DOI : https://doi.org/10.18517/ijaseit.15.1.12553

Phishing Domain Detection Using Machine Learning Algorithms

Dinny Komalasari (1), Tri Basuki Kurniawan (2), Deshinta Arrova Dewi (3), Mohd Zaki Zakaria (4), Zubaile Abdullah (5), Alde Alanda (6)
(1) Faculty of Vocasional, Universitas Bina Darma, Palembang, Indonesia
(2) Postgraduate Program, Universitas Bina Darma, Palembang, Indonesia
(3) Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
(4) Faculty of Computer & Mathematic Sciences, University Technology Mara, Malaysia
(5) Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
(6) Department of Information Technology, Politeknik Negeri Padang, Padang, Indonesia
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How to cite (IJASEIT) :
[1]
D. Komalasari, T. B. Kurniawan, D. A. Dewi, M. Z. Zakaria, Z. Abdullah, and A. Alanda, “Phishing Domain Detection Using Machine Learning Algorithms”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 1, pp. 318–327, Feb. 2025.
Citation Format :
Phishing, a prevalent cyber threat, continues to jeopardize sensitive information by exploiting the vulnerabilities of digital platforms. This research investigates the escalating danger of phishing attacks, focusing on the creation of deceptive websites known as phishing domains. Leveraging machine learning algorithms, particularly supervised and unsupervised learning techniques, the study aims to proactively identify and classify these malicious domains by analyzing diverse factors like domain names, online content, SSL certificates, and historical data. The proposed solution involves the development of prediction models using decision trees, random forests, support vector machines, and Gradient Boosting, with the latter exhibiting the highest accuracy at 92%. The system assigns risk scores to domains based on properties such as registration details and SSL certificate validity, facilitating the real-time identification of potential phishing activities. The research addresses the critical need for data security in the face of phishing threats affecting individuals and businesses, providing a robust defense mechanism against evolving cyber threats. Recommendations for continuous model training, regular updates, diversification of dataset sources, and integration with existing security infrastructure aim to enhance the system's adaptability and resilience in countering emerging phishing threats. Overall, this study contributes to ongoing efforts in cybersecurity, offering a proactive defense mechanism against the pervasive and evolving challenges posed by phishing attacks.

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