Phishing Attack Detection Using Machine Learning Approaches and its Legal Landscape

Phishing attacks, where cybercriminals attempt to obtain personal information through deception, are becoming increasingly sophisticated. In this study, we propose a novel approach that has machine learning techniques to detect phishing attacks effectively. Given the widespread prevalence of phishing, our method provides a robust mechanism to safeguard users' information. We employ ML models such as Random Forest, K-Nearest Neighbors, and Naïve Bayes to enhance the accuracy of phishing detection. Our approach is particularly effective in identifying phishing websites, malicious emails, and unsecured URLs. By leveraging the capabilities of various machine learning methodologies, we significantly improve detection performance. Our experimental results demonstrate substantial improvements in accuracy, F -score, recall, and precision, which give a promising solution for phishing attack detection. It's not only with the common people of India who are grappling with and being adversely affected by phishing attacks but also the highest court of India has faced it. During the first week of January 2025, Uttar Pradesh Additional Advocate General told the apex court that $\mathbf{1 0}$ IAS officers have been sent emails by an email address that is seemingly of the Supreme Court. Following that the court gave public notice. The Indian legal provisions related to phishing attack is also analyzed in this research.