Data Framing Attack on State Estimation

A new mechanism aimed at misleading a power system control center about the source of a data attack is proposed. As a man-in-the-middle state attack, a data framing attack is proposed to exploit the bad data detection and identification mechanisms currently in use at most control centers. In particular, the proposed attack frames meters that are providing correct data as sources of bad data such that the control center will remove useful measurements that would otherwise be used by the state estimator. The optimal design of a data framing attack is formulated as a quadratically constrained quadratic program. It is shown that the proposed attack is capable of perturbing the power system state estimate by an arbitrary degree controlling only half of a critical set of measurements that are needed to make a system unobservable. Implications of this attack on power system operations are discussed, and the attack performance is evaluated using benchmark systems.

[1]  G. Krumpholz,et al.  Power System Observability: A Practical Algorithm Using Network Topology , 1980, IEEE Transactions on Power Apparatus and Systems.

[2]  A. Monticelli State estimation in electric power systems : a generalized approach , 1999 .

[3]  Ali Abur,et al.  A robust WLAV state estimator using transformations , 1992 .

[4]  H. Vincent Poor,et al.  Distributed joint cyber attack detection and state recovery in smart grids , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[5]  G. Contaxis,et al.  Identification and updating of minimally dependent sets of measurements in state estimation , 1991 .

[6]  Lang Tong,et al.  Malicious Data Attacks on the Smart Grid , 2011, IEEE Transactions on Smart Grid.

[7]  David R. Karger,et al.  A new approach to the minimum cut problem , 1996, JACM.

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

[9]  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.

[10]  A. Monticelli,et al.  Multiple Bad Data Detectability and Identifiability: A Geometric Approach , 1986, IEEE Transactions on Power Delivery.

[11]  M. Vidyasagar,et al.  Bad Data Rejection Properties of Weughted Least Absolute Value Techniques Applied to Static State Estimation , 1982, IEEE Transactions on Power Apparatus and Systems.

[12]  Rong Zheng,et al.  Bad data injection in smart grid: attack and defense mechanisms , 2013, IEEE Communications Magazine.

[13]  Zhu Han,et al.  Defending false data injection attack on smart grid network using adaptive CUSUM test , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[14]  Ying Jun Zhang,et al.  Defending mechanisms against false-data injection attacks in the power system state estimation , 2011, 2011 IEEE GLOBECOM Workshops (GC Wkshps).

[15]  E. Handschin,et al.  Bad data analysis for power system state estimation , 1975, IEEE Transactions on Power Apparatus and Systems.

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

[17]  M. Ribbens-Pavella,et al.  Hypothesis Testing Identification: A New Method for Bad Data Analysis in Power System State Estimation , 1984, IEEE Power Engineering Review.

[18]  Felix F. Wu,et al.  Network Observability: Theory , 1985, IEEE Power Engineering Review.

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

[20]  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.

[21]  Lamine Mili,et al.  Robust state estimation of electric power systems , 1994 .

[22]  Thomas J. Overbye,et al.  Topology Perturbation for Detecting Malicious Data Injection , 2012, 2012 45th Hawaii International Conference on System Sciences.

[23]  Zhu Han,et al.  Coordinated data-injection attack and detection in the smart grid: A detailed look at enriching detection solutions , 2012, IEEE Signal Processing Magazine.

[24]  Felix Wu,et al.  Network Observability: Identification of Observable Islands and Measurement Placement , 1985, IEEE Transactions on Power Apparatus and Systems.

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

[26]  A. Abur,et al.  A fast algorithm for the weighted least absolute value state estimation (for power systems) , 1991 .

[27]  Karl Henrik Johansson,et al.  On Security Indices for State Estimators in Power Networks , 2010 .

[28]  Fernando L. Alvarado,et al.  Weighted Least Absolute Value state estimation using interior point methods , 1994 .

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

[30]  Lang Tong,et al.  On topology attack of a smart grid , 2013, 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT).

[31]  M. Ribbens-Pavella,et al.  Bad Data Identification Methods In Power System State Estimation-A Comparative Study , 1985, IEEE Transactions on Power Apparatus and Systems.

[32]  Lang Tong,et al.  On Topology Attack of a Smart Grid: Undetectable Attacks and Countermeasures , 2013, IEEE Journal on Selected Areas in Communications.

[33]  Lamine Mili,et al.  Identification of multiple interacting bad data via power system decomposition , 1996 .

[34]  A. Monticelli,et al.  Reliable Bad Data Processing for Real-Time State Estimation , 1983, IEEE Power Engineering Review.

[35]  Henrik Sandberg,et al.  Network-layer protection schemes against stealth attacks on state estimators in power systems , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).