Exposing the Invisible: Detecting Stealthy Parameter-Based Cyber-Attacks on Inverter Synchronization Loops

What the math gives to ML

The paper's key transferable mechanism is that stealthy controller-parameter tampering can preserve apparent stability while changing equilibrium points and reducing dynamic margins. Its modified PLL deliberately makes a hidden gain observable by coupling the gain to an equilibrium shift, turning parameter integrity into a state-estimation problem rather than relying on instability alarms. A neural-network analogue is a small trusted sentinel dynamical system attached to an optimizer or recurrent model, whose equilibrium encodes a secret gain and therefore exposes silent changes to learning-rate, recurrent, or control parameters.