SpamAssassin

Chapter 4. SpamAssassin as a Learning System

SpamAssassin provides many rules that have proven useful in distinguishing spam from non-spam messages, and these rules are updated at each new release. But SpamAssassin provides more than just generic rules; it has the capability of learning about your email environment and adapting its detection behavior to maximize its accuracy in that environment.

SpamAssassin includes two adaptive systems that can be used in concert: autowhitelisting and Bayesian filtering. This chapter discusses the principles, configuration, and operation of both systems.

Autowhitelisting

SpamAssassin’s autowhitelisting algorithm learns each sender’s history of sending spam or non-spam messages and modifies the spam score of their subsequent mailings on the basis of this history. The primary goal of autowhitelisting is to reduce false positives—to make it less likely that a non-spam message will be tagged as spam—by assuming that people who send you non-spam messages will not begin to spam you. It can also reduce false negatives if a spammer consistently sends email from the same email address, but this happens infrequently enough that autowhitelisting rarely has a significant effect on false negatives.

Principles

When autowhitelisting is enabled, SpamAssassin maintains a database keyed on message senders’ email addresses and the IP addresses of their nearest untrusted relay (if any). Each time a message from a given sender is received, the message’s spam score ...