LATIDIA · Ciberseguridad

Detección de anomalías autoverificable mediante IA explicable para la ciberseguridad de redes DER.

arXiv:2609.12305v1 Anuncio Tipo: nuevo Resumen: El rápido crecimiento de los Recursos Energéticos Distribuidos (RED) ha expandido significativamente la superficie de ataque cibernético de las redes eléctricas modernas. Además, la creciente sofisticación en

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arXiv:2609.12305v1 Announce Type: new Abstract: The rapid growth of Distributed Energy Resources (DERs) has significantly expanded the cyber attack surface of modern power grids. Furthermore, increasing sophistication in attack techniques demands anomaly detection systems (ADS) that are accurate, interpretable, and reliable to support DER cybersecurity. While ML-based ADS provide strong detection capabilities, their black-box nature reduces operator trust and limits Security Operation Center's (SOC) ability to effectively interpret alerts and respond, highlighting the need for explainable Artificial Intelligence (XAI) to ensure transparency and operational confidence. This paper presents an XAI-based anomaly detection framework tailored for DER networks (ExCYDER). The proposed framework uses a self-verifying mechanism that validates ADS alerts to