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Bayesian Filter - Fighting Spam with Keyword Technology

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Bayesian Filter - Fighting Spam with Keyword Technology Powered By Docstoc
					Not a long time ago, most anti-spam products simply used a list of keywords to
identify spam. A good set of keywords could catch much spam. However, a
keyword-based anti-spam filter requires manual updating and can be easily fooled by
tweaking the message a little. Spammers simply examine the latest anti-spam
techniques and find ways to bypass them. At the result you're left with a high number
of false positives.
  The need in a new effective technique to fight against spam stood up. The experience
showed that this new method might adapt itself to the spammers' tactics that would
change with time.
  The Bayesian filtering is based on the principle that most events are dependent and
that the probability of an event occurring in the future can be inferred from the
occurrences of this event in the past. This approach is used to identify spam. If some
piece of text occurred mostly in spam emails but not in legitimate mail, then it would
be reasonable to suppose that this email is probably spam.
  To filter mail using the Bayesian technology, you need to generate a database of
words collected from spam and legitimate mail. Then a probability value is assigned
to each word; the probability is based on the calculations that take into account how
often that word occurs in spam as opposed to legitimate mail.
  After the legitimate and spam databases are created during an initial training period,
the word probabilities can be calculated and the Bayesian filter is ready for use. When
a new mail arrives, it is broken into words and the most significant words are singled
out. From these words, the Bayesian filter calculates the probability of a new message
being spam or not. If the probability is greater than a spam threshold, say 0.9, the
message is classified as spam.
  Tip! G-Lock SpamCombat allows you assign the hot keys to the common operations.
For example, you can assign F8 to Mark Message as SPAM function and F9 to Mark
Message as Clean. Next time when you train the Bayesian filter you can simply use
two keys on your keyboard F8 and F9.
  It is important to note that the analysis of spam and legitimate mail is performed on
the mail the particular user (organization, company, etc.) receives, and therefore the
Bayesian filter is adjusted to this particular person, company, or organization. For
example, a financial institution may receive a lot of emails with the "mortgage" word
and would get a lot of false positives if using an outdated anti-spam filter. The
Bayesian filter analyzes the entire message with the word "mortgage", and concludes
whether this email is spam or legitimate basing NOT only on a single keyword
"mortgage". The Bayesian approach to filter spam is highly effective - spam detection
rates of over 99.7% can be achieved with a very low number of false positives!
  Let's summarize what benefits we get using the Bayesian filter to catch spam:
  1) Much more intelligent approach because it examines all aspects of a message, as
opposed to keyword checking that classifies a mail as spam on the basis of a single
word.
  2) Self-adapting - constantly learning from new spam and new valid inbound mails,
the Bayesian filter evolves and adapts to new spam techniques.
  3) Sensitive to the user - it learns the email habits of the company and understands
that, for example, the emails with the "mortgage" word are not always spam.
 4) Multi-lingual and international - being adaptive it can be used for any language.
The Bayesian filter also takes into account certain languages deviations or the diverse
usage of certain words in different areas, even if the same language is spoken.
 5) Difficult to fool, as opposed to a keyword filter - an advanced spammer who
wants to trick the Bayesian filter can either use fewer words that usually indicate
spam, or more words that generally indicate valid mail (such as a valid contact name,
etc). Doing the latter is impossible because the spammer would have to know the
email profile of each recipient - and a spammer can never hope to gather this kind of
information from every intended recipient.
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