一个基于边缘计算的集成框架用于网络流量分析和入侵检测,以增强工业物联网中的网络物理系统安全性
Tamara Zhukabayeva1,2, Zulfiqar Ahmad3, Aigul Adamova1,2
1Department of Information Systems, L.N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
本研究提出了一个工业物联网 (IIoT) 网络安全框架,使用边缘计算和机器学习进行实时网络流量分析和入侵检测. 该框架有效地识别威胁,增强IIoT安全性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 网络工程 网络工程
背景情况:
- 工业物联网 (IIoT) 环境面临越来越多的安全挑战,因为网络流量和连接性不断升级.
- 实施强有力的安全措施对于IIoT系统的可靠运行至关重要.
研究的目的:
- 开发和评估一个用于IIoT环境中的网络流量分析和入侵检测的新框架.
- 为了提高网络安全,利用边缘计算和机器学习技术.
主要方法:
- 利用k-means和DBSCAN集群来进行网络流量分析和异常检测.
- 采用k-最近邻居 (KNN),随机森林 (RF) 和后勤回归 (LR) 进行入侵检测.
- 集成边缘计算用于实时数据处理和降低延迟.
主要成果:
- 在交通行为聚类中,K-means (轮得分0.612) 优于DBSCAN (0.473).
- 随机森林和KNN在入侵检测方面展示了高精度,回忆和F1分数.
- 采用边缘计算的拟议框架实现了实时处理和降低安全系统延迟.
结论:
- 开发的框架通过提供实时威胁识别,有效地增强了IIoT的网络安全.
- 边缘计算和机器学习的整合比现有的安全方法提供了切实的改进.
- 这项研究为保护复杂的IIoT生态系统提供了有价值的见解.
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