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All Journal JOIV : International Journal on Informatics Visualization INVOTEK: Jurnal Inovasi Vokasional dan Teknologi Journal of Information Technology and Computer Engineering JITSI : Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Advanced Science Computing and Engineering
Alde Alanda, Alde
Politeknik Negeri Padang, Indonesia
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Transportation

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A Hybrid Approach for Malicious URL Detection Using Ensemble Models and Adaptive Synthetic Sampling Sujon, Khaled Mahmud; Hassan, Rohayanti; Zainodin, Muhammad Edzuan; Salamat, Mohamad Aizi; Kasim, Shahreen; Alanda, Alde
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4627

Abstract

Malicious URLs pose a significant cybersecurity threat, often leading to phishing attacks, malware infections, and data breaches. Early detection of these URLs is crucial for preventing security vulnerabilities and mitigating potential losses. In this paper, we propose a novel approach for malicious URL detection by combining ensemble learning methods with ADASYN-based oversampling to address the class imbalance typically found in malicious URL datasets. We evaluated three popular machine learning classifiers, including XGBoost, Random Forest, and Decision Tree, and incorporated ADASYN (Adaptive Synthetic Sampling) to handle the class-imbalanced nature of our selected dataset. Our detailed experiments demonstrate that the application of ADASYN can significantly increase the performance of the predictive model across all metrics. For instance, XGBoost saw a 2.2% improvement in accuracy, Random Forest achieved a 1.0% improvement in recall, and Decision Tree displayed a 3.0% improvement in F1-score. The Decision Tree model, in particular, showed the most substantial improvements, particularly in recall and F1-score, indicating better detection of malicious URLs. Finally, our findings in this research highlighted the potential of ensemble learning, enhanced by ADASYN, for improving malicious URL detection and demonstrated its applicability in real-world cybersecurity applications.
Co-Authors - Liliwarti A. Mostafa, Salama Ab Razak, Mohd Faizal Abdulkareem A. Hezam Ade Irma Suryani Ahmad Firdaus, Ahmad Aldo Erianda Aldo Erianda Aldo Erianda, Aldo Alfajri Maulana Ibrahim Andi Ahmad Dahlan Armoogum, Sheeba Armoogum, Vinaye Bappoo, Soodeshna Chandra, Anurag Deddy Prayama Defni, - Deni Satria Dewi, Deshinta Arrova Dwiny Meidelfi Erwadi Bakar Gunasekaran, Saraswathy Shamini Hadelina, Rizka Hanriyawan Adnan Mooduto Hanriyawan Adnan Mooduto Hidra Amnur Humaira, Humaira Huriati, Putri Indri Rahmayuni Indri Rahmayuni Iswandi Saputra Kamarudin, Nur Khairani Khaleefah, Shihab Hamad Khattak, Umar Farooq Kurniawan, Tri Basuki M.Isthofa Ardhana Meri Azmi Mohd Salikon, Mohd Zaki Mohd Zaki Salikon Mooduto, H.A Mooduto, Hanriyawan Adnan Osman, Mohd Zamri Rahmat Hidayat Rasyidah Rasyidah, - Rizka Hadelina Rohayanti Hassan, Rohayanti Ronal Hadi Salama A. Mostafa Salamat, Mohamad Aizi Sarah Latifah Marif Satwarnirat Satwarnirat Shahreen Kasim, Shahreen Sujon, Khaled Mahmud Yaacob, Siti Salwani Yance Sonatha Yuhefizar Yuhefizar Zainodin, Muhammad Edzuan Zirawani Baharum