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This paper introduces physics-constrained digital twins for detecting stealthy false data injection in urban pedestrian flow, using flow conservation and conformal prediction to improve sensor integrity.
This paper investigates how adversarial data modifications to electricity price forecasts can impact industrial demand response, finding that while attacks can erode profits, limited perturbations preserve most of the financial benefit.
This paper proposes a Cycle-Space Detector (CSD) for detecting blind false data injection attacks on power systems, where an autoencoder generates stealthy perturbations aligned with the measurement Jacobian null space. The CSD uses topology-derived cycle constraints to improve detection without requiring precise line parameters.