Detecting False Data Injection Attacks in AC State Estimation
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[1] Peng Ning,et al. False data injection attacks against state estimation in electric power grids , 2011, TSEC.
[2] Zhihua Qu,et al. Enhanced protection against false data injection by dynamically changing information structure of microgrids , 2012, 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM).
[3] N. Amjady,et al. A New Stochastic Search Technique Combined With Scenario Approach for Dynamic State Estimation of Power Systems , 2012, IEEE Transactions on Power Systems.
[4] A. G. Expósito,et al. Power system state estimation : theory and implementation , 2004 .
[5] A. Monticelli. State estimation in electric power systems : a generalized approach , 1999 .
[6] Nitish V. Thakor,et al. Describing the Nonstationarity Level of Neurological Signals Based on Quantifications of TimeFreque , 2007 .
[7] Linqiang Ge,et al. A novel architecture against false data injection attacks in smart grid , 2012, 2012 IEEE International Conference on Communications (ICC).
[8] Zhu Han,et al. Detecting False Data Injection Attacks on Power Grid by Sparse Optimization , 2014, IEEE Transactions on Smart Grid.
[9] Hui Lin,et al. Generalized Time-Series Active Search With Kullback–Leibler Distance for Audio Fingerprinting , 2006, IEEE Signal Processing Letters.
[10] G. Manimaran,et al. Vulnerability Assessment of Cybersecurity for SCADA Systems , 2008, IEEE Transactions on Power Systems.
[11] Lang Tong,et al. Limiting false data attacks on power system state estimation , 2010, 2010 44th Annual Conference on Information Sciences and Systems (CISS).
[12] Lang Tong,et al. On malicious data attacks on power system state estimation , 2010, 45th International Universities Power Engineering Conference UPEC2010.
[13] Minh N. Do,et al. Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance , 2002, IEEE Trans. Image Process..
[14] Gabriela Hug,et al. Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks , 2012, IEEE Transactions on Smart Grid.
[15] Philip Chan,et al. Learning States and Rules for Time Series Anomaly Detection , 2004, FLAIRS.
[16] Dipankar Dasgupta,et al. Novelty detection in time series data using ideas from immunology , 1996 .
[17] Steven W. Blume,et al. System Control Centers and Telecommunications , 2007 .
[18] Chen-Ching Liu,et al. Intruders in the Grid , 2012, IEEE Power and Energy Magazine.
[19] 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).
[20] A. Khatkhate,et al. Symbolic time-series analysis for anomaly detection in mechanical systems , 2006, IEEE/ASME Transactions on Mechatronics.
[21] Klara Nahrstedt,et al. Detecting False Data Injection Attacks on DC State Estimation , 2010 .
[22] 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.
[23] Siddharth Sridhar,et al. Cyber–Physical System Security for the Electric Power Grid , 2012, Proceedings of the IEEE.