Spam Mail Filtering: A Survey of Effective Spam Mail Detection Techniques

With the continuous growth of email users has resulted in the increase of unsolicited emails also known as Spam. E-mail spam, known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted e-mail messages, frequently with commercial content, which is normally in large quantities to set of recipients. There are many spam filters available that uses various approaches to identify the incoming message as spam or ham, ranging from white list / black list, Bayesian analysis, keyword matching, mail header analysis, postage, legislation, and content scanning etc. but we are still flooded with spam emails every day. This is not because the filters are not powerful enough, it is due to the swift adoption of new techniques by the spammers and the inflexibility of spam filters to adapt the changes. In our work, we employed supervised machine learning techniques to filter the email spam messages. Widely used supervised machine learning techniques namely C 4.5 Decision tree classifier, Multilayer Perceptron, Naive Bayes Classifier are used for learning the features of spam emails and the model is built by training with known spam emails and legitimate emails. The results of the models are discussed. In current, server side and client side anti-spam filters are introduced for detecting different features of spam emails. However, recently spammers introduced some new 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.