AI-Based Phishing Website Detection Using Machine Learning

Phishing websites are a common cybersecurity threat that trick users by pretending to be legitimate websites andattempting to obtain sensitive information. Traditional methods, such as blocklists and rule-based techniques, maynot always detect newly created or modified phishing websites. Machine learning provides an effective approachto phishing detection by learning patterns from examples of legitimate and phishing websites.This paper presents a conceptual framework for AI-based phishing website detection using machine learning. Theframework includes collecting phishing and legitimate website data, extracting important URL and websitefeatures, preprocessing the data, selecting relevant features, applying machine learning classification, andevaluating the detection performance. Recent research on feature engineering, feature selection, machine learning,deep learning, XGBoost-based detection, and phishing campaign detection is also reviewed. The proposedframework aims to provide a simple and structured approach for distinguishing phishing websites from legitimatewebsites while considering the changing nature of phishing attacks. Since this study is research and visualizationoriented, the proposed system is presented as a conceptual framework rather than a developed application ortrained software model