python script as an emergency mailbox cleaner

Phil Weldon pweldon at
Sun Sep 21 18:36:14 CEST 2003

I don't think 'fewer' examples of bogus 'Undeliverable e-mail' messages will
be 'better' because of the permutating and morphing nature of this worm
generated message.  'Fewer' examples would result in ALL 'Undeliverable
e-mail' message catagorized as objectionable because the number valid
messages of this type is so small in the save e-mail that most users have.

In addition, I'd opt for the commercial product because protecting my
systems against infection is not what I want to spend my time doing, and I'm
quite willing to pay for that product.  Now, if I can just find a way to
charge the cost to Earthlink because of their failure to perform their
implicit contract to provide reliable e-mail service.

Phil Weldon, pweldon at

"Tim Peters" < at> wrote in message
news:mailman.1064156609.10422.python-list at
> [Phil Weldon]
> > Inboxer for Outlook is a plugin written with Python that will analyze
> > collections of what you consider legitimate e-mail and and what you
> > consider illegitimate e-mail.
> The classification engine in Inboxer comes from the free spambayes
> Inboxer is a commercial product (produced by some old friends of mine from
> Dragon Systems, but I have no other connection to it), which can afford to
> pay people to research and add ease-of-use features.  The spambayes
> is behind on that count, but for the technically-minded should perform
> equally well.
> > I downloaded it and ran it against a collection of 1500 messages
> > generated by the Worm.Automat.AHB and 265 the latest legitimate
> > e-mails I've received.
> The spambayes engine works best when trained on approximately equal
> of ham and spam.  You should actually get better results if you train on
> *fewer* than 1500 of a particular species of spam.  In my home classifier,
> eee I've trained on 6 slightly different instances of Worm.Automat spew,
> that's all.  All the rest I've gotten were classed as spam (but I have my
> spam cutoff set to 80, and IIRC Inboxer defaults that to 90).
> > After the analysis, Inboxer has detected about 250 Worm.Automat.AHB
> > generated messages with no false negatives and no false positives
> > (granted there were only three new legitimate e-mails.
> If you start getting some, the paradoxical best thing to do would, again,
> to train on *fewer* worm spew messages.

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