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Dynamic graph neural network-based framework to increase detection accuracy in SDN under DDOS.

Saad Ahmed Ali Kalafy1, Saied Pashazadeh2, Pedram Salehpour3

  • 1Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran.

Scientific Reports
|December 30, 2025
PubMed
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This summary is machine-generated.

GCTNetwork, a novel framework using Dynamic Graph Neural Networks, effectively detects Distributed Denial of Service (DDoS) attacks in Software-Defined Networks (SDNs). It achieves high accuracy and reliability, outperforming existing models for enhanced network security.

Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Software-Defined Networking (SDN) centralizes network management, creating vulnerabilities for Distributed Denial of Service (DDoS) attacks.
  • Existing intrusion detection systems struggle with evolving attack patterns and capturing complex SDN topological features.

Purpose of the Study:

  • To propose GCTNetwork, an innovative framework utilizing Dynamic Graph Neural Networks (DGNN) for real-time DDoS attack identification in SDNs.
  • To enhance the detection capabilities beyond conventional and current deep learning models in SDN environments.

Main Methods:

  • GCTNetwork integrates Gated Convolutional Temporal (GCT) layers for node-edge feature analysis.
  • Employs an Edge-Aware LSTM for modeling temporal dependencies and a Graph Attention Layer (GAT) for communication pathway emphasis.
Keywords:
Distributed denial of service (DDoS) attacksDynamic graph neural networks (DGNN)Graph attention networksSoftware-Defined networking (SDN)Temporal graph modeling

Related Experiment Videos

Last Updated: Jan 7, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

990
  • Leverages the SDN Dataset for training and validation.

    • Main Results:

    • GCTNetwork achieved 94.08% accuracy and a 93.27% F1-score on the SDN Dataset.
    • Outperformed advanced models like LR-STGCN, GRAN, and ST-GCN.
    • Demonstrated high reliability with a False Alarm Index (FAI) of 0.06, indicating effective alert reduction.

    Conclusions:

    • GCTNetwork provides precise, efficient, and stable DDoS detection in SDNs.
    • Dynamic, edge-aware graph learning significantly improves security within SDN infrastructures.
    • The framework shows stable convergence and generalization without overfitting.