Efficient Spam Email Detection Model based on Dynamic Embedding with Deep Learning Classification

 

Salam Al-augby1,*, Zahraa Ch. Oleiwi 2, Hasanen Alyasiri1, Fahad Ghalib Abdulkadhim1

1Faculty of Computer Science and Mathematics, University of Kufa, Najaf, Iraq

2Faculty of Computer Science and Information Technology, University of Al-Qadisiyah, Dewaniyah, Iraq

Emails: salam.alaugby@uokufa.edu.iq ; zahraa.chaffat@qu.edu.iq; hasanen.alyasiri@uokufa.edu.iq; fahadg.abdulkadhim@uokufa.edu.iq

Text Box: Abstract One of the major concerns when transitioning emails is the potential influx of unsolicited and unwanted spam emails. These unwanted emails can clog inboxes, causing recipients to overlook important messages and opportunities. To ensure security and avoid the destructive and dangerous effect of these spam emails, machine learning and deep learning methods have been conducted to design spam detection models. In this work, a combination of embedding models and multi-layer artificial neural networks as deep learning classification models is utilized in order to introduce an approach to spam detection. The proposed classifier leverages the Bidirectional Encoder Representations from Transformers (BERT) model for word embedding, applied to the Enron-Spam dataset, offering a noteworthy technique for considerable spam detection. Experimental results demonstrate that the proposed spam detection model achieved a 99% recall rate for detecting spam emails. Notably, this model is a step forward in generality and improving the efficiency of spam detection. It presents a good attempt at presenting a solution for detecting spam emails and fake text within communication environments. Received: January 09, 2025 Revised: March 19, 2025 Accepted: May 23, 2025

 

Keywords: Spam email; BERT model; Embedding models; Deep learning