Spam Mails Filtering Using Different Classifiers with Feature Selection and Reduction Techniques

The continuous growth of email users has resulted in the increasing of unsolicited emails also known as Spam. In current, server side and client side anti spam filters are introduced for detecting different features of spam emails. However, recently spammers introduced some effective tricks consisting of embedding spam contents into digital image, pdf and doc as attachment which can make ineffective to current techniques that is based on analysis digital text in the body and subject fields of email. Many of proposed working strategy provides an anti spam filtering approach that is based on data mining techniques which classify the spam and ham emails. The effectiveness of these approaches is evaluated on large corpus of simple text dataset as well as text embedded image dataset. But most of the filtering techniques are unable to handle frequent changing scenario of spam mails adopted by the spammers over the time. Therefore improved spam control algorithms or enhancing the efficiency of various existing data mining algorithms to its fullest extent are the utmost requirement. A comparative study is presented on various spam filtering techniques adopted on the basis of various attributes to find best among all to extract the best results.