A Comprehensive Review of Long Short-term Memory Network for Email Spam Detection

Abstract

Email spam filtering is the process of detecting and preventing spam messages from making their way into users' inboxes while allowing valid email to be delivered. Out of various strategies used for spam detection, Long Short-Term Memory (LSTM) is possibly one of the most effective methods due to its ability to handle sequential data and model long-term temporal patterns. This paper aims to explore the state of the art in LSTM networks for email spam detection and present a systematic approach to their use. Using LSTM networks in spam detection has many advantages over traditional spam detection. They can evaluate the semantic context of e-mail content and subject lines much better, which makes them extremely useful for spam detection. Moreover, they can adapt to new types of spam as they occur,  keeping them relevant and useful in changing environments. However, despite their benefits, LSTM networks face challenges with computational complexity, which needs to be addressed for better performance when training and deploying them. In the future, we may combine LSTM networks with other deep learning methods, for instance, Convolutional Neural Networks (CNNs), to enhance their ability to extract more durable features from email. Such a hybrid methodology can improve spam detection systems' ability to detect spam and their effectiveness as well as accuracy. By addressing these problems and exploring new approaches, this study aims to improve current research and application of email spam detection and strengthen security solutions in the area.