"419 is just a game, you are the losers, we are the winners.
White people are greedy, I can say they are greedy
White men, I will eat your dollars, will take your money and disappear.
419 is just a game, we are the masters, you are the losers."
[Samuel] sent 500 e-mails a day and usually received about seven replies. Shepherd would then take over. "When you get a reply, it's 70% sure that you'll get the money," Samuel said.
Spam only became a problem for me about a year and a half ago, but clearly it's here to stay. I've used POPFile for about a year to cut down on my email spam**. Some people swear by challenge-response human verification systems such as SpamArrest, but as Scott Mitchell notes, this system has some issues:
While the challenge/response system was effective in reducing my spam intake from about 100 messages a day to around 1 or 2 messages a day, the approach, in my estimation, was not ideal. One big disadvantage was that fewer people took the time to respond to the challenge email than I had anticipated, for two reasons:
- Some people don't want to take the time to follow instructions for a challenge email. Maybe their message wasn't that important after all, maybe they're busy, or maybe they just don't like being told what to do. These people's messages, I reckoned, weren't that vital. If you can't take two seconds to respond to the challenge, then just how important is that email you're sending me?
- What worried me most, and led me to suspend my C/R anti-spam system, is that I noticed some people weren't responding to the challenge email because they never received it! This unfortunate circumstance could happen if their own spam blocking solution halted my challenge email. A couple folks informed me that Outlook 2003 categorized my challenge emails as spam. Others using a similar challenge/response anti-spam system would never get my challenge as my challenge would generate a challenge on their side.
The "I challenge your challenge!" scenario is particularly amusing. And on top of the two issues Scott highlights, there are other social problems with challenge/response spam blocking.
Although I've had great success with POPFile, which uses Bayesian filtering techniques, I had no idea that there's an even better technique: Markovian filtering. That's what the CRM114 Discriminator* uses. There's an outstanding slide deck (pdf) that explains how it all works. In a nutshell, Markovian filtering weights phrases and words, whereas Bayesian filtering only looks at individual words. How much better is it? I'll let the CRM114 author, Bill Yerazunis, pitch it:
For the month of April 2005, I receieved over 10,000 emails. About 60% were spam. I had ZERO classification errors. ZERO.
As of Feb 1 through March 1, 2004, 8738 messages (4240 spam, 4498 nonspam), and my total error rate was ONE. That translates to better than 99.984% accuracy, which is over ten times more accurate than human accuracy
I measured my own accuracy to be around 99.84%, by classifying the same set of about 3000 messages twice over a period of about a week, reading each message from the top until I feel "confident" of the message status, (one message per screen unless I want more than one screen to decide on a message.) and doing the classification in small batches with plenty of breaks and other office tasks to avoid fatigue. Then I diff()ed the two passes to generate a result. Assuming I never duplicate the same mistake, I, as an unassisted human, under nearly optimal conditions, am 99.84% accurate.
Most Bayesian techniques top out at around ~98% percent accuracy with a little training, but Markovian can achieve a rarified 99.5% accuracy. The most notable Windows port of CRM114 is SpamRIP.
* A reference to the movie Dr. Strangelove. In the movie, the "CRM114 Discriminator" is a fictional accessory for a radio receiver that's "designed not to receive at all", that is, unless the message is properly authenticated.
** I have since switched to K9 because it's simpler and faster-- and does the same Bayesian filtering.
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