Visible to the public Reconstruction of measurements in state estimation strategy against cyber attacks for cyber physical systems

TitleReconstruction of measurements in state estimation strategy against cyber attacks for cyber physical systems
Publication TypeConference Paper
Year of Publication2017
AuthorsLi, Q., Xu, B., Li, S., Liu, Y., Cui, D.
Conference Name2017 36th Chinese Control Conference (CCC)
ISBN Number978-988-15639-3-4
Keywordscomposability, compressed sensing, compressive sampling, compressive sensing, Cyber Attacks, cyber physical systems, Cyber-physical systems, Dictionaries, dictionary learning, K-singular value decomposition, K-SVD, machine learning, matrix algebra, Matrix decomposition, Observability, over-completed dictionary, Pollution measurement, Power systems, privacy, pubcrawl, Reconstruction of measurements, resilience, Resiliency, sampling matrix, security of data, singular value decomposition, state estimation

To improve the resilience of state estimation strategy against cyber attacks, the Compressive Sensing (CS) is applied in reconstruction of incomplete measurements for cyber physical systems. First, observability analysis is used to decide the time to run the 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). Besides, due to the irregularity of incomplete measurements, sampling matrix is designed as the measurement matrix. Finally, the simulation experiments on 6-bus power system illustrate that the proposed method achieves the incomplete measurements reconstruction perfectly, which is better than the joint dictionary. When only 29% available measurements are left, the proposed method has generality for four kinds of recovery algorithms.

Citation Keyli_reconstruction_2017