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Bayesian Statistical Analysis
Bayesian statistical analysis is a method of spam filtering, whereby algorithms identify and quantify the possibility that an email message is spam. The user trains a Bayesian filter by marking emails as spam, which the filter analyses and uses to alter its behaviour. It will then mark emails as spam which contain spammy
words, and will allow emails which contain terms associated with personal emails. For example, the words Viagra
and refinance
are common in unsolicited emails, but words such as the names of friends and family are more common in personal emails.
The benefit of Bayesian filtering is that it can be tailored to the particular user's purposes. For example, they may have subscribed to a newsletter which other spam filters mark as spam, but because the user decides what to mark as spam and what to accept, the Bayesian filter can be trained not to treat the newsletter as junk mail.
Spammers attempt to dodge Bayesian filters by using alternative characters or spacing so that the strings marked as dangerous by the filter do not appear in their emails. For example, they may write refinance
as r3fin4nce
. They might also add random, innocuous words to the content of their emails, in an attempt to slip past Bayesian filters.
Spam - Spam Bayesian Statistical Analysis Heuristics Rules-Based Analysis Distributed Checksum Clearinghouses Black and White Lists HTML Shield Technology Allow List Deny Delivery Junk Mail
Tag Subject Prepend Spam Virus Worm Phishing
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