Performance Evaluation of LSTM and RNN Models in the Detection of Email Spam Messages

Abstract

Email spam is an unwanted bulk message that is sent to a recipient’s email address without explicit consent from the recipient. This is usually considered a means of advertising and maximizing profit, especially with the increase in the usage of the internet for social networking, but can also be very frustrating and annoying to the recipients of these messages. Recent research has shown that about 14.7 billion spam messages are sent out every single day of which more than 45% of these messages are promotional sales content that the recipient did not specifically opt-in. This has gotten the attention of many researchers in the area of natural language processing. In this paper, we used the Long Short-Time Memory (LSTM) for classification tasks between spam and Ham messages. The performance of LSTM is compared with that of a Recurrent Neural Network( RNN) which can also be used for a classification task of this nature but suffers from short-time memory and tends to leave out important information from earlier time steps to later ones in terms of prediction. The evaluation of the result shows that LSTM achieved 97% accuracy with both Adams and RMSprop optimizers compared to RNN with an accuracy of 94% with RMSprop and 87% accuracy with Adams optimizer.

Detecting Spam E-mails with Content and Weight-based Binomial Logistic Model

“…The Phish responder method with LSTM and multi-layer perceptron was suggested in [18], which on the Spambase dataset gained 99 and 94% accuracies respectively. Again on the same dataset, 97% accuracy was attained by Adam and RMS using prop-optimized LSTM [19]. Jilani and Sultana tried to classify emails as spam or ham based on Uniform Resource Locators (URLs) in the body of the message [20].…”