Targeted Power System Frequency Attack via the Selection of Maliciously Controlled Inverters

What the math gives to ML

The paper provides a transferable actuator-selection mechanism: select a small subset of controllable coordinates by exploiting an unstable eigenmode whose amplitude is large at designated targets and small at the compromised coordinates. Its concrete asset is an eigenvector-based ranking combined with a binary quadratic subset-selection problem, rather than generic gradient saliency. For recurrent or state-space neural networks, the same construction can identify hidden-state or module interventions that selectively amplify target outputs, or conversely identify and damp the most dangerous mode-exposure channels. The transfer is falsifiable through a predicted sharp dependence of target amplification on the real part or modulus of the dominant Jacobian eigenvalue and on the selected subset size.