Email Anti-Phishing: Machine Learning Models and Evaluation Overview

Abstract

Phishing attacks have grown to be one of the most visible and challenging issues confronting internet users, organizations, and governments. To effectively combat phishing attacks, it is imperative to have robust machine learning models for email anti-phishing systems. These models play a crucial role in analyzing email content, sender behavior, and other relevant features to identify and block potential phishing emails. To make sure these machine learning models work well in real-world scenarios, it is crucial to evaluate their performance. This paper has reviewed machine learning anti-phishing solutions through a systematic literature review considering the integration of diverse machine learning techniques, including ensemble models, coupled with advanced evaluation methodologies. This review concludes that Email security has improved significantly with the application of machine learning to counter phishing attempts. Also, the incorporation of machine learning models into anti-phishing tactics has resulted in the creation of resilient defenses against the ever-growing sophistication of cyber threats.