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Related Experiment Videos

Explainable few-shot learning with modern BERT for detecting emerging phishing attacks using XF PhishBERT.

Mohammed Tawfik1, Ashraf A Abu-Ein2,3, Amr H Abdelhaliem4

  • 1Faculty of Computer and Information Technology, Sana'a University, Sana'a, Yemen. kmkhol01@gmail.com.

Scientific Reports
|December 1, 2025
PubMed
Summary

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This summary is machine-generated.

XF-PhishBERT offers explainable few-shot learning for phishing detection, achieving high accuracy with minimal data. This cybersecurity solution overcomes limitations of traditional methods by adapting to novel threats efficiently.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Artificial Intelligence

Background:

  • Phishing attacks evolve rapidly, outpacing traditional detection systems.
  • Machine learning models require extensive labeled data, creating vulnerabilities for new threats.
  • Acquiring labeled data for emerging cyber threats is costly and time-consuming.

Purpose of the Study:

  • To introduce XF-PhishBERT, an explainable few-shot learning framework for efficient phishing detection.
  • To enable effective phishing detection with minimal training examples.
  • To provide transparent decision-making support for security analysts.

Main Methods:

  • Combined ModernBERT transformer architecture with domain-specific URL features.
  • Integrated prototypical networks and model-agnostic meta-learning (MAML) for few-shot learning.
Keywords:
BERTFew-shot learningLarge language models (LLMs)Meta-learningPhishing detection

Related Experiment Videos

  • Utilized consensus-based feature selection (Random Forest, Mutual Information, RFECV) and SHAP analysis for explainability.

    • Main Results:

    • Achieved 99.9% accuracy with 10 examples per class and 98.5% in one-shot learning.
    • Demonstrated 186% performance retention in cross-dataset evaluation, significantly outperforming traditional methods (39%).
    • Browser extension deployment showed 98.3% precision and 42ms latency.

    Conclusions:

    • Few-shot learning effectively addresses the challenge of limited labeled data in cybersecurity.
    • XF-PhishBERT provides a robust and explainable solution for rapidly evolving phishing threats.
    • The framework enhances cybersecurity by enabling rapid adaptation and transparent threat analysis.