  In smart electrical grids, fault detection tasks may have a high impact on
society due to their economic and critical implications. In the recent years,
numerous smart grid applications, such as defect detection and load
forecasting, have embraced data-driven methodologies. The purpose of this study
is to investigate the challenges associated with the security of machine
learning (ML) applications in the smart grid scenario. Indeed, the robustness
and security of these data-driven algorithms have not been extensively studied
in relation to all power grid applications. We demonstrate first that the deep
neural network method used in the smart grid is susceptible to adversarial
perturbation. Then, we highlight how studies on fault localization and type
classification illustrate the weaknesses of present ML algorithms in smart
grids to various adversarial attacks
