Enhancing User Safety Through URL-Based Phishing Detection with Random Forest And Decision Tree

One of the most common and severe types of cyber security attacks is phishing, which attempts to get private information of individuals and organizations alike. Leveraging the power of Random Forest and Decision Tree algorithms, in this paper, a machine learning-based method for detecting URL-based phishing has been presented. Based on experimental results, the accuracy of the Random Forest classifier is 93.27% and that of the Decision Tree classifier is 73.84%. The results suggest the remarkable efficiency of Random Forest in enhancing phishing detection systems, providing maximum security to users, and promoting safe online practices.