Howdy,

Well, I've implemented the new bigevil.cf, the nov2rules, and the combined
popcorn/weeds/backhair rules (dated from 2003/10/14) from
merchantsoverseas.com (Oh, thank you for both, Jennifer and Chris!), and am
now getting about 85-88% detection vs. about 70-75% before, using SA 2.60.
I've also got Bayes autolearning turned on, and it's definitely active. I am
skipping RBL checks, and the other network checks, as being too resource
intensive. The good news is that I haven't gotten a single FP since
implementing. That's really nice.

Is there anything I can do to help improve the detection rate? I'm willing
to share a PST of some examples of uncaught spam (i.e., with scores lower
than 5), if desired. I haven't gotten to the suggested tactic of using PFs
in Exchange and fetchmail to seed the Bayes database, but that's coming.
(Thanks, Kent!)


A little explanation might be in order - I'm measuring against mails that
I'm getting that are addressed to abot 40 former employees. All emails get
forwarded to our Exchange server, and since I can consider all of these
mails to the former employees to be spam, all I have to do is save all of
those to a separate directory, and analyze them, instead of all of the mails
on the Exchange server. 

This helps a great deal, since it isn't a measurement against all of the
current employees, which I think would be far too time-consuming to figure
out, given that I'd have to separate out the non-internet mail from the
internet mail, then classify what what caught by spamassassin vs. not, etc.
I'm making the (hopefully warranted) assumption that the spam that those who
are departed is pretty much the same as the spam that those who are
currently employed are receiving.


Thanks all,

Kurt Buff
Sr. Network Administrator
Zetron, Inc.
425.820.6363 x463
[EMAIL PROTECTED]
PO Box 97004
Redmond, WA 98073



  



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