URL Phishing Detection System Utilizing Catboost Machine Learning Approach

Lim, Chian Fang and Ayop, Zakiah and Anawar, Syarulnaziah and Othman, Nur Fadzilah and Harum, Norharyati and Abdullah, Raihana Syahirah (2021) URL Phishing Detection System Utilizing Catboost Machine Learning Approach. International Journal of Computer Science and Network Security, 21 (9). pp. 297-302. ISSN 1738-7906

Text 2.3.1.1.1 IJCSNS URL PHISHING UTILIZING CATBOOST MACHINE LEARNING APPROACH.PDF Download (232kB)

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.

Item Type:	Article
Uncontrolled Keywords:	Phishing, URL, CatBoost, Logistic regression, Random forest
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	28 Feb 2022 13:09
Last Modified:	28 Feb 2022 13:09
URI:	http://eprints.utem.edu.my/id/eprint/25543
Statistic Details:	View Download Statistic

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