Detect, Repair and Audit: A Hierarchical Multi-Agent Defense Against Adversarial Attacks in Energy Forecasting

Summary

This research paper proposes a three-layer security model for energy infrastructure and a hierarchical defense against adversarial attacks on Times-Series Large Language Models used for electricity-demand forecasting. The defense pairs a Multi-Agent Reinforcement Learning detector with signal repair at local control centers, plus an evidence-grounded LLM auditing agent with human-in-the-loop validation for Virtual Power Plants. The best MARL configuration reaches an F1 score above 0.91, signal repair cuts adversarial-to-clean Relative Squared Error by up to 83%, and detection and repair average 7.95 ms.

Topics

• Adversarial machine learning

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