Vulnerability of Machine Learning Approaches Applied in IoT-Based Smart Grid: A Review

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based 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 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…

• NN
  National Natural Science Foundation of China

  Awards: 62073285, 62362008, 62293500, 61833015, 62293502, 62293503, 62303126, 62103371
• FR
  Fundamental Research Funds for the Central Universities

  Awards: 226-2023-00111, 226-2022-0010
• NS
  Natural Science Foundation of Zhejiang Province

  Awards: LZ23F030009, LR23F030001