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Adaptive Update Distribution Estimation under Probability Byzantine Attack

Gang Long, Zhaoxin Zhang*

Faculty of Computing, Harbin Institute of Technology, Harbin, 150000, China

* Corresponding Author: Zhaoxin Zhang. Email: email

Computers, Materials & Continua 2024, 81(1), 1667-1685. https://doi.org/10.32604/cmc.2024.052082

Abstract

The secure and normal operation of distributed networks is crucial for accurate parameter estimation. However, distributed networks are frequently susceptible to Byzantine attacks. Considering real-life scenarios, this paper investigates a probability Byzantine (PB) attack, utilizing a Bernoulli distribution to simulate the attack probability. Historically, additional detection mechanisms are used to mitigate such attacks, leading to increased energy consumption and burdens on distributed nodes, consequently diminishing operational efficiency. Differing from these approaches, an adaptive updating distributed estimation algorithm is proposed to mitigate the impact of PB attacks. In the proposed algorithm, a penalty strategy is initially incorporated during data updates to weaken the influence of the attack. Subsequently, an adaptive fusion weight is employed during data fusion to merge the estimations. Additionally, the reason why this penalty term weakens the attack has been analyzed, and the performance of the proposed algorithm is validated through simulation experiments.

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Cite This Article

APA Style
Long, G., Zhang, Z. (2024). Adaptive update distribution estimation under probability byzantine attack. Computers, Materials & Continua, 81(1), 1667-1685. https://doi.org/10.32604/cmc.2024.052082
Vancouver Style
Long G, Zhang Z. Adaptive update distribution estimation under probability byzantine attack. Comput Mater Contin. 2024;81(1):1667-1685 https://doi.org/10.32604/cmc.2024.052082
IEEE Style
G. Long and Z. Zhang, “Adaptive Update Distribution Estimation under Probability Byzantine Attack,” Comput. Mater. Contin., vol. 81, no. 1, pp. 1667-1685, 2024. https://doi.org/10.32604/cmc.2024.052082



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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