Black box attack on machine learning assisted wide area monitoring and protection systems

The applications for wide area monitoring, protection, and control systems
(WAMPC) at the control center, help with providing resilient, efficient,
and secure operation of the transmission system of the smart grid. The
increased proliferation of phasor measurement units (PMUs) in this space
has inspired many prudent applications to assist in the process of
decision making in the control centers. Machine learning (ML) based
decision support systems have become viable with the availability of
abundant high-resolution wide area operational PMU data. We propose a deep
neural network (DNN) based supervisory protection and event diagnosis
system and demonstrate that it works with very high degree of confidence.
The system introduces a supervisory layer that processes the data streams
collected from PMUs and detects disturbances in the power systems that may
have gone unnoticed by the local monitoring and protection system. Then,
we investigate compromise of the insights of this ML based supervisory
control by crafting adversaries that corrupt the PMU data via minimal
coordinated manipulation and identification of the spatio-temporal regions
in the multidimensional PMU data in a way that the DNN classifier makes
wrong event predictions.