The machine learning (ML) sees an increasing prevalence of being used in the internet-of-things enabled smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system's normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. The survey is organized from the aspects of adversarial assumptions, targeted applications, evaluation metrics, defending approaches, physics-related constraints, and applied datasets. We also highlight future directions on this topic to encourage more researchers to conduct further research on adversarial attacks and defending approaches for MLsgAPPs.

翻译：机器学习在物联网赋能的智能电网中正日益普及。然而，机器学习的可信度是一个必须解决的严重问题，以适应基于机器学习的智能电网应用的发展趋势。注入电力信号中的对抗性扰动将极大地影响系统的正常控制和运行。因此，对于应用于安全关键电力系统中的基于机器学习的智能电网应用进行脆弱性评估势在必行。本文对基于机器学习的智能电网应用的攻击和防御方法设计的最新进展进行了全面回顾。与传统的机器学习安全综述不同，这是首篇关注电力系统特性的基于机器学习的智能电网应用安全综述。该综述从对抗假设、目标应用、评估指标、防御方法、物理相关约束和所用数据集等方面进行组织。我们还强调了该领域的未来方向，以鼓励更多研究人员针对基于机器学习的智能电网应用的对抗性攻击和防御方法进行进一步研究。