# Topology Learning Aided False Data Injection Attack without Prior Topology Information

arXiv: https://arxiv.org/abs/2102.12248 · HTML: https://mlsanity.com/paper/2102.12248/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2021.

## Built on by (most cited first, 2 of 2)

- [Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles](https://mlsanity.com/paper/2212.07757/) (2022)
- [From Cycle Space to Cycle Manifold: Limits and Achievability of Blind False Data Injection Attacks](https://mlsanity.com/paper/2609.10631/) (2026)

## Abstract (arXiv)

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.