Classification and Prediction of Spam Emails Based on AI Enabling Models Using Deep and Machine Learning Techniques

Abstract

The increasing volume of unwanted/unsolicited bulk emails, also known as “SPAM,” is a devastating issue that provokes a multitude of problems in communication systems. Over the past few years, the work on spam classification has been tremendously enhanced to a greater extent. In this paper, we present an approach that encompasses machine and deep neural networks such as Gaussian Naive Bayes (GNB), Convolution Neural Networks (CNN) network, Long Short Term Memory (LSTM) network, and a customized model developed with the combination of CNN and LSTM to classify and predict the widely used open source spam assassin dataset that contains around 6000 real email samples. The models are trained and tested, and the results are presented in the paper. Overall, CNN-LSTM attained a prediction score of 98.68% on the spam dataset.