Open Access

ARTICLE

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:

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

Received 22 March 2024; Accepted 28 June 2024; Issue published 15 October 2024

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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Keywords

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