Secure Power Systems Against Malicious Cyber-Physical Data Attacks: Protection and Identification
The security of power systems against malicious cyberphysical
data attacks becomes an important issue. The adversary
always attempts to manipulate the information structure of the power
system and inject malicious data to deviate state variables while
evading the existing detection techniques based on residual test. The
solutions proposed in the literature are capable of immunizing the
power system against false data injection but they might be too costly
and physically not practical in the expansive distribution network.
To this end, we define an algebraic condition for trustworthy power
system to evade malicious data injection. The proposed protection
scheme secures the power system by deterministically reconfiguring
the information structure and corresponding residual test. More
importantly, it does not require any physical effort in either microgrid
or network level. The identification scheme of finding meters being
attacked is proposed as well. Eventually, a well-known IEEE 30-bus
system is adopted to demonstrate the effectiveness of the proposed
schemes.
[1] E. Handschin, F. Schweppe, J. Kohlas, and A. Fiechter, Bad data analysis
for power system state estimation, IEEE Transactions on Power Apparatus
and Systems, vol. 94, no. 2, pp. 329-337, 1975.
[2] Y. Liu, P. Ning, and M. K. Reiter, False data injection attacks against
state estimation in electric power grids, in Proc. ACM Conf. Comput.
Commun. Security, Chicago, IL, Nov. 2009.
[3] K. Clements, G. Krumpholz, and P. Davis, Power system state estimation
residual analysis: an algorithm using network topology, IEEE Transactions
on Power Apparatus and Systems, no. 4, pp. 1779-1787, 1981.
[4] A. Monticelli, Electric power system state estimation, Proceedings of the
IEEE, vol. 88, no. 2, pp. 262282, 2000.
[5] L. Xie, Y. Mo, and B. Sinopoli, False data injection attacks in electricity
markets, in 2010 First IEEE International Conference on Smart Grid
Communications (SmartGridComm)., pp. 226-231,IEEE 2010.
[6] T. T. Kim and H. V. Poor, Strategic protection against data injection
attacks on power grids, IEEE Transactions on Smart Grid, vol. 2, pp.326-
333, 2011.
[7] R. Bobba, K. Rogers, Q. Wang, H. Khurana, K. Nahrstedt, and T.
Overbye, Detecting false data injection attacks on dc state estimation, in
CPSWEEK 2010, the First Workshop on Secure Control Systems, 2010.
[8] O. Kosut, L. Jia, R. Thomas, and L. Tong, Malicious data attacks on the
smart grid, IEEE Transactions on Smart Grid, no. 4, pp. 645-658,2011.
[9] G. Dan and H. Sandberg, Stealth attacks and protection schemes for state
estimators in power systems, in First IEEE International Conference on
Smart Grid Communications (SmartGridComm), pp. 214-219, 2010.
[10] A. Giani, E. Bitar, M. McQueen, P. Khargonekar, K. Poolla, and M.
Garcia. Smart grid data integrity attacks: Characterizations and countermeasures.
In Proceedings of the IEEE SmartGridComm, October 2011.
[11] H. Sandberg, A. Teixeira, and K. H. Johansson. On security indices for
state estimators in power networks. In Preprints of the FirstWorkshop
on Secure Control Systems, CPSWEEK 2010, Stockholm, Sweden, April
2010.
[12] K. C. Sou, H. Sandberg, and K. H. Johansson. Electric power net- work
security analysis via minimum cut relaxation. In Proceedings of the 50th
IEEE Conference on Decision and Control, December 2011.
[13] Y. Yuan, Z. Li, and K. Ren, Modeling load redistribution attacks in power
systems, IEEE Transactions on Smart Grid, vol. 2. no. 2, pp. 382-390,
Jun. 2011.
[14] A. Gomez-Exposito, A. Abur, A. de la Villa Jaen, and C. Gomez-Quiles,
Amultilevel state estimation paradigm for smart grids, Proceedings of the
IEEE, vol. 99, pp. 952-976, 2011.
[15] Ben-Israel Adi and Greville Thomas N.E., Generalized Inverses, 2nd
Edition, Wiley-Interscience, Chpt. 2, Sec.4, 2003.
[16] G. Marsaglia and G. P. H. Styan. Equalities and inequalities for ranks
of matrices. Linear and Multi-linear Algebra, 269-292, 2 1974.
[17] R. W. Farebrother, Linear least squares computations, Marcel Dekker
INC, pp. 160, 1988.
[18] Muni S. Srivastava and Hirokazu Yanagihara, Testing the equality of
several covariance matrices with fewer observations than the dimension,
Elsevier, Journal of Multivariate Analysis 101,pp.1323, 2010.
[19] R.D. Zimmerman and C.E. Murillo-Sanchez. MATPOWER,
A MATLAB Power System Simulation Package.
http://www.pserc.cornell.edu/matpower/manual.pdf, September 2007.
[1] E. Handschin, F. Schweppe, J. Kohlas, and A. Fiechter, Bad data analysis
for power system state estimation, IEEE Transactions on Power Apparatus
and Systems, vol. 94, no. 2, pp. 329-337, 1975.
