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基于区块链的6G无线网络安全管理及机器学习技术优化

Blockchain 6G-Based Wireless Network Security Management with Optimization Using Machine Learning Techniques.

作者信息

Chinnasamy Ponnusamy, Babu G Charles, Ayyasamy Ramesh Kumar, Amutha S, Sinha Keshav, Balaram Allam

机构信息

Department of Computer Science and Engineering, School of Computing, Kalasalingam Academy of Research and Education, Srivilliputtur 626126, Tamil Nadu, India.

Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Bachupally, Hyderabad 500090, Telangana, India.

出版信息

Sensors (Basel). 2024 Sep 23;24(18):6143. doi: 10.3390/s24186143.

DOI:10.3390/s24186143
PMID:39338888
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11435844/
Abstract

6G mobile network technology will set new standards to meet performance goals that are too ambitious for 5G networks to satisfy. The limitations of 5G networks have been apparent with the deployment of more and more 5G networks, which certainly encourages the investigation of 6G networks as the answer for the future. This research includes fundamental privacy and security issues related to 6G technology. Keeping an eye on real-time systems requires secure wireless sensor networks (WSNs). Denial of service (DoS) attacks mark a significant security vulnerability that WSNs face, and they can compromise the system as a whole. This research proposes a novel method in blockchain 6G-based wireless network security management and optimization using a machine learning model. In this research, the deployed 6G wireless sensor network security management is carried out using a blockchain user datagram transport protocol with reinforcement projection regression. Then, the network optimization is completed using artificial democratic cuckoo glowworm remora optimization. The simulation results have been based on various network parameters regarding throughput, energy efficiency, packet delivery ratio, end-end delay, and accuracy. In order to minimise network traffic, it also offers the capacity to determine the optimal node and path selection for data transmission. The proposed technique obtained 97% throughput, 95% energy efficiency, 96% accuracy, 50% end-end delay, and 94% packet delivery ratio.

摘要

6G移动网络技术将设定新标准,以实现5G网络难以企及的性能目标。随着越来越多的5G网络投入使用,5G网络的局限性日益明显,这无疑促使人们对6G网络展开研究,将其视为未来的解决方案。这项研究涵盖了与6G技术相关的基本隐私和安全问题。实时系统需要安全的无线传感器网络(WSN)。拒绝服务(DoS)攻击是WSN面临的一个重大安全漏洞,可能会使整个系统陷入瘫痪。本研究提出了一种基于区块链的6G无线网络安全管理与优化的新方法,采用机器学习模型。在本研究中,所部署的6G无线传感器网络安全管理是通过带有强化投影回归的区块链用户数据报传输协议来进行的。然后,利用人工民主布谷鸟萤火虫吸盘鱼优化算法完成网络优化。仿真结果基于吞吐量、能效、数据包传输率、端到端延迟和准确率等各种网络参数。为了最大限度地减少网络流量,它还具备确定数据传输的最佳节点和路径选择的能力。所提出的技术实现了97%的吞吐量、95%的能效、96%的准确率、50%的端到端延迟和94%的数据包传输率。

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An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks.一种优化的机器学习技术方案及其在无线传感器网络故障检测中的应用。
J Appl Stat. 2021 May 26;50(3):592-609. doi: 10.1080/02664763.2021.1929089. eCollection 2023.
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Finite-Time-Prescribed Performance-Based Adaptive Fuzzy Control for Strict-Feedback Nonlinear Systems With Dynamic Uncertainty and Actuator Faults.具有动态不确定性和执行器故障的严格反馈非线性系统的基于有限时间规定性能的自适应模糊控制
IEEE Trans Cybern. 2022 Jul;52(7):6959-6971. doi: 10.1109/TCYB.2020.3046316. Epub 2022 Jul 4.
用于智能家居中基于无线传感器网络和时移分析的预测性温度控制的人工智能驱动的区块链框架。
Sci Rep. 2025 May 25;15(1):18168. doi: 10.1038/s41598-025-03146-w.
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Optimizing data privacy and security measures for critical infrastructures via IoT based ADP2S technique.通过基于物联网的ADP2S技术优化关键基础设施的数据隐私和安全措施。
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