Message-ID: <1065586704.2735.34.camel@tantor.nuclearelephant.com>
From: jonathan at nuclearelephant.com (Jonathan A. Zdziarski)
Subject: Spam with PGP

> Bayesian filters have had some amazing successes. The problem we (the 
> company I work for) continue to have, and the reason we continue to 
> choose SA, is that training a thousand users on how to use a Bayes 
> system is pretty much impossible (and we're small compared to many!) 
> Assuming that I give you (I'm do not believe it, but will give it for 
> the sake of argument) that Bayes is the best theoretical solution, the 
> Bayes folks have a problem in implementation. Training users is not 
> easy; think about training your mother or grandmother but multiply by 1000.

This is why two features exist, both which I think are components of any
good Bayesian solution:

1. User groups.  The ability to clump a large group of users who share
similar email behavior together sharing one dictionary and one spam
alias.  This is ideal for departments within corporations where email is
expected to be primarily for company use.

2. A merge tool.  A tool that will allow an administrator (or script) to
merge the dictionaries from N users over a large span of diversity to
create a single seeded dictionary for a new user.

This should solve a majority of your problem.  Granted, seeded
dictionaries still take a "little" bit of learning, but it's a lot
easier for granny to get going with one of them.

> The point is not that you are wrong; indeed, I'll accept that a 
> perfectly trained Bayes DB may produce better results than any other 
> technology right now, and that a tech savvy user may generate such a 
> perfect Bayes DB. The point is that spam is a global problem- unless 
> your solution can be extended to all users, there is no point IMHO.

Global tools are also an invaluable asset to fighting spam.  We're
working on a magical blacklisting tool that will capture source ips from
incoming spam...when a threshhold is exceeded, all incoming messages
from that source ip are marked/learned as spam for all users (system
wide) for whatever time period we specify.  Mechanisms like this, along
with some newer ideas for networking dictionaries, I'm confident will
help remove much of the learning curve from Bayesian filters.

Note, however, that the learning process does not need to be
tech-savvy.  For example, we specifically sculpted our tool to be brain
dead easy for grandma.  You get your mail like normal, and if you get a
spam you forward it to grandma-spam@...rdomain.com.  There are even
tools such as SpamSource (for Outlook) that can make this process a
simple click of a button.  The signature mechanism we use stores the
original tokenset in binary format in a temporary database on the server
(or in the form of message attachments), which our tool will then use to
relearn the message as spam.

We're working now to try and find a better way to eliminate the need for
checking a quarantine.  This is unnecessary anywhere from 99.900%
(worst) to 99.99% (best) of the time, and even though there's a button
you just click called 'THIS IS NOT SPAM' it would be nice if we could
eliminate the need to check quarantine unless alerted to do so under
certain statistical conditions.

Anyhow, my point is, we're trying to improve the ease-of-use factor,
which is a big reason tools like SA are still useful...out-of-the-box
functionality...however that doesn't necessarily mean heuristics are not
obsolete from a scientific perspective.  I think we're getting to a
point where enough tools exist to make a deployment just as easy, and
hopefully if things continue at the rate they're going, companies like
yours that require this level of ease will be able to use Bayesian
solutions.

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