In the below post on the Spam Curve, I explained a little bit about what the curve is and the nature of what it represents.  In the next series of posts, I will outline what I call “The Spam Theorems.”

The Spam Theorems are my own logical conclusions that we can hopefully infer from the curve.  In the next week or so I will be going over each the following theorems.  Note that this list may be subject to change while I iron out my views.

Spam Theorems

  1. It is impossible for a message to be both extremely clean and extremely dirty at the same time.
  2. Spam filters are not 100% effective at avoiding false positives because some legitimate email messages are written such that they resemble spam.
  3. Corollary to Theorem 2: spam filters are not 100% effective at catching spam because some spam can contain content that is routinely found in legitimate messages.
  4. Improvement in spam filtering effectiveness is achieved by improving the detection of the granularity of the "overlap" area in the bottom line of the Spam Curve.
  5. The precision of anti-spam pattern matching techniques are inversely proportional to their risk.