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

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Title Subspace Methods for Data Attack on State Estimation: A Data Driven Approach
Names Kim, Jinsub (creator)
Tong, Lang (creator)
Thomas, Robert J. (creator)
Date Issued 2015-03-01 (iso8601)
Note This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IEEE-Institute of Electrical and Electronics Engineers and can be found at: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78
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.
Genre Article
Topic state estimation
Identifier Kim, J., Tong, L., & Thomas, R. J. (2015). Subspace Methods for Data Attack on State Estimation: A Data Driven Approach. IEEE Transactions on Signal Processing, 63(5), 1102-1114. doi:10.1109/TSP.2014.2385670

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