arXiv Machine Learning September 14, 2026 By Damilola Popoola, Souradeep Bhattacharya, Manimaran Govindarasu

Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks

Read the original on arXiv Machine Learning →

The paper introduces ExCYDER, an explainable AI framework for anomaly detection in Distributed Energy Resource (DER) networks. It combines LightGBM with SHAP to self-verify alerts, ensuring that each detection aligns with feature‑attribution evidence. Experiments on a realistic DNP3 dataset show over 98% detection accuracy, 44.6% rule‑SHAP consistency, 14.5 ms SHAP latency per alert, and minimal confidence deviation, while distinguishing coherent from inconsistent alerts without sacrificing accuracy.

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