Email spam: A comprehensive review of optimize detection methods, challenges, and open research problems

• Main Authors: Tusher, Ekramul Haque, Mohd Arfian, Ismail, Rahman, Md Arafatur, Alenezi, Ali H., Uddin, Mueen
• Format: Article
• Language: English; English; English
• Published: IEEE 2024
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://umpir.ump.edu.my/id/eprint/42645/1/Email%20spam-A%20comprehensive%20review%20of%20optimize%20detection%20methods_ABST.pdf; http://umpir.ump.edu.my/id/eprint/42645/2/Email%20spam-A%20comprehensive%20review%20of%20optimize%20detection%20methods.pdf; http://umpir.ump.edu.my/id/eprint/42645/13/Email_Spam_A_Comprehensive_Review_of_Optimize_Detection_Methods_Challenges_and_Open_Research_Problems.pdf; http://umpir.ump.edu.my/id/eprint/42645/; https://doi.org/10.1109/ACCESS.2024.3467996; https://doi.org/10.1109/ACCESS.2024.3467996

Nowadays, emails are used across almost every field, spanning from business to education. Broadly, emails can be categorized as either ham or spam. Email spam, also known as junk emails or unwanted emails, can harm users by wasting time and computing resources, along with stealing valuable information. The volume of spam emails is rising rapidly day by day. Detecting and filtering spam presents significant and complex challenges for email systems. Traditional identification techniques like blacklists, real-time blackhole listing, and content-based methods have limitations. These limitations have led to the advancement of more sophisticated machine learning (ML) and deep learning (DL) methods for enhanced spam detection accuracy. In recent years, considerable attention has focused on the potential of ML and DL methods to improve email spam detection. A comprehensive literature review is therefore imperative for developing an updated, evidence-based understanding of contemporary research on employing these ethods against this persistent problem. The review aims to systematically identify various ML and DL methods applied for spam detection, evaluate their effectiveness, and highlight promising future research directions considering gaps. By combining and analyzing findings across studies, it will obtain the strengths and weaknesses of existing methods. This review seeks to advance knowledge on reliable and efficient integration of state-of-the-art ML and DL into identifying email spam.