Unsolicited emails, known as spam, are one of the fast growing and costly problems associated with the Internet today. Electronic mail is used daily by millions of people to communicate around the globe and is a mission-critical application for many businesses. Over the last decade, unsolicited bulk email has become a major problem for email users. An overwhelming amount of spam is flowing into user's mailboxes daily. Not only is spam frustrating for most email users, it strains the IT infrastructure of organizations and costs businesses billions of dollars in lost productivity. The necessity of effective spam filters increases. In this paper, we presented an efficient spam filter techniques to spam email based on Naive Bayes Classifier. Bayesian filtering works by evaluating the probability of different words appearing in legitimate and spam mails and then classifying them based on those probabilities.
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