International Journal of Science, Engineering and TechnologyAuthors: Suryavanshi Aditi Krushnakant, Prof. S. R. Borhade, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: —Phishing attacks use fake emails, messages and websites to trick people into revealing passwords, banking details and other personal data, causing financial losses and privacy violations. Attackers continuously create new sites and adapt their techniques, so blocklists and static rules often fail against advanced and short lived phishing campaigns. This paper reviews phishing detection with emphasis on website and URL analysis, and places detection within a broader defence-in-depth strategy. It surveys feature-based approaches that analyse URL structure, domain information, page content and HTML elements, including the influential phishing website feature set of Mohammad et al., and machine learning classifiers such as Random Forest, Decision Tree, Support Vector Machine and Logistic Regression. Deep learning on raw URL strings and visual similarity methods that compare page screenshots and logos against legitimate brands are examined. Defence mechanisms beyond detection, including browser warnings, email authentication, phishing-resistant multi-factor authentication and user awareness training, are discussed. A layered detection and defence architecture is proposed, along with research gaps, objectives and a comparison of algorithms. The review concludes that combining automated multi view detection with resistant authentication and informed users provides the most effective protection against phishing.
DOI: https://doi.org/10.5281/zenodo.24216546
