TY - JOUR
T1 - Gaussian Filtering With False Data Injection and Randomly Delayed Measurements
AU - Nanda, Sumanta Kumar
AU - Kumar, Guddu
AU - Naik, Amit Kumar
AU - Abdel-Hafez, Mohammed
AU - Bhatia, Vimal
AU - Krejcar, Ondrej
AU - Singh, Abhinoy Kumar
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2023
Y1 - 2023
N2 - State estimation in cyber-physical systems is a challenging task involving integrating physical models and measurements to estimate dynamic states accurately in practical machine-to-machine and IoT deployments. However, integrating advanced wireless communication and intelligent measurements has increased vulnerability of external intrusion through a centralized server. This study addresses the challenge of Gaussian filtering for a specific type of stochastic nonlinear system vulnerable to cyber attacks and delayed measurements. These attacks occur randomly when data is transmitted from sensor nodes to remote filter nodes. To address this issue, a new cyber attack model is proposed that combines false data injection attacks and delayed measurement into a unified framework. The study also analyzes the stochastic stability of the proposed filter and establishes sufficient conditions to ensure that the filtering error remains bounded even in the presence of randomly occurring cyber attacks and delayed measurements. The proposed methodology is demonstrated and compared with other widely used approaches using simulated data to highlight its effectiveness and usefulness.
AB - State estimation in cyber-physical systems is a challenging task involving integrating physical models and measurements to estimate dynamic states accurately in practical machine-to-machine and IoT deployments. However, integrating advanced wireless communication and intelligent measurements has increased vulnerability of external intrusion through a centralized server. This study addresses the challenge of Gaussian filtering for a specific type of stochastic nonlinear system vulnerable to cyber attacks and delayed measurements. These attacks occur randomly when data is transmitted from sensor nodes to remote filter nodes. To address this issue, a new cyber attack model is proposed that combines false data injection attacks and delayed measurement into a unified framework. The study also analyzes the stochastic stability of the proposed filter and establishes sufficient conditions to ensure that the filtering error remains bounded even in the presence of randomly occurring cyber attacks and delayed measurements. The proposed methodology is demonstrated and compared with other widely used approaches using simulated data to highlight its effectiveness and usefulness.
KW - Delay measurement
KW - FDI
KW - Gaussian filtering
KW - nonlinear Bayesian filtering
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U2 - 10.1109/ACCESS.2023.3305288
DO - 10.1109/ACCESS.2023.3305288
M3 - Article
AN - SCOPUS:85168275859
SN - 2169-3536
VL - 11
SP - 88637
EP - 88648
JO - IEEE Access
JF - IEEE Access
ER -