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Machine Learning for Phishing Detection by 2303a52037 is a document available to read on EtoBox.

This document presents a machine learning-based approach for detecting phishing websites by analyzing URL structures, domain features, and webpage behavior. The study evaluates models such as Random Forest, Support Vector Machine (SVM), and Gradient Boosting, with Random Forest achieving the highest accuracy of 96%. The proposed system aims to enhance online security by providing automated phishing detection, addressing the limitations of traditional detection methods.

Author

2303a52037

Language

EN