False data injection attacks against state estimation in electric power grids

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False data injection attacks against state estimation in electric power grids

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Conference on Computer and Communications Security archive
Proceedings of the 16th ACM conference on Computer and communications security table of contents

Chicago, Illinois, USA

SESSION: Attacks I table of contents

Pages: 21-32

Year of Publication: 2009

ISBN:978-1-60558-894-0

Authors

Yao Liu	North Carolina State University, Raleigh, NC, USA
Michael K. Reiter	University of North Carolina, Chapel Hill, Chapel Hill, NC, USA
Peng Ning	North Carolina State University, Raleigh, NC, USA

Sponsor

SIGSAC: ACM Special Interest Group on Security, Audit, and Control

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 53, Downloads (12 Months): 105, Citation Count: 0

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ABSTRACT

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including the interacting bad measurements introduced by arbitrary, non-random causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.

In this paper, we present a new class of attacks, called false data injection attacks, against state estimation in electric power grids. We show that an attacker can exploit the configuration of a power system to launch such attacks to successfully introduce arbitrary errors into certain state variables while bypassing existing techniques for bad measurement detection. Moreover, we look at two realistic attack scenarios, in which the attacker is either constrained to some specific meters (due to the physical protection of the meters), or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios, which can not only change the results of state estimation, but also modify the results in arbitrary ways. We demonstrate the success of these attacks through simulation using IEEE test systems. Our results indicate that security protection of the electric power grid must be revisited when there are potentially malicious attacks.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Box Plot: Display of Distribution. http://www.physics.csbsju.edu/stats/box2.html.
	2	Electric Power Risk Assessment. http://www.solarstorms.org/ElectricAssessment.html.
	3	Edoardo Amaldi , Viggo Kann, On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems, Theoretical Computer Science, v.209 n.1-2, p.237-260, Dec. 6, 1998 [doi>10.1016/S0304-3975(97)00115-1]
	4	E. N. Asada, A. V. Garcia, and R. Romero. Identifying multiple interacting bad data in power system state estimation. In IEEE Power Engineering Society General Meeting, pages 571--577, June 2005.
	5	T. Blumensath and M. Davies. Gradient pursuits. IEEE Transactions on Signal Processing, 56(6):2370--2382, June 2008.
	6	J. Chen and A. Abur. Improved bad data processing via strategic placement of PMUs. In IEEE Power Engineering Society General Meeting, pages 509--513, June 2005.
	7	J. Chen and A. Abur. Placement of PMUs to enable bad data detection in state estimation. IEEE Transactions on Power Systems, 21(4):1608--1615, November 2006.
	8	S. S. Chen. PhD thesis: Basis Pursuit. Department of Statistics, Stanford University, 1995.
	9	E.Handschin, F. C. Schweppe, J. Kohlas, and A. Fiechter. Bad data analysis for power system state estimation. IEEE Transactions on Power Apparatus and Systems, 94(2):329--337, April 1975.
	10	A. Garcia, A. Monticelli, and P. Abreu. Fast decoupled state estimation and bad data processing. IEEE Transactions on Power Apparatus and Systems, 98(5):1645--1652, September 1979.
	11	Michael R. Garey , David S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman & Co., New York, NY, 1979
	12	S. Gastoni, G. P. Granelli, and M. Montagna. Multiple bad data processing by genetic algorithms. In IEEE Power Tech Conference, pages 1--6, June 2003.
	13	P. Georgiev and A. Cichoki. Sparse component analysis of overcomplete mixtures by improved basis pursuit method. In the 2004 IEEE International Symposium on Circuits and Systems (ISCAS 2004), pages 5:37--40, May 2004.
