A Model for Fuzzy Logic Based Machine Learning Approach for Spam Filtering

Abstract

It is definitely impossible to say, who was that first person to come up with the simple idea of sending out a public announcement to millions of people, and then at least one person will react to it no matter what is the proposal. E-mail provides a perfect way to send these millions of advertisements without any for a sender, and this fortunate fact is nowadays extensively exploited by several organizations. As a result, the e-mail boxes of millions of people get cluttered with all these so-called Unsolicited Bulk E-mail (UBE) also known as or junk mail. E-mail is a subset of electronic spam involving nearly identical messages sent to numerous recipients through e-mail. Definitions of spam usually include the aspects that e-mail is unsolicited and sent in bulk. Another subset of UBE is UCE (Unsolicited Commercial E-mail). The opposite of spam, e- mail which one wants, is called ham, usually when referring to a message's automated analysis (such as Bayesian filtering). Machine learning techniques now days are used to automatically filter the spam e-mail in a very successful and efficient way. In this paper we consider some of the machine learning methods such as Naive Bayes, Artificial Neural Networks, Artificial Immune System Classifier methods, and fuzzy logic.