Smart-grid detector study finds shuffled tests overstate attack detection
Temporal evaluation drops macro-F1 from 0.96 to 0.88, and stealthy false-data injection defeats all three observation views.
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Temporal evaluation drops macro-F1 from 0.96 to 0.88, and stealthy false-data injection defeats all three observation views.
Simply reporting the maximum feasible queue was as damaging as a trained surrogate; random inflation was not.
Under chronological splits, no model beats a constant normal predictor, and unseen-attack AUROC averages 0.64.
StarGNN flags smart-grid instability and unseen false-data injection using only stable training data.
Researchers propose Safety-Aware Zero Trust that weighs physical harm from restricting IoT and cyber-physical components.
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