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Related Experiment Videos

Network security situation prediction method based on KG and Parsimonious memory unit.

Zhiyong Sun1, Liheng Li2, Yang Zong1

  • 1The Second Affiliated Hospital of Heilongjiang, University of Chinese Medicine, Harbin, 150001, China.

Scientific Reports
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces an efficient network security situation prediction model using a knowledge graph (KG), graph convolutional network (GCN), and multilayer perceptron (MLP). The model achieves high accuracy (96.7%) and rapid prediction (11.2 ms), enhancing network risk management.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • The expanding Internet has led to increasingly prominent network security challenges.
  • Current network security situation prediction models suffer from long prediction times and low efficiency.

Purpose of the Study:

  • To design an efficient and accurate network security situation prediction model.
  • To address the limitations of existing prediction models in terms of speed and efficiency.

Main Methods:

  • A knowledge graph (KG) for network security situations was designed.
  • Graph convolutional networks (GCN) and multilayer perceptrons (MLP) were used for feature extraction from KG data.
  • Convolutional Block Attention Module (CBAM) and a prediction model (PMU) were employed for situation prediction.
Keywords:
Convolutional attention moduleGraph convolutional networkKGMulti-layer perceptronNetwork security situation predictionParsimonious memory unit

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Main Results:

  • Feature extraction accuracy reached 98.4%.
  • Optimized PMU prediction accuracy achieved 98.5%.
  • The designed model demonstrated a prediction accuracy of 96.7% with a prediction time of 11.2 ms and a Mean Absolute Error (MAE) of 0.081.
  • The model maintained over 90% prediction accuracy under various disturbances like traffic fluctuations, network conditions, equipment failure, and virus attacks.

Conclusions:

  • The developed model offers a significant improvement in network security situation prediction efficiency and accuracy.
  • The model provides valuable early warning information for managers, aiding in network risk reduction.
  • The research presents a viable method for real-time network security monitoring and threat assessment.