Gaussian Filtering with False Data Injection and Randomly Delayed Measurements
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020599" target="_blank" >RIV/62690094:18450/23:50020599 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10217812/" target="_blank" >https://ieeexplore.ieee.org/document/10217812/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2023.3305288" target="_blank" >10.1109/ACCESS.2023.3305288</a>
Alternative languages
Result language
angličtina
Original language name
Gaussian Filtering with False Data Injection and Randomly Delayed Measurements
Original language description
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. Author
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
11
Issue of the periodical within the volume
Autumn
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
88637-88648
UT code for WoS article
001055265300001
EID of the result in the Scopus database
2-s2.0-85168275859