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

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

Published on: December 15, 2023

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

Summary

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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) attacks Dynamic graph neural networks (DGNN)Graph attention networks Software-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

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

Published on: December 15, 2023

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.