Phishing URL Detection using Machine Learning

Phishing, as a modern form of cybercrime, has emerge Phishing attacks remain a pervasive and insidious threat to internet security, exploiting human vulnerabilities and technical weaknesses to deceive users into divulging sensitive information, compromising confidentiality, integrity, and availability. The consequences of such attacks can be devastating, resulting in financial losses, reputational damage, and compromised data. To effectively combat this menace, we propose a robust and intelligent phishing detection model utilizing the Random Forest algorithm, a potent ensemble learning technique. Our primary objective is to harness the strengths of this algorithm to develop a scalable, adaptable, and highly accurate solution that enhances cybersecurity measures, accurately distinguishing between malicious and legitimate URLs. By conducting a comprehensive evaluation of the model's performance using key metrics such as accuracy, precision, recall, and F1-score, we demonstrate its efficacy in detecting phishing threats and identify areas for further improvement. This work contributes to the ongoing efforts to improve URL classification, protect users from phishing attacks, and fortify internet security, providing a valuable addition to the existing body of research in this field.