MapReduce mRMR: Random Forests-Based Email Spam Classification in Distributed Environment

Abstract

The furthermost standard message transfer system used on the internet for communication is email. These days spam is a serious concern that causes major problems in today’s internet. Spam emails are uninhibited messages that are sent to a large number of beneficiaries arbitrarily. Owing to an overgrowing rise in reputation, the number of unsolicited data has also increased promptly and has led to many security concerns. Although the sufficient number of spam filtering techniques exists, nowadays spammers start discovering innovative practices to escape data that are filtered using the spam filters. Spammers use this communication source for spreading the malware in the name of an executable file. These spam emails waste user’s system memory, computing power, and bandwidth of the network. Spam emails have been initiated to progressively damage the integrity of email and destroy the online experience. The research revealed that if the classification algorithms are used with feature selection then that will return the exact results than the standard classification. In this paper, feature selection is done through minimum redundancy and maximum relevance (mRMR) and the classification is done by means of Random Forests in the MapReduce environment. The performance is compared using various measures, namely sensitivity, correctness, and accuracy with the Random Forests in the distributed environment using Spambase dataset.