Robert Schetterer schrieb:
arni schrieb:
aymond
as i said several times on this maillist now, i've never had any of
these mails get through, here is how the current ones score:
you are in a luck,
you are a "late reciever" of that spam, so it was detected
by others before ( look at your headers )
but it wasnt detected by i.e a plain pdf_spam rule/solution
( like fuzzy_ocr etc )
this is what i am looking for
I looked for the lowest scoring email of the past 2 days (dont save them
longer), this is the one:
X-Spam-Status: Yes, score=10.7 required=5.0 tests=BAYES_99,DCC_CHECK,
DKIM_POLICY_SIGNSOME,HTML_MESSAGE,LOGINHASH1,LOGINHASH2,MIME_HTML_MOSTLY
autolearn=no version=3.2.0
X-Spam-Report:
* 5.5 BAYES_99 BODY: Bayesian spam probability is 99 to 100%
* [score: 1.0000]
* 0.0 DKIM_POLICY_SIGNSOME Domain Keys Identified Mail: policy says
domain
* signs some mails
* 0.0 MIME_HTML_MOSTLY BODY: Multipart message mostly text/html MIME
* 0.0 HTML_MESSAGE BODY: HTML included in message
* 1.5 LOGINHASH2 BODY: mail has been classified as spam @ unknown
company,
* Germany
* 1.5 LOGINHASH1 BODY: mail has been classified as spam @
LogIn&Solutions
* AG, Germany
* 2.2 DCC_CHECK Listed in DCC (http://rhyolite.com/anti-spam/dcc/)
Note that already a well trained BAYES can take these mails out on its
own on my system.
If you find your bayes to score really acurate then its a good idea to
increase the scores. For me bayes is fed from 2 spamtrap addresses with
around 50 pieces of the finest spam every day. Doing this, bayes scores
BAYES_99 on 99.5% of my remaining spam - i hardly ever see it score
below BAYES_80 and thats just great.
So maybe training bayes better or increasing the score will put and end
to this for you.
arni