[2] Y. Liu, P. Ning, and M. K. Reiter, False data injection attacks against
state estimation in electric power grids, in Proc. ACM Conf. Comput.
Commun. Security, Chicago, IL, Nov. 2009.
[3] K. Clements, G. Krumpholz, and P. Davis, Power system state estimation
residual analysis: an algorithm using network topology, IEEE Transactions
on Power Apparatus and Systems, no. 4, pp. 1779-1787, 1981.
[4] A. Monticelli, Electric power system state estimation, Proceedings of the
IEEE, vol. 88, no. 2, pp. 262282, 2000.
[5] L. Xie, Y. Mo, and B. Sinopoli, False data injection attacks in electricity
markets, in 2010 First IEEE International Conference on Smart Grid
Communications (SmartGridComm)., pp. 226-231,IEEE 2010.
[6] T. T. Kim and H. V. Poor, Strategic protection against data injection
attacks on power grids, IEEE Transactions on Smart Grid, vol. 2, pp.326-
333, 2011.
[7] R. Bobba, K. Rogers, Q. Wang, H. Khurana, K. Nahrstedt, and T.
Overbye, Detecting false data injection attacks on dc state estimation, in
CPSWEEK 2010, the First Workshop on Secure Control Systems, 2010.
[8] O. Kosut, L. Jia, R. Thomas, and L. Tong, Malicious data attacks on the
smart grid, IEEE Transactions on Smart Grid, no. 4, pp. 645-658,2011.
[9] G. Dan and H. Sandberg, Stealth attacks and protection schemes for state
estimators in power systems, in First IEEE International Conference on
Smart Grid Communications (SmartGridComm), pp. 214-219, 2010.
[10] A. Giani, E. Bitar, M. McQueen, P. Khargonekar, K. Poolla, and M.
Garcia. Smart grid data integrity attacks: Characterizations and countermeasures.
In Proceedings of the IEEE SmartGridComm, October 2011.
[11] H. Sandberg, A. Teixeira, and K. H. Johansson. On security indices for
state estimators in power networks. In Preprints of the FirstWorkshop
on Secure Control Systems, CPSWEEK 2010, Stockholm, Sweden, April
2010.
[12] K. C. Sou, H. Sandberg, and K. H. Johansson. Electric power net- work
security analysis via minimum cut relaxation. In Proceedings of the 50th
IEEE Conference on Decision and Control, December 2011.
[13] Y. Yuan, Z. Li, and K. Ren, Modeling load redistribution attacks in power
systems, IEEE Transactions on Smart Grid, vol. 2. no. 2, pp. 382-390,
Jun. 2011.
[14] A. Gomez-Exposito, A. Abur, A. de la Villa Jaen, and C. Gomez-Quiles,
Amultilevel state estimation paradigm for smart grids, Proceedings of the
IEEE, vol. 99, pp. 952-976, 2011.
[15] Ben-Israel Adi and Greville Thomas N.E., Generalized Inverses, 2nd
Edition, Wiley-Interscience, Chpt. 2, Sec.4, 2003.
[16] G. Marsaglia and G. P. H. Styan. Equalities and inequalities for ranks
of matrices. Linear and Multi-linear Algebra, 269-292, 2 1974.
[17] R. W. Farebrother, Linear least squares computations, Marcel Dekker
INC, pp. 160, 1988.
[18] Muni S. Srivastava and Hirokazu Yanagihara, Testing the equality of
several covariance matrices with fewer observations than the dimension,
Elsevier, Journal of Multivariate Analysis 101,pp.1323, 2010.
[19] R.D. Zimmerman and C.E. Murillo-Sanchez. MATPOWER,
A MATLAB Power System Simulation Package.
http://www.pserc.cornell.edu/matpower/manual.pdf, September 2007.
@article{"International Journal of Information, Control and Computer Sciences:55689", author = "Morteza Talebi and Jianan Wang and Zhihua Qu", title = "Secure Power Systems Against Malicious Cyber-Physical Data Attacks: Protection and Identification", abstract = "The security of power systems against malicious cyberphysical
data attacks becomes an important issue. The adversary
always attempts to manipulate the information structure of the power
system and inject malicious data to deviate state variables while
evading the existing detection techniques based on residual test. The
solutions proposed in the literature are capable of immunizing the
power system against false data injection but they might be too costly
and physically not practical in the expansive distribution network.
To this end, we define an algebraic condition for trustworthy power
system to evade malicious data injection. The proposed protection
scheme secures the power system by deterministically reconfiguring
the information structure and corresponding residual test. More
importantly, it does not require any physical effort in either microgrid
or network level. The identification scheme of finding meters being
attacked is proposed as well. Eventually, a well-known IEEE 30-bus
system is adopted to demonstrate the effectiveness of the proposed
schemes.", keywords = "Algebraic Criterion, Malicious Cyber-Physical Data
Injection, Protection and Identification, Trustworthy Power System.", volume = "6", number = "6", pages = "780-8", }
