Phishing Website Detection using Machine Learning

In today's digital world, phishing is a major danger that may result in significant financial losses and data breaches. By developing efficient methods to recognize phishing websites using cutting-edge machine learning, the project "Phishing Website Detection Using Machine Learning Techniques" seeks to address this pressing problem. Techniques. The project aims to give consumers a reliable tool for identifying fraudulent websites by looking at many aspects of URLs and web pages. This project properly classifies websites as either authentic or phishing by using a variety of machine learning methods, such as Random Forests, Decision Trees, and Logistic Regression. These techniques were chosen because they are good at deciphering intricate data patterns and provide accurate classifications based on characteristics taken from URLs. Additionally, to improve detection accuracy, feature extraction techniques are used to identify important signs of phishing activity. Creating an extensive collection of URLs from phishing and trustworthy sites is part of the "cat-phish" project technique. Important characteristics including domain age, URL length, and the existence of questionable keywords are retrieved. Several models for machine learning are created and assessed. following data preparation. finding the best algorithm by utilizing performance criteria like as recall, accuracy, precision, and F1 score. To assist customers, learn about potential risks, a user-friendly interface is also designed to offer real-time analysis.