Cybersecurity Threat Intelligence using Graph Neural Networks: A Survey and Future Directions

Author(s)	Mr. Ronak Goyal, Mrs. Ashwini Somani
Country	India
Abstract	This study explores the impact of Graph Neural Network (GNN) usage and Graph Feature Scores (GFS) on Threat Detection Accuracy (TDA) within cybersecurity systems. Using a structured questionnaire based on a 5-point Likert scale, data were collected from 242 cybersecurity professionals in New York. The analysis, conducted using R Studio, employed multiple regression techniques to assess the influence of GNN-based tools and graph feature integration on improving detection capabilities. The findings reveal a significant and positive relationship between both GNN Use and GFS with TDA, indicating that graph-based AI models can substantially enhance cybersecurity performance. The study contributes to the growing literature on AI-driven cybersecurity frameworks and highlights the practical relevance of GNNs in real-world threat intelligence. Future research can expand the model’s application to other sectors and geographies to validate scalability and adaptability.
Keywords	Cybersecurity, Graph Neural Networks, Threat Detection Accuracy, Graph Feature Score
Field	Computer Applications
Published In	Volume 17, Issue 2, April-June 2026
Published On	2026-04-09

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