Adaptive Update Distribution Estimation under Probability Byzantine Attack
作者机构：Faculty of Computing Harbin Institute of Technology Harbin 150000 China
出 版 物：《Computers, Materials and Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2024年第81卷第1期
页 面：1667-1685页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
摘 要：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. © 2024 The Authors. Published by Tech Science Press.
维普期刊数据库