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.
- It is impossible for a message to be both extremely clean and extremely dirty at the same time.
- Spam filters are not 100% effective at avoiding false positives because some legitimate email messages are written such that they resemble spam.
- 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.
- 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.
- The precision of anti-spam pattern matching techniques are inversely proportional to their risk.