🤖 AI Summary
This work addresses cyber-physical attacks on power systems that combine physical disturbances with data manipulation, particularly data denial attacks that undermine conventional detection methods. To tackle this challenge, the authors propose AdaptoNet, a modular neural network architecture that dynamically activates trainable adaptive modules based on a binary measurement availability vector, while keeping the base modules frozen and thus avoiding retraining. This design enables effective discrimination between genuine anomalies and denied measurements even under partial observability. Experimental results on four IEEE benchmark systems demonstrate that, under measurement blocking attacks affecting up to 20% of sensors, AdaptoNet restores the F1 score from below 12% to over 81%, closely approaching the baseline performance of 89%–99% achieved with complete data.
📝 Abstract
Cyber attacks on the power grid combine physical disruptions with compromised data to destabilize cyber-physical systems. We demonstrate that data denial attacks, where adversaries block measurements in a targeted region while triggering a line outage, reduce detection performance by more than 86\%, rendering standard data-driven methods ineffective. We propose AdaptoNet, a modular neural network that adapts to measurement availability through conditional controls. AdaptoNet pairs a frozen foundational module trained on complete data with a trainable adaptive module, conditioned on a binary measurement-availability vector, enabling the model to distinguish between denied and anomalous data without retraining the foundational module. Evaluated across four IEEE test systems (30-, 39-, 57-, and 118-bus) under in-region attacks blocking up to 20% of measurements, AdaptoNet recovers F1 from below 12\% to above 81\%, an approximate sevenfold improvement approaching the 89%-99% baseline with complete measurements.