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Machine learning (ML) sees an increasing prevalence of being used in theinternet-of-things (IoT)-based smart grid. However, the trustworthiness of MLis a severe issue that must be addressed to accommodate the trend of ML-basedsmart grid applications (MLsgAPPs). The adversarial distortion injected intothe power signal will greatly affect the system's normal control and operation.Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPsapplied in the context of safety-critical power systems. In this paper, weprovide a comprehensive review of the recent progress in designing attack anddefense methods for MLsgAPPs. Unlike the traditional survey about ML security,this is the first review work about the security of MLsgAPPs that focuses onthe characteristics of power systems. We first highlight the specifics forconstructing the adversarial attacks on MLsgAPPs. Then, the vulnerability ofMLsgAPP is analyzed from both the aspects of the power system and ML model.Afterward, a comprehensive survey is conducted to review and compare existingstudies about the adversarial attacks on MLsgAPPs in scenarios of generation,transmission, distribution, and consumption, and the countermeasures arereviewed according to the attacks that they defend against. Finally, the futureresearch directions are discussed on the attacker's and defender's side,respectively. We also analyze the potential vulnerability of large languagemodel-based (e.g., ChatGPT) power system applications. Overall, we encouragemore researchers to contribute to investigating the adversarial issues ofMLsgAPPs.

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