A model for detection, classification and identification of spam mails using decision tree algorithm

Abstract

Spam mails are unsolicited bulk mails which are meant to fulfill some malicious purpose of the sender. They may cause economical, emotional and time losses to the recipients. Hence there is a need to understand their characteristics and distinguish them from normal in box mails. Decision tree classifier has been trained with the major characteristics of spam mails and results obtained with more then 86.7437% accuracy. This classifier can be a valuable strategy for software developers who are trying to combat this ever growing problem. © 2011 Springer-Verlag.