Abstract

In a time of escalating digital threats, cybersecurity takes on paramount significance, as website phishing attacks pose considerable risks to individuals and organizations. This extensive survey delves into the ever-evolving realm of website phishing detection, with a specific spotlight on the pivotal role of machine learning-based preprocessing techniques in fortifying security measures. Research explores a wide spectrum of preprocessing methods, including URL analysis, content examination, and anomaly detection, all geared towards early-stage phishing detection. We also investigate the latest advancements in data feature extraction, dimensionality reduction, and data representation, all of which play a crucial part in the ongoing battle against website phishing. Through a methodical analysis of leading methodologies and illustrative case studies, this survey strives to provide a comprehensive understanding of how machine learningbased preprocessing enhances website phishing detection. Additionally, it examines the challenges, opportunities, and promising research directions within this domain. The insights presented here contribute to a knowledge base that serves as a valuable resource for security practitioners, researchers, and policymakers, helping them make informed decisions and implement effective countermeasures in the relentless fight against website phishing threats.