Phishing Website Detection using Machine Learning

Phishing attacks represent a prevalent and serious type of cybercrime, in which malicious actors mimic reputable entities to trick individuals into disclosing confidential details such as usernames, passwords, and credit card information. These deceptive tactics frequently involve authentic-looking websites, underscoring the importance of remaining alert. It is imperative for both individuals and organizations to adopt more advanced and creative detection methods in order to efficiently combat these security risks. The goal of this project is to create a machine learning-based system for detecting phishing websites effectively. We will use machine learning techniques to analyze extensive data about both phishing and genuine websites, extracting important features like URL types, webpage content, and metadata to train different machine learning models. Our main emphasis will be on the Gradient Boosting Classifier, a strong ensemble learning technique recognized for its accuracy and resilience. After conducting thorough tests and analysis, our goal is to assess how well the Gradient Boosting Classifier can identify phishing websites. Our aims include minimizing incorrect identifications, improving accuracy in detection, and speeding up the overall response time of the detection system. This project aims to combine sophisticated machine learning algorithms with phishing detection to have a substantial impact on cybersecurity, offering users a dependable tool for defending against phishing attacks.