NeuroShield: An Intelligent Graph Neural Network Model For Detecting Cyber Threats in Social Networks

Author(s)	Ms. Arti Arun Dhobale, Dr. N S Bagal
Country	India
Abstract	The rapid growth of social networking platforms has increased cyber threats such as phishing, fake accounts, spam, and malware attacks. Traditional security systems fail to effectively analyze complex user relationships in social networks. This paper proposes NeuroShield, an intelligent Graph Neural Network (GNN)-based framework for detecting cyber threats. The system uses Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to analyze user behavior and network interactions. Experimental results show that the proposed system achieves high accuracy, scalability, and efficient threat detection, making it suitable for modern cybersecurity applications.
Keywords	Graph Neural Network (GNN), Cyber Threat Detection, Social Networks, Deep Learning, Node Classification, Bot Detection, Misinformation Detection, Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Cybersecurity.
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-13

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