URL Phishing Detection System Utilizing Catboost Machine Learning Approach
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 c...
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International Journal of Computer Science and Network Security (IJCSNS)
2021
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Online Access: | http://eprints.utem.edu.my/id/eprint/25543/2/2.3.1.1.1%20IJCSNS%20URL%20PHISHING%20UTILIZING%20CATBOOST%20MACHINE%20LEARNING%20APPROACH.PDF http://eprints.utem.edu.my/id/eprint/25543/ http://paper.ijcsns.org/07_book/202109/20210939.pdf |
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my.utem.eprints.255432022-02-28T13:09:47Z http://eprints.utem.edu.my/id/eprint/25543/ URL Phishing Detection System Utilizing Catboost Machine Learning Approach Lim, Chian Fang Ayop, Zakiah Anawar, Syarulnaziah Othman, Nur Fadzilah Harum, Norharyati Abdullah, Raihana Syahirah 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. International Journal of Computer Science and Network Security (IJCSNS) 2021-09 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/25543/2/2.3.1.1.1%20IJCSNS%20URL%20PHISHING%20UTILIZING%20CATBOOST%20MACHINE%20LEARNING%20APPROACH.PDF 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 http://paper.ijcsns.org/07_book/202109/20210939.pdf 10.22937/IJCSNS.2021.21.9.39 |
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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. |
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Lim, Chian Fang Ayop, Zakiah Anawar, Syarulnaziah Othman, Nur Fadzilah Harum, Norharyati Abdullah, Raihana Syahirah |
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Lim, Chian Fang Ayop, Zakiah Anawar, Syarulnaziah Othman, Nur Fadzilah Harum, Norharyati Abdullah, Raihana Syahirah URL Phishing Detection System Utilizing Catboost Machine Learning Approach |
author_facet |
Lim, Chian Fang Ayop, Zakiah Anawar, Syarulnaziah Othman, Nur Fadzilah Harum, Norharyati Abdullah, Raihana Syahirah |
author_sort |
Lim, Chian Fang |
title |
URL Phishing Detection System Utilizing Catboost Machine Learning Approach |
title_short |
URL Phishing Detection System Utilizing Catboost Machine Learning Approach |
title_full |
URL Phishing Detection System Utilizing Catboost Machine Learning Approach |
title_fullStr |
URL Phishing Detection System Utilizing Catboost Machine Learning Approach |
title_full_unstemmed |
URL Phishing Detection System Utilizing Catboost Machine Learning Approach |
title_sort |
url phishing detection system utilizing catboost machine learning approach |
publisher |
International Journal of Computer Science and Network Security (IJCSNS) |
publishDate |
2021 |
url |
http://eprints.utem.edu.my/id/eprint/25543/2/2.3.1.1.1%20IJCSNS%20URL%20PHISHING%20UTILIZING%20CATBOOST%20MACHINE%20LEARNING%20APPROACH.PDF http://eprints.utem.edu.my/id/eprint/25543/ http://paper.ijcsns.org/07_book/202109/20210939.pdf |
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13.18916 |