Phishing URL Detection Using Machine Learning

Phishing is a common cyber-attack where attackers create fake websites to trick users into revealing sensitive information such as passwords, credit card numbers, and personal details. Detecting phishing URLs is important for protecting users and reducing the risk of identity theft and fraud. This project focuses on the use of machine learning techniques to help identify phishing URLs by analysing different characteristics of the URLs. The aim is to understand how these methods can learn from data and recognize patterns that distinguish phishing URLs from legitimate ones. The study involves gathering datasets of URLs, extracting features that describe their structure and content, and building models that can classify URLs accordingly. This research does not assume any particular machine learning approach but explores various possibilities to find effective solutions. The goal is to investigate how machine learning can contribute to automated and efficient phishing detection, which could be useful in improving online security tools such as browsers and email filters.