Glossary
Our online glossary of terms allows you to understand our email and internet specific jargon. If there is
any term that you are not familiar with and would like added to the glossary please contact support with
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Honeypots
Honeypots use decoy email accounts designed to attract spam.
One vendor (Brightmail) has a
spam attack analysis center staffed 24 hours a day by email experts. When a new spam
attack is launched, Brightmail picks it up through its hundreds of thousands of email
addresses placed at strategic domains across the Internet. All messages that land in
the decoy e-mail accounts are considered by Brightmail to be spam. The company uses
the network of accounts to detect developing spam attacks and to create filter rules
that its customers can use to block the spam from their own messaging servers.
Heuristics
Heuristic, or self-learning, techniques are commonly applied to spam filtering.
Heuristic filters sift through e-mail messages for the characteristics and behaviors that
are unique to spam messages, and - as they "learn" about new approaches - get better with
experience. One heuristic approach is sieve filtering. Once a spam message is identified,
the anti spam vendor uses an algorithm to calculate a unique string of bits, or "signature,"
for the spam message (including information buried in the e-mail message header that
is invisible to most e-mail recipients, such as the path the e-mail took to reach its
destination); the filter uses that signature to scan new incoming messages. Another heuristic
filtering approach is Bayesian analysis, in which large volumes of spam and an equal amount of
legitimate e-mail undergo sophisticated statistical analysis. A comparison of the results creates a
baseline threshold against which newly arriving messages are judged; proponents of this approach
claim extremely high (up to 99 percent-plus) success rates for the approach. Many spam filtering
programs use heuristic programming, including
ActiveState's PureMessage,
Mirapoint's MessageDirector,
Lyris MailShield Server
and the open source program SpamAssasin.
Header Analysis
Most anti-spam products examine headers, looking for such items as the validity of the
sender's address, whether the same information is found in the "sender" and "from"
fields of an email, and whether a specific message contains information not
common to "normal" email. Furthermore, bulk mail programs often insert tracking
methods within headers that can be instantly identified as SPAM.
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