Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks
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 tailore...
| Authors | Damilola Popoola, Souradeep Bhattacharya, Manimaran Govindarasu |
|---|---|
| Primary category | cs.CR |
| Published | 11 September 2026 |
| Last updated at source | 11 September 2026 |
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