A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection

ArXi:2510.26307v2 Announce Type: replace-cross Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience. Graph-based approaches have become increasingly important for modeling entity interactions, yet most rely on homogeneous and static structures, which limits their ability to capture the heterogeneity and temporal evolution of real-world environments.