On the security of distributed power system state estimation under targeted attacks

State estimation plays an essential role in the monitoring and control of power transmission systems. In modern, highly inter-connected power systems the state estimation should be performed in a distributed fashion and requires information exchange between the control centers of directly connected systems. Motivated by recent reports on trojans targeting industrial control systems, in this paper we investigate how a single compromised control center can affect the outcome of distributed state estimation. We describe five attack strategies, and evaluate their impact on the IEEE 118 benchmark power system. We show that that even if the state estimation converges despite the attack, the estimate can have up to 30% of error, and bad data detection cannot locate the attack. We also show that if powerful enough, the attack can impede the convergence of the state estimation, and thus it can blind the system operators. Our results show that it is important to provide confidentiality for the measurement data in order to prevent the most powerful attacks. Finally, we discuss a possible way to detect and to mitigate these attacks.

[1]  Kameshwar Poolla,et al.  Smart grid data integrity attacks: characterizations and countermeasuresπ , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[2]  Le Xie,et al.  Fully distributed bad data processing for wide area state estimation , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[3]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[4]  Henrik Sandberg,et al.  Stealth Attacks and Protection Schemes for State Estimators in Power Systems , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[5]  Tim Dierks,et al.  The Transport Layer Security (TLS) Protocol Version 1.2 , 2008 .

[6]  H. Vincent Poor,et al.  Strategic Protection Against Data Injection Attacks on Power Grids , 2011, IEEE Transactions on Smart Grid.

[7]  Mohammad Shahidehpour,et al.  Communication and Control in Electric Power Systems: Applications of Parallel and Distributed Processing , 2003 .

[8]  Peng Ning,et al.  False data injection attacks against state estimation in electric power grids , 2009, CCS.

[9]  H. Poor,et al.  Fully Distributed State Estimation for Wide-Area Monitoring Systems , 2012, IEEE Transactions on Smart Grid.

[10]  Eric Chien,et al.  W32.Duqu: The Precursor to the Next Stuxnet , 2012, LEET.

[11]  A. G. Expósito,et al.  Power system state estimation : theory and implementation , 2004 .

[12]  L. Tong,et al.  Malicious Data Attacks on Smart Grid State Estimation: Attack Strategies and Countermeasures , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[13]  A. Monticelli,et al.  Electric power system state estimation , 2000, Proceedings of the IEEE.

[14]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[15]  A.J. Conejo,et al.  An Optimization Approach to Multiarea State Estimation , 2007, IEEE Transactions on Power Systems.

[16]  H. Vincent Poor,et al.  Competitive privacy in the smart grid: An information-theoretic approach , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[17]  Karl Henrik Johansson,et al.  A Cyber Security Study of a SCADA Energy Management System: Stealthy Deception Attacks on the State Estimator , 2010, ArXiv.

[18]  Henrik Sandberg,et al.  Network-Aware Mitigation of Data Integrity Attacks on Power System State Estimation , 2012, IEEE Journal on Selected Areas in Communications.

[19]  Klara Nahrstedt,et al.  Detecting False Data Injection Attacks on DC State Estimation , 2010 .