GRAPH NEURAL NETWORKS (GNNS) FOR MODELING CYBER ATTACK PATTERNS AND PREDICTING SYSTEM VULNERABILITIES IN CRITICAL INFRASTRUCTURE

Abstract

This study presents an extensive quantitative and literature-based examination of Graph Neural Networks (GNNs) as an emerging paradigm for modeling cyber attack patterns and predicting system vulnerabilities in critical infrastructure (CI) environments. Drawing upon a comprehensive review of over 130 peer-reviewed studies published between 2015 and 2025, the research synthesizes current methodologies, architectural advances, and applied frameworks that demonstrate how GNNs can effectively capture the relational and temporal complexity inherent in modern cyber-physical systems. Traditional cybersecurity techniques—such as rule-based intrusion detection, statistical anomaly analysis, and conventional deep learning—often fail to represent non-Euclidean dependencies and multi-stage attack sequences common in industrial control systems (ICS), power grids, and enterprise networks. In contrast, GNN-based models encode assets, users, communication protocols, and event flows as interconnected nodes and edges, enabling the detection of lateral movements, privilege escalations, and cascading failures that evolve dynamically across networks. The study explores core architectures including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), GraphSAGE, and Temporal Graph Networks (TGN), highlighting their performance advantages in node classification, link prediction, subgraph anomaly detection, and vulnerability scoring tasks. Furthermore, it evaluates robustness strategies against adversarial perturbations, self-supervised pretraining for label-scarce data, and interpretability mechanisms such as GNNExplainer and SubgraphX for operator trust and regulatory compliance. The comparative findings confirm that GNNs outperform traditional baselines in precision, recall, and contextual awareness while providing transparent, scalable, and temporally aware analytics suitable for mission-critical systems. Overall, this research establishes GNNs as a transformative approach for advancing cyber resilience through relational modeling and predictive vulnerability assessment, offering both theoretical insights and practical implications for safeguarding national and industrial infrastructures against sophisticated and evolving cyber threats.