Branch did not disappoint.
Like to build things? August This article describes the spam-filtering techniques used in the spamproof web-based mail reader we built to exercise Arc.
An improved algorithm is described in Better Bayesian Filtering. I think it's possible to stop spam, and that content-based filters are the way to do it. The Achilles heel of the spammers is their message.
They can circumvent any other barrier you set up. They have so far, at least. But they have to deliver their message, whatever it is. If we can write software that recognizes their messages, there is no way they can get around that.
If you hired someone to read your mail and discard the spam, they would have little trouble doing it. How much do we have to do, short of AI, to automate this process? I think we will be able to solve the problem with fairly simple algorithms.
In fact, I've found that you can filter present-day spam acceptably well using nothing more than a Bayesian combination of the spam probabilities of individual words. Using a slightly tweaked as described below Bayesian filter, we now miss less than 5 per spams, with 0 false positives.
The statistical approach is not usually the first one people try when they write spam filters. Most hackers' first instinct is to try to write software that recognizes individual properties of spam. You look at spams and you think, the gall of these guys to try sending me mail that begins "Dear Friend" or has a subject line that's all uppercase and ends in eight exclamation points.
I can filter out that stuff with about one line of code. And so you do, and in the beginning it works.
A few simple rules will take a big bite out of your incoming spam. Merely looking for the word "click" will catch I spent about six months writing software that looked for individual spam features before I tried the statistical approach. What I found was that recognizing that last few percent of spams got very hard, and that as I made the filters stricter I got more false positives.
False positives are innocent emails that get mistakenly identified as spams. For most users, missing legitimate email is an order of magnitude worse than receiving spam, so a filter that yields false positives is like an acne cure that carries a risk of death to the patient.
The more spam a user gets, the less likely he'll be to notice one innocent mail sitting in his spam folder. And strangely enough, the better your spam filters get, the more dangerous false positives become, because when the filters are really good, users will be more likely to ignore everything they catch.
I don't know why I avoided trying the statistical approach for so long. I think it was because I got addicted to trying to identify spam features myself, as if I were playing some kind of competitive game with the spammers.
Nonhackers don't often realize this, but most hackers are very competitive. When I did try statistical analysis, I found immediately that it was much cleverer than I had been.
It discovered, of course, that terms like "virtumundo" and "teens" were good indicators of spam. But it also discovered that "per" and "FL" and "ff" are good indicators of spam.
In fact, "ff" html for bright red turns out to be as good an indicator of spam as any pornographic term. I start with one corpus of spam and one of nonspam mail.
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