Horus-CDS: The Digital Eye on Smart Grid Security

• Alexandro de O. Paula UnB http://orcid.org/0000-0002-9504-7308
• Weslei F. Santos UESB
• Vinícius P. Gonçalves UnB https://orcid.org/0000-0002-3771-2605
• André L. M. Serrano UnB
• Rodolfo I. Meneguette USP https://orcid.org/0000-0003-2982-4006
• Roger Immich UFRN https://orcid.org/0000-0003-2483-6382
• Geraldo P. Rocha Filho UESB https://orcid.org/0000-0001-6795-2768

DOI: https://doi.org/10.5753/sbrc_estendido.2025.6622

Abstract

This work proposes the Hórus-Cyber Detection for Smart Grids (Hórus-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 Neoenergia, where its resilience against DDoS attacks was tested. The results demonstrated that Hórus-CDS outperformed other approaches, ensuring the operational continuity of power distribution systems.

Keywords: Cybersecurity, Smart grids, Neural network, TCN, incident detection and response

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