论文标题
隐形MTD反对电力系统中基于学习的盲目外国直接投资攻击
Stealthy MTD Against Unsupervised Learning-based Blind FDI Attacks in Power Systems
论文作者
论文摘要
本文探讨了如何通过无监督的基于学习的虚假数据注入（FDI）攻击来抵消在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的弹性。一种新颖的智能攻击，结合了基于密度的空间聚类和降低维度，并被证明可有效地在存在传统MTD策略的情况下保持隐身。在抵抗这种新型攻击时，提出了一种新型的MTD结合与物理水印的实现，这是通过将高斯水印添加到物理植物参数中以推动传统和智能外国直接投资攻击的检测，同时又隐藏在攻击者身上并限制了对系统操作和稳定性的影响。
This paper examines how moving target defences (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 density-based spatial clustering and dimensionality reduction, 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.
