The Phisherman’s Gambit

Phishing remains a pervasive threat in the digital landscape, posing significant risks to individuals and organizations alike. In response, this paper presents a sophisticated approach to phishing website detection utilizing machine learning methodologies. Through the amalgamation of diverse features extracted from URLs, domain attributes, and webpage content, a robust classification framework is constructed. Employing an array of supervised learning algorithms, including decision trees, support vector machines, and neural networks, our proposed system demonstrates its efficacy in accurately discerning phishing websites with notable precision and recall rates. Moreover, feature engineering and selection techniques are implemented to optimize model performance and computational efficiency. Experimental evaluations conducted on comprehensive datasets validate the effectiveness of the proposed approach in proactively identifying and mitigating phishing threats, thereby fortifying cybersecurity measures for users and organizations in the digital realm.