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http://dx.doi.org/10.22937/IJCSNS.2021.21.9.39

URL Phishing Detection System Utilizing Catboost Machine Learning Approach  

Fang, Lim Chian (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM))
Ayop, Zakiah (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM))
Anawar, Syarulnaziah (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM))
Othman, Nur Fadzilah (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM))
Harum, Norharyati (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM))
Abdullah, Raihana Syahirah (Information Security Forensics and Computer Networking (INSFORNET), Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM))
Publication Information
International Journal of Computer Science & Network Security / v.21, no.9, 2021 , pp. 297-302 More about this Journal
Abstract
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.
Keywords
Phishing; URL; CatBoost; Logistic Regression; Random Forest;
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