False Data Injection (FDI) attacks against powersystem state estimation are a growing concern for operators.Previously, most works on FDI attacks have been performedunder the assumption of the attacker having full knowledge ofthe underlying system without clear justification. In this paper, wedevelop a topology-learning-aided FDI attack that allows stealthycyber-attacks against AC power system state estimation withoutprior knowledge of system information. The attack combinestopology learning technique, based only on branch and bus powerflows, and attacker-side pseudo-residual assessment to performstealthy FDI attacks with high confidence. This paper, for thefirst time, demonstrates how quickly the attacker can developfull-knowledge of the grid topology and parameters and validatesthe full knowledge assumptions in the previous work.

中文翻译：

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没有先验拓扑信息的拓扑学习辅助错误数据注入攻击

对电力系统状态估计的虚假数据注入（FDI）攻击已成为运营商日益关注的问题。以前，大多数FDI攻击工作都是在假设攻击者对底层系统有充分了解而又没有明确理由的前提下进行的。在本文中，我们开发了一种拓扑学习辅助的FDI攻击，该攻击可在不事先了解系统信息的情况下，对交流电源系统状态估计进行偷窃攻击。攻击结合了仅基于分支和总线功率流的拓扑学习技术，以及攻击者侧的伪残差评估，可以以高置信度进行完全的FDI攻击。本文首次展示了攻击者如何快速开发出完整的网格拓扑和参数知识，并验证了先前工作中的全部知识假设。