This paper examines how moving target defenses (MTD) implemented in power systems can be countered by unsupervised learning-based false data injection (FDI) attack and how MTD can be combined with physical watermarking to enhance the system resilience. A novel intelligent attack, which incorporates dimensionality reduction and density-based spatial clustering, is developed and shown to be effective in maintaining stealth in the presence of traditional MTD strategies. In resisting this new type of attack, a novel implementation of MTD combining with physical watermarking is proposed by adding Gaussian watermark into physical plant parameters to drive detection of traditional and intelligent FDI attacks, while remaining hidden to the attackers and limiting the impact on system operation and stability.

中文翻译：

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针对电力系统中基于无监督学习的盲目 FDI 攻击的隐形 MTD


本文研究了如何通过基于无监督学习的虚假数据注入（FDI）攻击来对抗电力系统中实施的移动目标防御（MTD），以及如何将 MTD 与物理水印相结合以增强系统弹性。开发了一种新颖的智能攻击，该攻击结合了降维和基于密度的空间聚类，并被证明可以在传统 MTD 策略存在的情况下有效地保持隐身性。为了抵御这种新型攻击，提出了一种MTD与物理水印相结合的新颖实现方式，通过将高斯水印添加到物理工厂参数中来驱动对传统和智能FDI攻击的检测，同时对攻击者保持隐藏并限制对系统运行的影响和稳定性。