Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids. (arXiv:2303.18136v2 [cs.CR] UPDATED)

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 …

adversarial adversarial attacks applications arxiv attacks cs.cr data data-driven defect detection detection economic forecasting grid impact machine prediction smart society study systems tasks