As the complexities of Dynamic Data Driven Applications Systems increase, preserving their resilience becomes more challenging. For instance, maintaining power grid resilience is becoming increasingly complicated due to the growing number of stochastic variables (such as renewable outputs) and extreme weather events that add uncertainty to the grid. Current optimization methods have struggled to accommodate this rise in complexity. This has fueled the growing interest in data-driven methods used to operate the grid, leading to more vulnerability to cyberattacks. One such disruption that is commonly discussed is the adversarial disruption, where the intruder attempts to add a small perturbation to input data in order to "manipulate" the system operation. During the last few years, work on adversarial training and disruptions on the power system has gained popularity. In this paper, we will first review these applications, specifically on the most common types of adversarial disruptions: evasion and poisoning disruptions. Through this review, we highlight the gap between poisoning and evasion research when applied to the power grid. This is due to the underlying assumption that model training is secure, leading to evasion disruptions being the primary type of studied disruption. Finally, we will examine the impacts of data poisoning interventions and showcase how they can endanger power grid resilience.

翻译：随着动态数据驱动应用系统复杂性的增加，维持其韧性变得更具挑战性。例如，由于随机变量（如可再生能源出力）的增多以及极端天气事件给电网带来的不确定性日益增加，维持电网韧性正变得越来越复杂。当前的优化方法难以适应这种复杂性的增长。这促使人们对用于电网运行的数据驱动方法兴趣日增，同时也导致系统更易受到网络攻击。其中一种常被讨论的破坏形式是对抗性破坏，即入侵者试图向输入数据添加微小扰动以“操纵”系统运行。过去几年中，针对电力系统的对抗性训练与破坏研究日益受到关注。本文首先将综述这些应用，特别是最常见的两类对抗性破坏：规避攻击与投毒攻击。通过此综述，我们揭示了投毒研究与规避研究在电网应用领域存在的差距。这是由于现有研究普遍假设模型训练过程是安全的，导致规避攻击成为主要研究对象。最后，我们将探讨数据投毒干预的影响，并展示其如何危及电网韧性。