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URL Phishing Detection System Utilizing Catboost Machine Learning Approach


Lim Chian Fang, Zakiah Ayop, Syarulnaziah Anawar, Nur Fadzilah Othman, Norharyati Harum, Raihana Syahirah Abdullah


Vol. 21  No. 9  pp. 297-302


The development of various phishing websites enables hackers to access confidential personal or financial data, thus, decreasing the trust in e-business. This paper compared the detection techniques utilizing URL-based features. To analyze and compare the performance of supervised machine learning classifiers, the machine learning classifiers were trained by using more than 11,005 phishing and legitimate URLs. 30 features were extracted from the URLs to detect a phishing or legitimate URL. Logistic Regression, Random Forest, and CatBoost classifiers were then analyzed and their performances were evaluated. The results yielded that CatBoost was much better classifier than Random Forest and Logistic Regression with up to 96% of detection accuracy.


Phishing; URL; CatBoost; Logistic Regression; Random Forest.