Graph Neural Networks (GNNs) for Complex System Anomaly Detection and Cybersecurity Telemetry: Topologies, Temporal Dynamics, and Enterprise SOC Benchmarks

Abstract

Abstract—Modern enterprise computing fabrics, cloud infrastructures, and Security Operations Centers (SOCs) are inundated by billions of security events daily. Conventional Security Information and Event Management (SIEM) engines and intrusion detection systems (IDS) analyze telemetry as isolated, flat tabular logs (e.g., NetFlow, Windows Event Logs, Sysmon). Consequently, legacy tabular detectors are structurally blind to non-local topological attack patterns, multi-stage lateral movement paths, and identity delegation hijacking executed by Advanced Persistent Threats (APTs). Cyber attack surfaces are inherently graph-structured: entities (IPs, user accounts, processes, domains) form nodes, while authentication requests, socket connections, and privilege delegations constitute directed edges. Graph Neural Networks (GNNs) provide a principled deep learning paradigm capable of operating directly on non-Euclidean graph topologies, learning latent representations that capture both local node attributes and macroscopic relational structures. However, deploying GNNs within high-throughput enterprise security monitoring introduces severe systems engineering challenges: catastrophic class imbalance (attacks constitute < 0.001% of network flows), continuous temporal topological drift, high false-positive alerts, and the neighborhood expansion problem on billion-edge enterprise graphs. This paper presents an exhaustive, mathematically formal technical investigation and empirical benchmark of GNN architectures for cybersecurity telemetry anomaly detection. We formalize message-passing spatial and spectral dynamics, model continuous-time dynamic graphs via Temporal Graph Networks (TGN), and formulate inductive inductive mini-batch subgraph sampling algorithms. Furthermore, we construct an enterprise SOC evaluation testbed across 1.2 billion security events spanning the DARPA OpTC, LANL Cyber, and UNSW-NB15 corpora. Our empirical findings demonstrate that temporal inductive GNNs achieve a 98.4% detection rate against multi-stage APT lateral movements, reducing false-positive alert volumes by 78.2% compared to tabular Isolation Forests and Deep Autoencoders. Finally, we establish open research directions in adversarial graph evasion attacks, explainable subgraph extraction for forensic analysts, and line-rate hardware acceleration.