| 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 |
| DOI： |
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| 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). |
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| Reconstruction of measurements in state estimation strategy against deception attacks for cyber physical systems |
| Q. Li,Bugong XU,Shanbin LI,Yonggui LIU,Delong CUI |
| (School of Automation Science and Engineering, South China University of Technology, Guangzhou Guangdong 510640, China; College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming Guangdong 525000, 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. |
| Key words: State estimation, deception attacks, cyber physical systems, reconstruction of measurements, compressive sensing |
