A Review on Malicious URLs Detection Using Machine Learning Methods
Tasfia Tabassum, Md. Mahbubul Alam, Md. Sabbir Ejaz, Mohammad Kamrul Hasan
Journal of Engineering Research and Reports · pp. 76–88 · Published 19 Dec 2023
10.9734/jerr/2023/v25i121042Abstract
Malicious URLs are a serious threat to cybersecurity because they can compromise user security and inflict large financial losses. The extensiveness and adaptability of traditional detection approaches which rely on blacklists are limited when it comes to rapidly emerging threats. In response, machine learning methods have become more popular as a means of improving the detection efficiency of malicious URLs. This paper provides a thorough analysis providing a structured understanding of all aspects and formal formulation of the machine learning job of malicious URL detection. It covers feature representation and algorithm design, classifying and reviewing contributions from literature studies. The survey aims to provide a state-of-the-art understanding and support future research and practical implementations. It targets a diverse audience, including experts, cybersecurity professionals and machine learning researchers. The article provides a comprehensive overview of the field discussing practical system design considerations, ongoing research challenges and future research directions.
Cited by 6
Student, Department of Information Technology, University of Mumbai, Mumbai (Maharashtra), India., Aqsa Shaikh, Mariya Shaikh · International Journal of Innovative Technology and Exploring Engineering · 2025
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Saurabh Kailas, R. Roopalakshmi · IEEE Access · 2025
Melvin Ajuluchukwu, Emmanuel Balogun, Nafeeul Alam Walee · 2025 IEEE 17th International Conference on Computational Intelligence and Communication Networks (CICN) · 2025
Abiodun Esther Omolara, Moatsum Alawida · Computers & Security · 2025
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