What are Adaptive Filters

Adaptive filters learn from the environment as time passes by. This learning ability improves the junk mail filtering functionality in Xeams.
There are two types of adaptive filters in Xeams
  1. Bayesian Analysis
  2. Auto-learn sender

Bayesian Analysis

Bayesian spam filters calculate the probability of a message being spam based on its contents. Unlike simple content-based filters, Bayesian spam filtering learns from spam and from good mail, resulting in a very robust, adapting and efficient anti-spam approach that, best of all, returns hardly any false positives. Click here to learn more about the Bayesian Filter and tips on how to improve it for your needs.

Auto-learn sender

This filter is used when you use Xeams to handle out-bound emails. Xeams learns and remembers email addresses on the Internet that receive emails from your local users. To understand this filter, consider the following scenario.

  • Mark, who is a user in your company sends an email to Mary who is a user on the Internet
  • You have configured Xeams to accept out-bound messages
  • Since Mark is a local user, Xeams will allow his message to go through and will also remember that Mark has sent an email to Mary.
  • When Mary replies back to Mark, the system gives credit to Mary's message hence avoiding a false-positive

Scoring for Auto-Learn Sender

Considering the above example, Mary is going to get a credit of 25 points by default for every message generated by Mark. For example, if Mark has sent 4 emails to Mary in the past, incoming messages from Mary will get a -100 score.

Follow the steps below to change the default score of 25 to a higher/lower values.
  • Locate server.properties file in $INSTALL_DIR\config folder. Create a new text file if this file is missing. Ensure the name of the this file is server.properties and not service.properties or server.properties.txt
  • Add the following line:
  • This will change the default value from 25 to 50. Once this is done every email sent by Mary will get a score of -200 (50 * -4).
  • Save the file and restart Xeams