Temporal convex combination‐based secure distributed estimation against cyberattacks and noisy input

This paper proposes a new secure distributed estimation for cyber‐physical systems against adversarial attacks with noisy input. To mitigate the effect of attacks, a novel distributed attack detection based on a reliable reference estimation obtained by temporal convex combination is proposed. Furth...

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Bibliographic Details
Published in:	Electronics letters 2024-03, Vol.60 (6), p.n/a
Main Authors:	Zhang, Zhanxi, Jia, Lijuan, Peng, Senran, Yang, Zi‐Jiang, Tao, Ran
Format:	Article
Language:	eng
Subjects:	adaptive estimation adaptive filters adaptive signal processing
Online Access:	Get full text
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Summary:	This paper proposes a new secure distributed estimation for cyber‐physical systems against adversarial attacks with noisy input. To mitigate the effect of attacks, a novel distributed attack detection based on a reliable reference estimation obtained by temporal convex combination is proposed. Furthermore, for more effective and robust performance, an adaptation rule to adjust convex combination weights is presented, in which the generalized correntropy method with nonlinear loss function and stochastic gradient descent are utilized. Besides, to eliminate input noise, a bias‐compensation method in local adaptation of the secure distributed estimation is proposed. Simulations show superior dynamic and real‐time adaptability of the proposed algorithm under complex attacking scenarios. This letter proposes a new secure distributed estimation for cyber‐physical systems against adversarial attacks with noisy input. To mitigate the effect of attacks, a novel adversarial detection method is proposed based on a reliable reference estimation obtained by temporal convex combination and a generalized correntropy based adaptation rule is presented to adjust convex combination weights. Besides, to eliminate input noise, a bias‐compensation method in local adaptation of the secure distributed estimation is proposed.
ISSN:	0013-5194 1350-911X