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Reconstruction of measurements in state estimation strategy against deception attacks for cyber physical systems
作者姓名:Qinxue LI  Bugong XU  Shanbin LI  Yonggui LIU  Delong CUI
作者单位:School of Automation Science and Engineering, South China University of Technology, Guangzhou Guangdong 510640, China
基金项目:This work was supported by the Natural Science Foundation of China (NSFC) Guangdong Joint Foundation Key Project (No. U1401253), the NSFC (Nos. 61573153, 61672174), the Foundation of Guangdong Provincial Science and Technology Projects (No. 2013B010401001), the Fundamental Research Funds for the Central Universities (No. 2015ZZ099), the Guangzhou Science and Technology Plan Project (No. 201510010132), the Maoming Science and Technology Plan Project (No. MM2017000004), and the National Natural Science Foundation of Guangdong Province (No. 2016A030313510).
摘    要:Without the known state equation, a new state estimation strategy is designed to be against malicious attacks for cyber physical systems. Inspired by the idea of data reconstruction, the compressive sensing (CS) is applied to reconstruction of residual measurements after the detection and identification scheme based on the Markov graph of the system state, which increases the resilience of state estimation strategy against deception attacks. First, the observability analysis is introduced to decide the triggering time of the measurement reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over completed dictionary by K singular value decomposition (K SVD), which is produced adaptively according to the characteristics of the measurement data. In addition, due to the irregularity of residual measurements, a sampling matrix is designed as the measurement matrix. Finally, the simulation experiments are performed on 6 bus power system. Results show that the reconstruction of measurements is completed well by the proposed reconstruction method, and the corresponding effects are better than reconstruction scheme based on the joint dictionary and the traditional Gauss or Bernoulli random matrix respectively. Especially, when only 29% available clean measurements are left, performance of the proposed strategy is still extraordinary, which reflects generality for five kinds of recovery algorithms.

关 键 词:State  estimation    deception  attacks    cyber  physical  systems    reconstruction  of  measurements    compressive  sensing

Reconstruction of measurements in state estimation strategy against deception attacks for cyber physical systems
Qinxue LI,Bugong XU,Shanbin LI,Yonggui LIU,Delong CUI.Reconstruction of measurements in state estimation strategy against deception attacks for cyber physical systems[J].Journal of Control Theory and Applications,2018,16(1):1-13.
Authors:Q Li  Bugong XU  Shanbin LI  Yonggui LIU and Delong CUI
Institution:School of Automation Science and Engineering, South China University of Technology, Guangzhou Guangdong 510640, China
Abstract:Without the known state equation, a new state estimation strategy is designed to be against malicious attacks for cyber physical systems. Inspired by the idea of data reconstruction, the compressive sensing (CS) is applied to reconstruction of residual measurements after the detection and identification scheme based on the Markov graph of the system state, which increases the resilience of state estimation strategy against deception attacks. First, the observability analysis is introduced to decide the triggering time of the measurement reconstruction and the damage level from attacks. In particular, the dictionary learning is proposed to form the over completed dictionary by K singular value decomposition (K SVD), which is produced adaptively according to the characteristics of the measurement data. In addition, due to the irregularity of residual measurements, a sampling matrix is designed as the measurement matrix. Finally, the simulation experiments are performed on 6 bus power system. Results show that the reconstruction of measurements is completed well by the proposed reconstruction method, and the corresponding effects are better than reconstruction scheme based on the joint dictionary and the traditional Gauss or Bernoulli random matrix respectively. Especially, when only 29% available clean measurements are left, performance of the proposed strategy is still extraordinary, which reflects generality for five kinds of recovery algorithms.
Keywords:State estimation  deception attacks  cyber physical systems  reconstruction of measurements  compressive sensing
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