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

机译:攻击电网对电网不利后果的重建重构,重点是监测层攻击

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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:监控层(ML)攻击注入了虚假测量以操纵操作决策。现有的ML攻击模型研究具有内在缺陷:攻击者的目标是由错误的测量建模的,但不是通过对电网的最终后果进行建模。在本文中,我们提出了基于不利身体后果的ML攻击模型。该建模方法对于重建电力系统取证中的攻击路径以及对ML攻击的未来发展,是必不可少的。在子传输系统和低电压分布系统上呈现攻击路径重建的示例。

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