# Subspace Methods for Data Attack on State Estimation: A Data Driven Approach

arXiv: https://arxiv.org/abs/1406.0866 · HTML: https://mlsanity.com/paper/1406.0866/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2014.

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

- [Physically Consistent Null Space Alignment for Detection of Low-Magnitude False Data Injection Attacks](https://mlsanity.com/paper/2606.08473/) (2026)
- [From Cycle Space to Cycle Manifold: Limits and Achievability of Blind False Data Injection Attacks](https://mlsanity.com/paper/2609.10631/) (2026)

## Abstract (arXiv)

Data attacks on state estimation modify part of system measurements such that the tempered measurements cause incorrect system state estimates. Attack techniques proposed in the literature often require detailed knowledge of system parameters. Such information is difficult to acquire in practice. The subspace methods presented in this paper, on the other hand, learn the system operating subspace from measurements and launch attacks accordingly. Conditions for the existence of an unobservable subspace attack are obtained under the full and partial measurement models. Using the estimated system subspace, two attack strategies are presented. The first strategy aims to affect the system state directly by hiding the attack vector in the system subspace. The second strategy misleads the bad data detection mechanism so that data not under attack are removed. Performance of these attacks are evaluated using the IEEE 14-bus network and the IEEE 118-bus network.