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

TL;DR

This paper introduces data-driven subspace methods for executing undetectable data attacks on power system state estimation, bypassing the need for detailed system parameters.

Contribution

It proposes novel subspace-based attack strategies that learn system behavior from measurements, enabling unobservable attacks without prior system knowledge.

Findings

Effective attack strategies demonstrated on IEEE 14-bus network.

Attack methods successfully mislead bad data detection.

Performance validated on IEEE 118-bus network.

Abstract

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.