False data separation for data security in smart grids

False data separation for data security in smart grids The smart grid is emerging as an efficient paradigm for electric power generation, transmission, and consumption, based on optimized decision making and control that leverage the measurement data of sensors and meters in the grid. False data injection is a new type of power grid attacks aiming to tamper such important data. For the security and robustness of the grid, it is critical to separate the false data injected by such attacks and recover the original measurement data. Nonetheless, the existing approaches often neglect the true changes on original measurement data that are caused by the real perturbations on grid states and hence have a risk of removing these true changes as injected false data during the data recovery. In this paper, we preserve these true changes by modeling the false data problem as a rank-bounded $$L_1$$ L 1 norm optimization and propose both offline and online algorithms to filter out the injected false data and recover original measurement data. Trace-driven simulations verify the efficacy of our solution. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge and Information Systems Springer Journals

False data separation for data security in smart grids

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Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Computer Science; Information Systems and Communication Service; IT in Business
ISSN
0219-1377
eISSN
0219-3116
D.O.I.
10.1007/s10115-016-1019-8
Publisher site
See Article on Publisher Site

Abstract

The smart grid is emerging as an efficient paradigm for electric power generation, transmission, and consumption, based on optimized decision making and control that leverage the measurement data of sensors and meters in the grid. False data injection is a new type of power grid attacks aiming to tamper such important data. For the security and robustness of the grid, it is critical to separate the false data injected by such attacks and recover the original measurement data. Nonetheless, the existing approaches often neglect the true changes on original measurement data that are caused by the real perturbations on grid states and hence have a risk of removing these true changes as injected false data during the data recovery. In this paper, we preserve these true changes by modeling the false data problem as a rank-bounded $$L_1$$ L 1 norm optimization and propose both offline and online algorithms to filter out the injected false data and recover original measurement data. Trace-driven simulations verify the efficacy of our solution.

Journal

Knowledge and Information SystemsSpringer Journals

Published: Jan 18, 2017

References

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