Topology Learning Aided False Data Injection Attack without Prior Topology Information

2021/02/24 by Martin Higgins, Higgins, Martin, Jiawei Zhang +5 · 1 citation
Computer Science · #Network Security and Intrusion Detection #Quantum-Dot Cellular Automata #Coding theory and cryptography

paper · pdf · doi:10.48550/arxiv.2102.12248

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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