A one-class GRU forecasting framework with adaptive residual windows and modified SHAP detects replayed false data injections in Purdue's PUR-1 reactor signals with 93% point-level accuracy and under 1% false positives.
A data-based private learning framework for enhanced security against replay attacks in cyber-physical systems
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A One-Class Explainable AI Framework for Identification of Non-Stationary Concurrent False Data Injections in Nuclear Reactor Signals
A one-class GRU forecasting framework with adaptive residual windows and modified SHAP detects replayed false data injections in Purdue's PUR-1 reactor signals with 93% point-level accuracy and under 1% false positives.