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可能对社会产生重大影响。近年来，诸如缺陷检测和负荷预测等众多智能电网应用已采用数据驱动方法。本研究旨在探讨智能电网场景下机器学习应用的安全性挑战。实际上，这些数据驱动算法的鲁棒性和安全性尚未在所有电网应用中得到充分研究。我们首先证明了智能电网中使用的深度神经网络方法易受对抗性扰动的影响。随后，通过故障定位与类型分类研究，揭示了当前智能电网中机器学习算法在面对多种对抗攻击时的脆弱性。