Neural-Network-Based Secure State Estimation Under Energy-Constrained Denial-of-Service Attacks: An Encoding-Decoding Scheme

This paper is concerned with the secure state estimation issue for a class of networked nonlinear systems under energy-constrained denial-of-service (EC-DoS) cyber-attacks and encoding-decoding scheme (EDS). The information transmissions between sensors and the estimator are executed via a bandwidth...

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Veröffentlicht in:IEEE transactions on network science and engineering Jg. 10; H. 4; S. 1 - 14
Hauptverfasser: Zhang, Yuhan, Wang, Zidong, Zou, Lei, Dong, Hongli, Yi, Xiaojian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Artificial neural networks
Constraints
Cybersecurity
Denial of service attacks
Denial-of-service attack
Dynamic stability
Encoding
Encoding-Decoding
encoding-decoding scheme
Estimation
Networked nonlinear systems
Neural networks
Nonlinear systems
Sensors
State estimation
Transmission efficiency
ISSN:2327-4697, 2334-329X
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Zusammenfassung:This paper is concerned with the secure state estimation issue for a class of networked nonlinear systems under energy-constrained denial-of-service (EC-DoS) cyber-attacks and encoding-decoding scheme (EDS). The information transmissions between sensors and the estimator are executed via a bandwidth-limited communication network, on which the EDS is deployed to convert transmitted signals into finite-length codewords for the purpose of improving transmission efficiency. The EC-DoS attacks, whose intention is to jeopardize the network-based signal transmissions by overloading the communication resource, are assumed to occur in an intermittent way with bounded occurrence frequency/durations owing to the inherent energy constraints on the attackers. Considering the worst case of such EC-DoS attacks, a neural-network (NN)-based state estimator is constructed to generate the desired state estimates for the underlying networked nonlinear system. By employing the Lyapunov stability theory, the estimation error dynamics of the system state and the neural-network weight are jointly analyzed within a unified framework. Subsequently, sufficient conditions are obtained for the existence of the desired NN-based state estimator, and then both the desired estimator gain matrix and the NN tuning parameters are characterized. Finally, the validity of our estimation approach is confirmed by an example.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2327-4697
2334-329X
DOI:10.1109/TNSE.2023.3237639