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一种安全且轻量级的信任评估模型,用于加强资源有限的工业WSNs的决策

Vinay Pathak¹, Karan Singh², Tayyab Khan¹

¹Department of Computer Science and Engineering, IIIT Sonepat, Khewra, Haryana, India.

Scientific reports

|November 15, 2024

概括

自然模型通过使用多层次的信任框架来检测和缓解不可靠的节点,提高准确性和安全性来提高工业无线传感器网络 (IWSN) 的可靠性.

科学领域:

计算机科学计算机科学
网络安全网络安全
工业自动化工业自动化

背景情况:

工业无线传感器网络 (IWSN) 中节点的可靠性对于准确的决策至关重要.
不可靠的节点可能导致错误的数据,操作失败,安全风险和安全漏洞.
现有的信任模型旨在缓解这些问题,但需要加强动态的工业环境.

研究的目的:

为IWSN引入一种用于信任利用和可靠性增强 (NATURE) 的新方法.
改善攻击缓解,减少通信开销,并在动态的工业环境中提高整体网络效率.
提高数据的准确性和加强IWSN的安全性.

主要方法:

自然采用一个多层次的集群模型,具有多因素信任估计框架.
它使用时间衰变因子,动态调整因子和可调节的逻辑时间窗口来准确评估信任.
最佳领先节点选择算法 (OLNEA) 和基于信任的攻击检测算法集成,以实现强大的网络操作和安全.

主要成果:

自然实现了高恶意节点检测准确度 (97%与30%恶意节点,91%与50%恶意节点).
该模型显著降低了能源消耗,并改善了与现有方案相比的吞吐率.
自然在具有挑战性和动态的工业网络条件下表现出卓越的性能.

结论:

通过其先进的信任管理系统,NATURE模型有效地提高了IWSN的可靠性和安全性.
它的多层次集群方法和动态信任评估机制非常适合动态的工业环境.
自然提供了一个强大的解决方案,以减轻不可靠节点的影响,并在IWSN中检测恶意活动.

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