Horus-CDS: The digital eye on smart grid security

This work proposes the Horus-Cyber Detection for Smart Grids (Horus-CDS), a tool based on temporal neural networks for detecting anomalies in the network traffic of a Smart Grid (SG). The model employs Temporal Convolutional Networks (TCNs), which leverage dilated causal convolutions to capture sequential patterns and enhance real-time inference. Unlike conventional approaches, the tool enables greater parallelization, lower latency, and an interface for monitoring and decision-making. The tool was validated in two scenarios: a simulated SG environment and a real-world deployment at an brazilian Smart grid managing company, where its resilience against Distributed Denial-of-Service(DDoS) and Distributed Denial-of-Control (DDoC) attacks was tested. The results demonstrated that Horus-CDS outperformed other approaches, ensuring the operational continuity of power distribution systems.