Achieving a 98%+ spam detection rate using a mathematical approach

This white paper describes how Bayesian mathematics can be applied to the spam problem, resulting in an adaptive, ‘statistical intelligence’ technique that achieves very high spam detection rates.
It also explains why the Bayesian approach is the best way to tackle spam once and for all, as it overcomes the obstacles faced by more static technologies such as blacklist checking, comparing to databases of known spam and keyword checking. These technologies are not obsolete, but cannot be relied upon without a Bayesian filter.

Current spam detection techniques
Spam is an ever-increasing problem. The number of spam mails is increasing daily - studies show that over 50% of all current email is spam; the Radicati Group predicts this will reach 70% by 2007. Added to this, spammers are becoming more sophisticated and are constantly managing to outsmart 'static' methods of fighting spam.
The techniques currently used by most anti-spam software are static, meaning that it is fairly easy to evade by tweaking the message a little. To do this, spammers simply examine the latest anti-spam techniques and find ways how to dodge them.
To effectively combat spam, an adaptive new technique is needed. This method must be familiar with spammers' tactics as they change over time. It must also be able to adapt to the particular organization that it is protecting from spam. The answer lies in Bayesian mathematics. Read the full article: Bayesian Filtering

» posted by gillykramer

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