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

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  • 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.
  • ©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. 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
  • Keywords: subspace method, cyber physical system, state estimation, data framing attack, false data injection
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  • 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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  • 63
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  • 5
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  • This work is supported in part by the National Science Foundation under Grant CNS-1135844.
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