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Related Experiment Videos

Improving phishing email detection performance through deep learning with adaptive optimization.

Mehdi Hosseinzadeh1,2,3, Usman Ali4, Saqib Ali5

  • 1Institute of Research and Development, Duy Tan University, Da Nang, Vietnam. mehdihosseinzadeh@duytan.edu.vn.

Scientific Reports
|October 21, 2025
PubMed
Summary

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This summary is machine-generated.

This study introduces a hybrid deep learning model using Bidirectional Encoder Representations from Transformers (BERT) and Mountain Gazelle Optimizer (MGO) for advanced phishing email detection, significantly improving accuracy and reducing false positives.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Phishing email attacks are increasingly sophisticated, challenging traditional detection methods.
  • Distinguishing between legitimate and malicious emails requires advanced techniques due to realistic impersonation.
  • Existing cybersecurity measures face limitations against evolving phishing tactics.

Purpose of the Study:

  • To propose a novel hybrid deep learning architecture for enhanced phishing email detection.
  • To leverage Bidirectional Encoder Representations from Transformers (BERT) for contextual understanding.
  • To optimize the model using the Mountain Gazelle Optimizer (MGO) for improved performance.

Main Methods:

  • A hybrid architecture combining BERT embeddings, Convolutional Neural Network (CNN), and Gated Recurrent Unit (GRU).
Keywords:
Deep learningEmail securityMachine learningMountain gazelle optimizerOptimizationPhishing email detection

Related Experiment Videos

  • Incorporation of multi-head attention to refine focus on critical email features.
  • Hyperparameter optimization using the Mountain Gazelle Optimizer (MGO) on a Kaggle phishing email dataset.

    • Main Results:

    • Achieved high classification accuracy (96.8%), precision (97.2%), recall (95.4%), and F1 score (96.3%).
    • Demonstrated a 2.5% reduction in false positives compared to state-of-the-art methods.
    • Validated the model's effectiveness against sophisticated phishing threats.

    Conclusions:

    • The proposed hybrid deep learning model significantly enhances phishing email detection capabilities.
    • Transformer-based embeddings combined with advanced neural networks and optimization are effective against phishing.
    • The MGO-optimized architecture offers a robust solution for improving email security.