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Related Experiment Videos

Self-learning model fusion for network anomaly detection: A hybrid CNN-LSTM-transformer framework.

Jun Wang1,2, Ning Huang1,2, Houzhong Zhang1,2

  • 1College of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang, Liaoning, China.

Plos One
|October 29, 2025
PubMed
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This study introduces a hybrid deep learning framework for network anomaly detection, featuring a self-learning mechanism to adapt to evolving cyber threats and maintain high detection accuracy.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • Rapidly evolving cyber threats challenge traditional anomaly detection systems.
  • Existing systems struggle with adaptability and performance against novel attack patterns.
  • Need for autonomous systems that can learn and adapt to dynamic threat landscapes.

Purpose of the Study:

  • To develop an innovative hybrid deep learning framework for enhanced network traffic anomaly detection.
  • To integrate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer models with a self-learning mechanism.
  • To improve the adaptability and robustness of anomaly detection systems against evolving cyber threats.

Main Methods:

  • A synergistic two-stage model fusion architecture combining CNN, LSTM, and Transformer models.

Related Experiment Videos

  • An adaptive learning mechanism with multi-metric drift detection for autonomous threat response.
  • A knowledge preservation strategy to maintain detection capabilities during adaptation.

    • Main Results:

    • CNN-LSTM model achieved F1-scores of 0.9778 (UNSW-NB15) and 0.9695 (CICIDS2017) for binary classification.
    • LSTM-Transformer model achieved accuracies of 0.9632 and 0.9528 for specific anomaly type classification.
    • Framework maintained an average accuracy of 0.955 over 15 days with induced concept drifts.
    • Self-learning mechanism detected drifts and recovered performance within 23.4 hours, with a 92.8% detection rate for zero-day attacks.

    Conclusions:

    • The proposed hybrid deep learning framework effectively enhances network traffic anomaly detection.
    • The self-learning mechanism provides autonomous adaptation to evolving threats and concept drifts.
    • The framework demonstrates robustness and improved performance, offering a promising direction for future cybersecurity systems.