Topology Learning Aided False Data Injection Attack without Prior Topology Information

TL;DR

This paper presents a novel method for stealthy false data injection attacks on power systems that learns system topology without prior information, enabling high-confidence cyber-attacks.

Contribution

It introduces a topology-learning-based FDI attack that operates without prior system knowledge, demonstrating rapid topology and parameter acquisition.

Findings

Attacker can quickly learn grid topology and parameters.

Proposed attack achieves high confidence in stealthy FDI.

Validates assumptions of full knowledge in prior work.

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.