A Survey on Various Machine Learning and Deep Learning Algorithms used for Classification of Spam and Non-Spam Emails

Abstract

An email client receives emails from different websites, portals and domains, which can be an advertisement. Receiving a bulk amount of emails can cause serious damages like suspension of a particular email id.Mostly an email client gets exposed to the number of malicious receipts by registering an email account to a web portal, which in turn sends a bulk amount of emails. The email client wants to be decisive about differentiating the useful emails and spam emails. One of the solutions to escape from spam emails is to develop a decision based system which can classify the spam and non-spam emails. This survey gives an overview about different machine learning and deep learning algorithms to classify the spam and non-spam emails by accessing the received emails of an email client. The machine learning approaches and mechanisms like support vector machine, naive Bayesian classifier, artificial neural networks and logistic regression can be of important help to determine spam emails. These approaches use decision trees to run tests on a given sets of data (emails). After classifying a spam email source a user can navigate, block and report the source of the spam email generator. Most of the times the spam emails are generated by the autonomous sources which are called spam-bots.