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Oracle Tips by Burleson 

Web Stalkers
Chapter 13 - Spam, Spam, Spam, Spam

Using Spam Control Programs

A spam filter that targets and rejects all e-mail from a list of known spammers also suffers from two major flaws.  The first is that the list will never be complete.  Spammers jump between ISPs and make hundreds of almost imperceptible changes to their e-mail addresses, effectively flooding a user’s inbox with messages from the same spammer with a new address.  The second way spammers thwart this type of filter is to actually break into another e-mail server and use that server to send their spam messages.  If the hi-jacked server is identified by the system as a known spammer, it is possible that future legitimate e-mails from this server will not make it through.

In addition to the word list and address filters, there are other methods employed by commercially available anti-spam programs.  These programs use various methods to determine what the user considers spam.  One approach is to use a statistical analysis scheme known as Bayesian Statistics.  The Bayesian approach is powerful because it promotes increasing accuracy based on historical data.

The user of the program identifies messages as spam and instructs the program to delete them.  The program essentially learns what is spam based on this input from the user, and it automatically places subsequent messages with similar characteristics in a special folder.  The user may view the contents of this special folder to ensure no legitimate e-mail has been mistakenly placed there.

Several anti-spam programs are listed below.  Many of these programs use a variety of the approaches described above.  Their inclusion is not an endorsement of accuracy, but merely a starting point for additional research.

  • SpamBayes (

  • SpamInspector (

  • SpamEater (

The above text is an excerpt from:

Web Stalkers
Protect yourself from Internet Criminals & Psychopaths
ISBN 0-97-45993-9-5

by Donald K. Burleson, Stephen Andert

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