# PCGAN

Working with write access to only a subset of deployed sensor channels and no knowledge of grid topology or detector parameters, PCGAN learns approximate active-power–angle and reactive-power–voltage-magnitude relations from historical measurements and uses adversarial training to generate data-injection perturbations that resemble normal operation. On the IEEE 300-bus system with one year of PJM hourly demand and 10% to 100% compromised sensors, it reaches 84.28% mean evasion of ten machine-learning detectors and 22.31% mean spatial load misperception, for an average stealth-impact score of 18.69% against 5.61% for the strongest competing attack.

Working with write access to only a subset of deployed sensor channels and no knowledge of grid topology or detector parameters, PCGAN learns approximate active-power–angle and reactive-power–voltage-magnitude relations from historical measurements and uses adversarial training to generate data-injection perturbations that resemble normal operation. On the IEEE 300-bus system with one year of PJM hourly demand and 10% to 100% compromised sensors, it reaches 84.28% mean evasion of ten machine-learning detectors and 22.31% mean spatial load misperception, for an average stealth-impact score of 18.69% against 5.61% for the strongest competing attack.