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Enhancing Phishing Detection in Semantic Web Systems Using Optimized Deep Learning Models

Author

Listed:
  • Liang Zhou

    (Jiading District Central Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China)

  • Akshat Gaurav

    (Ronin Institute, USA)

  • Varsha Arya

    (Hong Kong Metropolitan University, Hong Kong & Department of Electrical and Computer Engineering, Lebanese American University, Beirut, Lebanon)

  • Razaz Waheeb Attar

    (Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia)

  • Shavi Bansal

    (Insights2Techinfo, India & Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India)

  • Ahmed Alhomoud

    (Department of Computer Science, Faculty of Science, Northern Border University, Arar, Saudi Arabia)

Abstract

Phishing detection in Semantic Web systems is crucial to safeguarding users from malicious attacks. In this context, this work presents a deep learning-based phishing attack detection model using MobileBERT for feature extraction and hyperparameter optimization using covariance matrix adaptation evolution strategy (CMA-ES). The model obtained a 95% classification accuracy. Important benchmarks like accuracy, recall, and F1-score show good ability to discriminate between phishing and legitimate emails. Applying CMA-ES, which improved detection accuracy, helps to verify the model even more. MobileBERT and CMA-ES together offer Semantic Web systems a fresh, efficient method of phishing detection.

Suggested Citation

  • Liang Zhou & Akshat Gaurav & Varsha Arya & Razaz Waheeb Attar & Shavi Bansal & Ahmed Alhomoud, 2024. "Enhancing Phishing Detection in Semantic Web Systems Using Optimized Deep Learning Models," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-13, January.
    • Handle: RePEc:igg:jswis0:v:20:y:2024:i:1:p:1-13
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    Cited by:

    1. Yimeng Liang & Jun Zhang, 2025. "Semantic Web-Enhanced Reinforcement Learning Model for Urban Planning Optimization," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 21(1), pages 1-20, January.

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