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

翻译：在智能电网中，故障检测任务因其经济性和关键性影响而对社会具有高度重要性。近年来，诸多智能电网应用（如缺陷检测和负荷预测）已广泛采用数据驱动方法。本研究旨在探讨智能电网场景下机器学习应用的安全挑战。事实上，这些数据驱动算法的鲁棒性与安全性尚未在所有电网应用场景中得到充分研究。我们首先证明智能电网中使用的深度神经网络方法易受对抗性扰动影响。随后，通过故障定位与类型分类研究，揭示当前智能电网中机器学习算法在应对多种对抗攻击时的脆弱性。