Phishing URL Classification in Cybersecurity Education: A Comparative Study of RF, SVM, DT, and LR

The integration of artificial intelligence (AI) and machine learning (ML) into cybersecurity practice has increased the need for hands-on, data-driven learning in cybersecurity education. This paper presents a classroom-oriented phishing URL classification study using the UCI PhiUSIIL Phishing URL Dataset (2024), a public benchmark with 235,795 instances and 54 features suitable for supervised learning tasks. Four widely used ML classifiers—Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR)—were evaluated using accuracy, precision, recall, specificity, and F1-score. The results show that RF achieved the best overall performance with 93.33% accuracy and 93.75% F1-score, while SVM produced the highest precision (93.85%). DT showed strong specificity but lower recall, and LR yielded the weakest overall performance. The study is designed to support cybersecurity education by using interpretable baseline models, standard evaluation metrics, and a reproducible public dataset. It provides a practical workflow for lab activities and course projects while contributing an empirical comparison that complements existing pedagogical frameworks in AI-enabled cybersecurity education.