Camouflage-resistant graph neural networks for power grid anomaly detection
Abstract Power grid anomaly detection requires modeling both network topology and device dependencies. Graph neural networks (GNNs) are suitable for this task. However, few prior works have considered the stealthy camouflage behavior of grid anomalies, where faulty devices or malicious attackers intentionally mask thei...
Ya Guo, Junyi Wang, Boyu Liu et al.
· Cybersecurity · 0 citations