Abstract

Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. The economics of spam details that the spammer has to target several recipients with identical and similar email messages. As a result a dynamic knowledge sharing effective defense against a substantial fraction of spam has to be designed which can alternate the burdens of frequent training stand alone spam filter. A weighted email attribute based classification is proposed to mainly focus to encounter the issues in normal email system. These type of classification helps to formulate an effective utilization of our email system by combining the concepts of Bayesian Spam Filtering Algorithm, Iterative Dichotmiser 3(ID3) Algorithm and Bloom Filter. The details captured by the system are processed to track the original sender causing disturbances and prefer them to block further mails from them. We have tested the effectiveness of our scheme by collecting offline data from Yahoo mail & Gmail dumps. This proposal is implemented using .net and sample user-Id for knowledge base.