Quick Fix, an expeditious approach to diminish SPAM

In recent times e-mail is proved to be an eradicator application on Internet. Spam has traditionally been most visible threat and fashioning the major problem in the e-commerce society. Survey reports showed that the 60% of all mail messages are spam. Identifying and removal of spam from the email delivery systems allows end users to regain a useful means of communication. Spam filters offer a way to curb the problem. In this paper, we present learning based personalized anti spam filter. We discuss the architecture and present a comparative study of the proposed anti spam filtering technique. We use public and private email corpora from wide community of Internet users collected over several months for comparative study. Simulation results show that appropriate training of a personalized filter gives more adequate results than the schemas previously proposed

1. Introduction

With the rapid growth of the internet users, the electronic mail is becoming the most efficient, fastest and reliable medium of communication. Availability of bulk mailing software and lists of e-mail addresses harvested from millions of recipients at essentially no cost, has make the spam more harmful. Spam messages are extremely annoying for most users, as they clutter their mail boxes. It is really an easy and cheep way to send and receive the e-mail. Spammers bombard on the unknown e-mail addresses to advertise there every thing whether it is items for sale or offering to visit some adult material.