在工业物联网中基于深度学习的可靠和可靠的网络攻击检测
Fazlullah Khan1, Ryan Alturki2, Md Arafatur Rehman3
1Department of Computer Science, Abdul Wali Khan, University Mardan, Pakistan.
IEEE transactions on industrial informatics
|July 20, 2023
概括
本研究介绍了一种新的网络攻击检测方法,用于工业物联网 (IIoT) 网络,使用深度学习和集体决策树. 该方法提高了关键工业应用中的网络可靠性和安全性.
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) 网络对于关键任务需要高可靠性和可持续性.
- 由于协议差异和过时的调整,传统的安全机制对于IIoT是不够的.
- 新的方法对于加强对IIoT网络的安全性,隐私和信任至关重要.
研究的目的:
- 提出一种新的方法来提高IIoT支持网络的可信度.
- 为IIoT内部的监督控制和数据采集 (SCADA) 网络开发准确可靠的网络攻击检测方案.
- 加强IIoT环境中的安全和隐私机制.
主要方法:
- 开发了一种混合深度学习模型,将金字塔循环单元 (PRU) 和决策树 (DT) 结合起来.
- 在基于SCADA的IIoT网络中,采用集体学习方法来检测网络攻击.
- PRU的非线性学习和整体DT解决了对高检测率的特征敏感性.
主要成果:
- 与传统和现有的机器学习检测方法相比,拟议的方案显示出更高的性能.
- 高检测率是通过有效处理无关的特征来实现的.
- 这种方法显著提高了IIoT支持网络的安全性和可靠性.
结论:
- 基于PRU和ensemble DT的新型网络攻击检测方案为IIoT安全提供了强大的解决方案.
- 拟议的方法提高了SCADA网络在工业环境中的可靠性和可信度.
- 这项研究有助于保护关键的IIoT基础设施免受网络威胁.
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