URL-Based Phishing Detection: A Comprehensive Review and New Taxonomy
DOI:
https://doi.org/10.29304/jqcsm.2026.18.32760Keywords:
Phishing URL Detection Cybersecurity Natural Language Processing(NLP) Metadata analysis Machine Learning Techniques Deep Learning TechniquesAbstract
Phishing attacks remain to be classified among one of the most severe and persistent types of cyber threats, introducing a substantial danger to the contemporary online infrastructure. Attackers frequently utilize spoofed URLs to mislead users into disclosing confidential information. Accelerating evolution of phishing strategies, facilitated by AI-driven mechanisms, diminished the reliability of conventional detection approaches, such as blacklist and content-based methods, making these approaches inadequate for real-time deployment. This paper provides a comprehensive review of URL-based phishing detection techniques, with primary focus on Deep Learning, Machine Learning, Natural Language Processing (NLP), and metadata-based approaches. The paper analysis demonstrates the most existing studies rely on isolated feature-sets, restricting their ability to address the sophistication of the contemporary phishing attempts. Consequently, there is a demand for lightweight, real-time phishing detection mechanisms. This paper proposes a lightweight and real-time URL phishing detection method that is based on the concept of integrating Natural Language Processing (NLP) and metadata analysis. In response, this study emphasizes on the integration of NLP-based semantic analysis, accompanied with metadata-based attributes. The integration of these approaches may enhance the capability to discriminate between legitimate and malicious URLs. It may also maintain relatively low computational overhead. Overall, this review highlights the importance of hybrid feature-based, and establishes a foundation for the implementation of computationally-efficient, scalable, and real-time phishing detection frameworks for web applications.
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Copyright (c) 2026 Rafal Abd-Alkadhim Mohammed, Riyadh Rahef Nuiaa Alogaili, Ahmed Raad Al-Sudani
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
