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Posted
by
Margaret Rouse
A spam cocktail (or
anti-spam cocktail) is the use of several different technologies in combination
to successfully identify and minimize spam. The use of multiple mechanisms
increases the accuracy of spam identification and reduces the number of false
positives.
A spam cocktail (or anti-spam cocktail) is
the use of several different technologies in combination to successfully
identify and minimize spam.
The use of multiple mechanisms increases the accuracy of spam identification
and reduces the number of false positives.
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A spam cocktail puts each e-mail message
through a series of tests that provides a numeric score showing how likely the
message is to be spam. Scores are computed and the message is assigned a
probability rating. For example, it may be determined that a message has 85%
probability that it is spam. E-mail administrators can create rules that govern
how the messages are handled based on their scores; the highest scores may be
deleted, medium scores may quarantined, and lower scores may be delivered but
marked with a spam warning.
A spam cocktail commonly includes several
of the following identification methods, which may be weighted differently for
message scoring:
·
Machine
learning: Implementing sophisticated computer algorithms that improve over time
to analyze the subject line and contents of a message and predict the
probability that it is spam based on past results. The Bayesian filter
is a type of machine learning.
·
Blacklisting:
Subscribing to a blacklist or blackhole list
of known spammers and blocking messages from those sources
·
Content
filtering: Using programs that look for specific words or criteria in the
subject line of body of a message
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