Optimized Graph Neural Network Framework for Intelligent Cyber Threat Identification and Security Monitoring in Cloud Platforms

Abstract

The increasing interconnectivity, virtualization, and dynamic resource allocation of cloud platforms have created complex cybersecurity environments in which conventional security monitoring approaches may struggle to represent relationships among users, virtual machines, services, network flows, and attack behaviors. This research proposes an Optimized Graph Neural Network (OGNN) Framework for intelligent cyber threat identification and security monitoring in cloud platforms. The framework models cloud infrastructures as heterogeneous interaction graphs and applies graph-based representation learning to capture structural and behavioral dependencies among interconnected entities. Graph normalization, feature aggregation, uncertainty estimation, and optimization-oriented decision mechanisms are incorporated to improve detection robustness and operational effectiveness. The theoretical foundation combines graph neural network reasoning with machine-learning-assisted optimization, particularly insights from graph-based combinatorial reasoning and mathematical optimization. The proposed framework uses node, edge, and graph-level representations to classify suspicious activities and generate security-risk indicators while accounting for prediction uncertainty. The conceptual findings indicate that graph-based modeling can improve contextual threat identification compared with isolated event analysis, while optimization mechanisms can support efficient monitoring under computational constraints. The framework further emphasizes uncertainty-aware decisions because high-confidence and low-confidence alerts should not receive identical security responses. The research contributes an integrated architecture for cloud threat monitoring that connects relational deep learning, optimization, and uncertainty quantification. The study also identifies limitations associated with dynamic graph construction, computational scalability, heterogeneous cloud telemetry, and the absence of universally representative benchmark conditions. The proposed architecture provides a research foundation for adaptive, explainable, and optimization-driven cloud cybersecurity systems.