In the digital era, phishing attacks pose a significant threat to online security, especially in areas such as e-banking, ecommerce, and information systems. This study focuses on enhancing the detection of phishing websites using advanced Machine Learning (ML) techniques. Phishing attacks typically involve deceiving users by mimicking legitimate websites to steal sensitive information such as usernames, passwords, and financial details. These attacks are often carried out through malicious URLs or cloned webpages that appear authentic to unsuspecting users. Accurately identifying such threats is critical, as phishing remains one of the leading causes of cybersecurity breaches. To address this, the proposed work utilizes supervised ML algorithms, including Random Forest and Decision Tree classifiers, based on extracted URL features such as lexical patterns, domain information, and host-based attributes. The system also integrates techniques for real-time URL analysis and domain verification. Experimental results demonstrate the effectiveness of the approach in accurately classifying phishing and legitimate websites, contributing to the development of intelligent cybersecurity solutions.
Mohammed Sharief (2025) studied this question.