An Innovative URL-Based System Approach with ML Based Prevention

This paper delves into the pervasive threat of phishing in the e-commerce sector, where adversaries impersonate financial institutions to perpetrate identity theft. The modus operandi involves sending deceptive emails urging users to click on fraudulent links, leading them to fake websites mimicking authentic ones. Focused on URL-based detection, the study employs sophisticated algorithms and machine learning techniques, utilizing a dataset for training and testing to identify phishing URLs. The paper outlines various types of phishing attacks, including deceptive, spell, clone, whale, link manipulation, and voice phishing. Prevention strategies, such as spam avoidance, secure communication, and user education, are discussed. The methodology involves frontend and backend development using Python, JavaScript, HTML, CSS, PHP, and MySQL, incorporating machine learning for analysis. The website provides a user-friendly interface with a reporting option, contributing to a robust defense against phishing threats. Results indicate the effectiveness of URL-based detection, emphasizing the importance of staying updated on evolving phishing trends and employing a multifaceted approach for enhanced organizational cybersecurity.