Spam email detection using a novel multilayer classification-based decision technique

Abstract

Because of the rapid advancement of technology over the last several years, the number of internet users is growing at an exponential rate, and as a result, email communication has become popular as a means of exchanging information over the internet. Sending data and communicating with peers via email is the most cost-effective method. These email services also cause problems for users by sending electronic junk mail, often known as spam mail. Spam email is a privacy concern that is linked to a slew of commercial and dangerous websites, causing phishing, virus distribution, and a slew of other problems. This study examines several aspects that have been used for email spam classification, as well as offering an overview of a handful of classifiers or algorithms that have been successfully evaluated, as well as exploratory data analysis. The proposed email spam classifier uses three parallel layers of machine learning and deep learning techniques, followed by a decision function to determine whether or not the emails are spam. During testing, it was found that the proposed classifier beats similar systems on the standard dataset with an accuracy of 98.4%.