	14	D. V. Hertem, J. Verboomen, K. Purchala, R. Belmans, and W. L. Kling. Usefulness of DC power flow for active power flow analysis with flow controlling devices. In The 8th IEE International Conference on AC and DC Power Transmission, pages 58--62, March 2006.
	15	P. S. Huggins and S. W. Zucker. Greedy basis pursuit. IEEE Transactions on Signal Processing, 55(7):3760--3772, July 2007.
	16	J. Lin and H. Pan. A static state estimation approach including bad data detection and identification in power systems. In IEEE Power Engineering Society General Meeting, pages 1--7, June 2007.
	17	R. Kinney, P. Crucitti, R. Albert, and V. Latora. Modeling cascading failures in the north American power grid. European Physical Journal B - Condensed Matter and Complex Systems, 46:101--107, 2005.
	18	M. Li, Q. Zhao, and P. B. Luh. DC power flow in systems with dynamic topology. In Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, pages 1--8, 2008.
	19	L. Lovisolo, E. A. B. da Silva, M. A. M. Rodrigues, and P. S. R. Diniz. Efficient coherent adaptive representations of monitored electric signals in power systems using damped sinusoids. IEEE Transactions on Signal Processing, 53(10):3831--3846, October 2005.
	20	Carl D. Meyer, Matrix analysis and applied linear algebra, Society for Industrial and Applied Mathematics, Philadelphia, PA, 2000
	21	L. Mili, T. V. Cutsem, and M. Ribbens-Pavella. Hypothesis testing identification: A new method for bad data analysis in power system state estimation. 103(11):3239--3252, November 1984.
	22	L. Milli, T. V. Cutsem, and M. R. Pavella. Bad data identification methods in power system state estimation, a comparative study. IEEE Transactions on Power Apparatus and Systems, 103(11):3037--3049, November 1985.
	23	A. Monticelli. State Estimation in Electric Power Systems, A Generalized Approach. Kluwer Academic Publishers, 1999.
	24	A. Monticelli and A. Garcia. Reliable bad data processing for real-time state estimation. IEEE Transactions on Power Apparatus and Systems, 102(5):1126--1139, May 1983.
	25	A. Monticelli, F. F. Wu, and M. Y. Multiple. Bad data identification for state estimation by combinatorial optimization. IEEE Transactions on Power Delivery, 1(3):361--369, July 1986.
	26	B. K. Natarajan, Sparse Approximate Solutions to Linear Systems, SIAM Journal on Computing, v.24 n.2, p.227-234, April 1995 [doi>10.1137/S0097539792240406]
	27	Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In the 27th Asilomar Conference on Signals, Systems and Computers, 1993.
	28	V. H. Quintana, A. Simoes-Costa, and M. Mier. Bad data detection and identification techniques using estimation orthogonal methods. IEEE Transactions on Power Apparatus and Systems, 101(9):3356--3364, September 1982.
	29	F. C. Schweppe, J. Wildes, and D. B. Rom. Power system static state estimation. parts 1, 2, 3. IEEE Transactions on Power Apparatus and Systems, 89(1):120--135, January 1970.
	30	U.S.-Canada Power System Outage Task Force. Final report on the August 14, 2003 blackout in the United States and Canada. https://reports.energy.gov/B-F-Web-Part1.pdf, April 2004.
	31	A. Wood and B. Wollenberg. Power generation, operation, and control. John Wiley and Sons, 2nd edition, 1996.
	32	N. Xiang and S. Wang. Estimation and identification of multiple bad data in power system state estimation. In the 7th Power Systems Computation Conference, PSCC, pages 1061--1065, July 1981.
	33	N. Xiang, S. Wang, and E. Yu. A new approach for detection and identification of multiple bad data in power system state estimation. IEEE Transactions on Power Apparatus and Systems, 101(2):454--462, Febuary 1982.
	34	N. Xiang, S. Wang, and E. Yu. An application of estimation-identification approach of multiple bad data in power system state estimation. In IEEE Power Engineering Society Summber Meeting, July 1983.
	35	L. Zhao and A. Abur. Multi area state estimation using synchronized phasor measurements. IEEE Transactions on Power Systems, 20(2):611--617, May 2005.
	36	J. Zhu and A. Abur. Bad data identification when using phasor measurements. In IEEE Power Tech Conference, pages 1676--1681, July 2007.
	37	R. D. Zimmerman and C. E. Murillo-Sánchez. MATPOWER, A MATLAB Power System Simulation Package. http://www.pserc.cornell.edu/matpower/manual.pdf, September 2007.