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All Journal Journal of Future Artificial Intelligence and Technologies
Mangj, Syefy Mohammed
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Computer Science & IT

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BERTPHIURL: A Teacher-Student Learning Approach Using DistilRoBERTa and RoBERTa for Detecting Phishing Cyber URLs Hussan, Payman Hussein; Mangj, Syefy Mohammed
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 4 (2025): March 2025
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-71

Abstract

Phishing is a fraudulent activity wherein an attacker impersonates a trusted individual or organization to acquire sensitive information from an online user. Phishing websites have become a major cyber-security issue in the contemporary digital landscape. As online activities expand in e-commerce, banking, and social media, the hazards presented by these fraudulent websites have intensified. Deep learning-based Natural Language Processing (NLP) approaches offer an effective solution for detecting phishing URLs. However, deploying large models like BERT or RoBERTa for real-time detection poses computational challenges. This study proposes BERTPHIURL, a Teacher-Student Learning framework that leverages RoBERTa as the Teacher model and DistilRoBERTa as the Student model to improve phishing detection efficiency while reducing computational overhead. By applying knowledge distillation, the Student model learns from the Teacher, preserving high detection accuracy with significantly lower resource consumption. The proposed approach effectively captures contextual relevance and local features in malicious URL detection tasks. The experiments were conducted on a dataset exceeding 50,000 URLs to evaluate performance. Results indicate that BERTPHIURL achieves a 94.22% accuracy, outperforming existing phishing detection methods while maintaining efficiency suitable for real-time applications.
Co-Authors Hussan, Payman Hussein