Abstract

The internet faces a major security threat because of phishing attacks. Attackers establish deceptive websites which have identical appearances to authentic websites to obtain sensitive information including passwords and personal details and financial resources from victims. The attackers achieve their goal through the implementation of homograph URLs which use letters from multiple languages and symbols to create addresses that closely resemble authentic websites. The identification of fake URLs together with phishing websites stands as a critical measure to defend users from potential threats. The research study evaluates sixty recent academic papers which investigate multiple approaches to identify phishing attacks and homograph URLs. The discussion focuses on two primary methods which include URL-based approaches that examine website addresses and website-based approaches that evaluate website content and visual elements. The paper demonstrates how machine learning and deep learning models enhance detection precision through their implementation. This review includes popular datasets together with testing standards and evaluation metrics which demonstrate the testing procedures for method comparison. The research concludes by identifying essential obstacles which affect present methods and proposing new investigation paths. The review provides a basic yet thorough summary of phishing detection approaches to assist researchers and developers in selecting their most effective detection methods.