Machine Learning Approaches For Email Spam Detection: A Comprehensive Review

Abstract

Email communication is an essential part of modern digital interactions; however, the increasing volume of spam emails poses significant security and efficiency challenges. Traditional rule-based spam detection methods struggle to keep up with the evolving tactics used by spammers. In recent years, machine learning (ML) approaches have emerged as powerful tools for accurately classifying spam emails by learning patterns from large datasets. This review provides a comprehensive analysis of various ML techniques used for email spam detection, including supervised, unsupervised, and deep learning models. We discuss popular algorithms such as Naïve Bayes, Support Vector Machines, Decision Trees, Random Forest, and advanced deep learning architectures like artificial neural networks (ANNS) and transformers. Additionally, we explore feature engineering techniques, dataset challenges, evaluation metrics, and recent advancements in hybrid models. The study also highlights the advantages and limitations of different ML approaches and presents future research directions to enhance spam classification accuracy and efficiency. This review serves as a valuable resource for researchers and practitioners seeking insights into the latest developments in machine learning-based spam detection.

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