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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.
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