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  3. Improving Phishing Email Detection Performance Through Deep Learning With Adaptive Optimization.
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  3. Improving Phishing Email Detection Performance Through Deep Learning With Adaptive Optimization.

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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

View abstract on PubMed

Summary
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.

Keywords:
Deep learningEmail securityMachine learningMountain gazelle optimizerOptimizationPhishing email detection

Related Experiment Videos

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).
  • 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.