Graph-Based Threat Analytics with Neural Networks for Cloud-Connected Enterprise Cybersecurity

Abstract

Graph-based threat analytics provides a structured approach for understanding complex cybersecurity relationships across cloud-connected enterprise environments. Traditional security mechanisms frequently analyze events independently, which can limit their ability to identify coordinated attacks involving multiple users, devices, applications, identities, workloads, and network resources. This research proposes a graph-based threat analytics framework integrated with neural networks to represent enterprise security entities as interconnected nodes and their interactions as edges. The methodology combines graph construction, feature engineering, graph representation learning, neural threat classification, anomaly detection, and risk scoring to identify suspicious behavioral patterns across distributed cloud infrastructures. Enterprise telemetry from authentication systems, cloud workloads, network flows, APIs, endpoints, and application logs is transformed into temporal security graphs. Graph Neural Networks are then applied to learn structural and behavioral dependencies that conventional machine-learning models may overlook. The proposed framework supports detection of lateral movement, privilege escalation, anomalous access, coordinated attacks, compromised identities, and unusual resource interactions. Temporal analysis further enables evolving attack paths to be examined across multiple stages. The approach is designed to improve contextual threat detection while supporting scalable cloud security operations, automated alert prioritization, and explainable investigation. The resulting architecture provides an intelligent foundation for proactive enterprise cybersecurity across interconnected hybrid and cloud environments.