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arXiv · 2102.12248

Topology Learning Aided False Data Injection Attack without Prior Topology Information

Martin Higgins·Jiawei Zhang·Ning Zhang·Fei Teng
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Abstract

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.

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Martin Higgins, Jiawei Zhang, Ning Zhang, Fei Teng. 2021-02-24. Topology Learning Aided False Data Injection Attack without Prior Topology Information. https://arxiv.org/abs/2102.12248

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