On Optimal PMU Placement-Based Defense Against Data Integrity Attacks in Smart Grid

State estimation plays a critical role in self-detection and control of the smart grid. Data integrity attacks (also known as false data injection attacks) have shown significant potential in undermining the state estimation of power systems, and corresponding countermeasures have drawn increased scholarly interest. Nonetheless, leveraging optimal phasor measurement unit (PMU) placement to defend against these attacks, while simultaneously ensuring the system observability, has yet to be addressed without incurring significant overhead. In this paper, we enhance the least-effort attack model, which computes the minimum number of sensors that must be compromised to manipulate a given number of states, and develop an effective greedy algorithm for optimal PMU placement to defend against data integrity attacks. Regarding the least-effort attack model, we prove the existence of smallest set of sensors to compromise and propose a feasible reduced row echelon form (RRE)-based method to efficiently compute the optimal attack vector. Based on the IEEE standard systems, we validate the efficiency of the RRE algorithm, in terms of a low computation complexity. Regarding the defense strategy, we propose an effective PMU-based greedy algorithm, which cannot only defend against data integrity attacks, but also ensure the system observability with low overhead. The experimental results obtained based on various IEEE standard systems show the effectiveness of the proposed defense scheme against data integrity attacks.

[1]  Xinyu Yang,et al.  Towards optimal PMU placement against data integrity attacks in smart grid , 2016, 2016 Annual Conference on Information Science and Systems (CISS).

[2]  Yang Liu,et al.  Abnormal traffic-indexed state estimation: A cyber-physical fusion approach for Smart Grid attack detection , 2015, Future Gener. Comput. Syst..

[3]  Xinyu Yang,et al.  On data integrity attacks against optimal power flow in power grid systems , 2017, 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[4]  M.M. Ahmed,et al.  Supervisory Control and Data Acquisition System (SCADA) based customized Remote Terminal Unit (RTU) for distribution automation system , 2008, 2008 IEEE 2nd International Power and Energy Conference.

[5]  Rui Li,et al.  Towards a Low-Cost Remote Memory Attestation for the Smart Grid , 2015, Sensors.

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

[7]  Xinyu Yang,et al.  Data integrity attacks against the distributed real-time pricing in the smart grid , 2016, 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC).

[8]  Akiko Takeda,et al.  Cyber Security Analysis of Power Networks by Hypergraph Cut Algorithms , 2015, IEEE Transactions on Smart Grid.

[9]  Wei Yu,et al.  On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures , 2014, IEEE Transactions on Parallel and Distributed Systems.

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

[11]  György Dán,et al.  Security of Fully Distributed Power System State Estimation: Detection and Mitigation of Data Integrity Attacks , 2014, IEEE Journal on Selected Areas in Communications.

[12]  Young-Jin Kim,et al.  A secure decentralized data-centric information infrastructure for smart grid , 2010, IEEE Communications Magazine.

[13]  Jiang Xin-li,et al.  Current situation and development of the methods on bad-data detection and identification of power system , 2010 .

[14]  Haibo He,et al.  Resilience Analysis of Power Grids Under the Sequential Attack , 2014, IEEE Transactions on Information Forensics and Security.

[15]  Karl Henrik Johansson,et al.  On the Exact Solution to a Smart Grid Cyber-Security Analysis Problem , 2011, IEEE Transactions on Smart Grid.

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

[17]  Seema Singh,et al.  Optimal PMU placement method for complete topological and numerical observability of power system , 2010 .

[18]  Karl Henrik Johansson,et al.  Electric power network security analysis via minimum cut relaxation , 2011, IEEE Conference on Decision and Control and European Control Conference.

[19]  Xinyu Yang,et al.  Towards Multistep Electricity Prices in Smart Grid Electricity Markets , 2016, IEEE Transactions on Parallel and Distributed Systems.

[20]  Xiaodong Wang,et al.  Quickest Detection of False Data Injection Attack in Wide-Area Smart Grids , 2015, IEEE Transactions on Smart Grid.

[21]  A.G. Phadke,et al.  Phasor measurement unit placement techniques for complete and incomplete observability , 2005, IEEE Transactions on Power Delivery.

[22]  Wen-Long Chin,et al.  Blind False Data Injection Attack Using PCA Approximation Method in Smart Grid , 2015, IEEE Transactions on Smart Grid.

[23]  Zuyi Li,et al.  Local Load Redistribution Attacks in Power Systems With Incomplete Network Information , 2014, IEEE Transactions on Smart Grid.

[24]  H.M. Ismail,et al.  A Unified Approach for the Optimal PMU Location for Power System State Estimation , 2009, IEEE Transactions on Power Systems.

[25]  Igor Djurovic,et al.  Optimal placement of PMUs with limited number of channels , 2012 .

[26]  B. Gou,et al.  Unified PMU Placement for Observability and Bad Data Detection in State Estimation , 2014, IEEE Transactions on Power Systems.

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

[28]  Wei Yu,et al.  On false data injection attacks against Kalman filtering in power system dynamic state estimation , 2016, Secur. Commun. Networks.

[29]  Xinyu Yang,et al.  On False Data Injection Attacks against Distributed Energy Routing in Smart Grid , 2012, 2012 IEEE/ACM Third International Conference on Cyber-Physical Systems.

[30]  Karl Henrik Johansson,et al.  Efficient Computations of a Security Index for False Data Attacks in Power Networks , 2012, IEEE Transactions on Automatic Control.

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

[32]  Yun Gu,et al.  Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids , 2014 .

[33]  Russell Bent,et al.  Phasor measurement unit selection for unobservable electric power data integrity attack detection , 2014, Int. J. Crit. Infrastructure Prot..

[34]  Bruno Sinopoli,et al.  Detecting Integrity Attacks on SCADA Systems , 2011 .

[35]  Zahir Tari,et al.  An Efficient Data-Driven Clustering Technique to Detect Attacks in SCADA Systems , 2016, IEEE Transactions on Information Forensics and Security.

[36]  Jinping Hao,et al.  Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids , 2015, IEEE Transactions on Industrial Informatics.

[37]  Kit Po Wong,et al.  Forecasting-Aided Imperfect False Data Injection Attacks Against Power System Nonlinear State Estimation , 2016, IEEE Transactions on Smart Grid.

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

[39]  Guo Zhizhong,et al.  PMU placement and data processing in WAMS that complements SCADA , 2005, IEEE Power Engineering Society General Meeting, 2005.

[40]  Xinyu Yang,et al.  Toward a Gaussian-Mixture Model-Based Detection Scheme Against Data Integrity Attacks in the Smart Grid , 2017, IEEE Internet Things J..

[41]  David Stephen.  Energy management systems , 1994 .

[42]  Nada Golmie,et al.  An integrated detection system against false data injection attacks in the Smart Grid , 2015, Secur. Commun. Networks.

[43]  A. Abur,et al.  Placement of PMUs to Enable Bad Data Detection in State Estimation , 2006, IEEE Transactions on Power Systems.