Subspace Methods for Data Attack on State Estimation: A Data Driven Approach
This addresses the challenge of practical data attacks in power systems, where attackers lack detailed system knowledge, but the approach is incremental as it builds on existing subspace methods for attack strategies.
The paper tackles the problem of data attacks on state estimation in power systems by proposing subspace methods that learn the system operating subspace from measurements to launch attacks without requiring detailed system parameters. The attacks are evaluated on IEEE 14-bus and 118-bus networks, showing they can hide attack vectors or mislead detection mechanisms.
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.