001
i.e so it's scored in the non-Bayes score sets. You can just modify
the scores and tflags of an original rule, but that's less flexible.
I have just listed all rules with negative scores, and surprise, I haven't
found any realiable rule with negative score.
(MAILING_LIST_MULTI added manually as it doesn't have score set explicitly)
It seems that I will need to whitelist and use the hack you have proposed
above.
- unreliable rules
ALL_TRUSTED -1.000
ENCRYPTED_MESSAGE -1.000 -1.000 -1.000 -1.000
ENV_AND_HDR_SPF_MATCH -0.5
DKIM_VALID -0.1
DKIM_VALID_AU -0.1
DKIM_VALID_EF -0.1
HASHCASH_20 -0.5
HASHCASH_21 -0.7
HASHCASH_22 -1.0
HASHCASH_23 -2.0
HASHCASH_24 -3.0
HASHCASH_25 -4.0
HASHCASH_HIGH -5.0
MAILING_LIST_MULTI -1.000
- not used for autolearning
BAYES_00 0 0 -1.5 -1.9
BAYES_05 0 0 -0.3 -0.5
- not available everywhere
DCC_REPUT_00_12 0 -0.8 0 -0.4
DCC_REPUT_13_19 0 -0.1 0 -0.1
- DNS whitelists
RCVD_IN_DNSWL_HI 0 -5 0 -5
RCVD_IN_DNSWL_LOW 0 -0.7 0 -0.7
RCVD_IN_DNSWL_MED 0 -2.3 0 -2.3
RCVD_IN_IADB_DK 0 -0.223 0 -0.095 # n=0 n=1 n=2
RCVD_IN_IADB_DOPTIN 0 -4 0 -4
RCVD_IN_IADB_LISTED 0 -0.380 0 -0.001 # n=0 n=2
RCVD_IN_IADB_MI_CPR_MAT 0 -0.332 0 -0.000 # n=0 n=1 n=2
RCVD_IN_IADB_ML_DOPTIN 0 -6 0 -6
RCVD_IN_IADB_OPTIN 0 -2.057 0 -1.470 # n=0 n=1 n=2
RCVD_IN_IADB_OPTIN_GT50 0 -1.208 0 -0.007 # n=0 n=2
RCVD_IN_IADB_RDNS 0 -0.167 0 -0.235 # n=0 n=1 n=2
RCVD_IN_IADB_VOUCHED 0 -2.2 0 -2.2
RCVD_IN_RP_CERTIFIED 0.0 -3.0 0.0 -3.0
RCVD_IN_RP_SAFE 0.0 -2.0 0.0 -2.0
DKIMDOMAIN_IN_DWL 0 -3.5 0 -3.5
- local whitelists:
HEADER_HOST_IN_WHITELIST -100.0
SUBJECT_IN_WHITELIST -100
URI_HOST_IN_WHITELIST -100.0
USER_IN_ALL_SPAM_TO -100.000
USER_IN_DEF_DKIM_WL -7.500
USER_IN_DEF_SPF_WL -7.500
USER_IN_DEF_WHITELIST -15.000
USER_IN_DKIM_WHITELIST -100.000
USER_IN_MORE_SPAM_TO -20.000
USER_IN_SPF_WHITELIST -100.000
USER_IN_WHITELIST -100.000
USER_IN_WHITELIST_TO -6.000
--
Matus UHLAR - fantomas, uh...@fantomas.sk ; http://www.fantomas.sk/
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