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Related Experiment Videos

Network attack security detection model based on model agnostic meta-learning algorithm.

Liang Chen1, Jingtao Ma2

  • 1Network Security and Information Construction Office, Jilin Provincial Institute of Education, Changchun, 130022, China.

Scientific Reports
|June 30, 2026
PubMed
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This study introduces a novel network attack detection model using Model-Agnostic Meta-learning (MAML) and Convolutional Neural Networks (CNNs). It effectively enhances generalization for diverse threats, improving adaptive intrusion detection systems.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Evolving network intrusion threats necessitate advanced detection methods.
  • Existing systems struggle with diverse and dynamic attack scenarios.
  • Few-shot learning offers potential for rapid adaptation to new threats.

Purpose of the Study:

  • To develop a Model-Agnostic Meta-learning (MAML)-based network attack detection model.
  • To enhance generalization capabilities across diverse attack scenarios using an error-corrected few-shot dataset.
  • To improve adaptive intrusion detection systems against complex and evolving threats.

Main Methods:

  • Integration of an optimized Convolutional Neural Network (CNN) architecture with meta-learning algorithms.
  • Utilizing an error-corrected few-shot dataset for enhanced generalization.
Keywords:
Convolutional neural networkDefensive capabilityFew sample dataMeta-learningNetwork attack

Related Experiment Videos

  • Employing Model-Agnostic Meta-learning (MAML) for adaptive feature extraction and task-specific adaptation.

    • Main Results:

    • Near-optimal loss convergence (loss value close to 0) and excellent training curve stability.
    • Achieved a defense action probability of 0.85 for simulated attacks.
    • Demonstrated a detection accuracy of 0.91 and recognition accuracy of 0.92 for real-world attacks, outperforming baselines.

    Conclusions:

    • The MAML-based CNN model effectively detects simulated and real-world network attacks.
    • The framework shows robust performance and strong optimization stability.
    • The study advances adaptive intrusion detection systems for dynamic network environments.