Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids
This addresses security vulnerabilities in smart grid fault prediction systems, which have critical economic and societal impacts, but is incremental as it builds on existing adversarial attack research in a specific domain.
The study demonstrated that deep neural networks used in smart grid fault detection are vulnerable to adversarial perturbations, showing weaknesses in fault localization and type classification under various attacks.
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