Spam Filtering

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Approaches

Manual Classification Hand-Coded Rules Machine Learning

Hand-Coded Rules

For example http://spamassassin.apache.org/tests_3_3_x.html “Dear Friend, …!!!!!!”

Machine Learning

Paul Graham: A Plan for Spam http://www.paulgraham.com/spam.html

Machine Learning

Advantages You don’t have to read so many spams to

recognize spam features. You know what you are measuring: probabilities

instead of mysterious scores. It automatically discovers spam features that you

may have neglected. ff0000

It considers all the evidence in the email: both good and bad.

Machine Learning

Advantages It is personalized: each user has his own spam

filter based on the actual mails he receives Pharmacy

It is evolving with the spam. Viagra => V1agra

Machine Learning

For example http://www.google.com/mail/help/fightspam/spamexplained.html

When the Gmail community votes with their clicks to report a particular email as spam, our system quickly learns to start blocking similar messages.

And machine-learning algorithms developed to merge and rank large sets of Google search results allow us to combine hundreds of factors to classify spam.

Machine Learning

For example

Machine Learning

Beyond Spam Filtering Automatic Email Classification

http://getpopfile.org/

Machine Learning

Beyond Spam Filtering Smart Labels

http://mail.google.com/mail/help/priority-inbox.html

Machine Learning

Beyond Spam Filtering Priority Inbox

http://mail.google.com/mail/help/priority-inbox.html