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Attack path reconstruction from adverse consequences on power grids with a focus on Monitoring-Layer attacks

机译:从电网的不良后果重建攻击路径,重点是监控层攻击

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The Monitoring Layer (ML) attacks injects false measurements to manipulate operation decisions. Existing research on ML attacking models have an intrinsic deficiency: an attacker's goal is modeled by erroneous measurements, but not by final consequences on power grids. In this paper, we propose an ML-attack model based on adverse physical consequences. The proposed modeling method is essential to reconstruct attack paths in power system forensics and to future development of defense mechanisms against ML attacks. Examples of attack paths reconstruction are presented, on a sub-transmission system and a low voltage distribution system.
机译:监视层(ML)攻击会注入错误的度量来操纵操作决策。现有的关于ML攻击模型的研究有一个内在的缺陷:攻击者的目标是通过错误的度量建模的,而不是通过对电网的最终后果建模的。在本文中,我们提出了一种基于不利物理后果的ML攻击模型。所提出的建模方法对于重建电力系统取证中的攻击路径以及未来针对ML攻击的防御机制的发展至关重要。提出了在子传输系统和低压配电系统上重建攻击路径的示例。

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