Abstract
Email spam is a significant issue confronting both email consumers and providers. The evolution of spam filtering has progressed considerably, transitioning from basic rule-based filters to more sophisticated machine learning algorithms. Deep learning has become a potent collection of techniques for addressing intricate issues such as spam classification in recent times. A thorough literature evaluation is required to have a comprehensive overview of the current research on utilizing deep learning methods for email spam classification. This review aims to identify the various deep learning techniques used for email spam, their effectiveness, and areas for future research. By synthesizing the outcomes of pertinent studies, this review delineates the strengths and drawbacks of various approaches, offering valuable insights into the challenges that must be tackled to enhance the precision and efficacy of email spam classification.
Recommended Citation
Tusher, Ekramul Haque; Ismail, Mohd Arfian; and Mat Raffei, Anis Farihan
(2025)
"Email Spam Classification Based on Deep Learning Methods: A Review,"
Iraqi Journal for Computer Science and Mathematics: Vol. 6:
Iss.
1, Article 2.
DOI: https://doi.org/10.52866/2788-7421.1236
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/2
