Phishing Website Detection Using Machine Learning

Phishing websites have proven to be a major security concern.Several cyberattacks risk the confidentiality, integrity, and availability of company and consumer data, and phishing is the beginning point for many of them.Many researchers have spent decades creating unique approaches to automatically detect phishing websites.While cutting-edge solutions can deliver better results, they need a lot of manual feature engineering and aren't good at identifying new phishing attacks.As a result, finding strategies that can automatically detect phishing websites and quickly manage zero-day phishing attempts is an open challenge in this field.While existing systems have been effective in detecting phishing websites with often exceeding 90% in well-balanced datasets.However, the accuracy of these systems can be impacted by issues such as over-fitting, an imbalanced data-set, and the evolving nature of phishing attacks.So In this proposed system adresses the approache that By combining multi-modal feature analysis the system provides a more accurate solution for phishing website detection.The Project focuses on the detection of phishing websites using machine learning algorithms, with the goal of identifying the algorithm that yields the highest accuracy for phishing detection.Random Forest with 97% and Decision Tree Classifier with 94% are the most fitted algorithms.