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堆叠集团深度学习用于实时入侵检测在IoMT环境中的IoMT环境.

Easa Alalwany¹, Bader Alsharif^2,3, Yazeed Alotaibi⁴

¹College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.

Sensors (Basel, Switzerland)

|February 13, 2025

概括

此摘要是机器生成的。

一个新的入侵检测系统 (IDS) 使用机器学习和深度学习来保护医疗物联网 (IoMT). 这种先进的网络安全解决方案有效地检测和分类连接医疗保健设备上的网络攻击.

关键词:

医疗事物的互联网 (IoMT)卡帕建筑公司卡帕建筑公司医疗保健中的网络安全.侵入检测系统 (IDS) 是一种入侵检测系统.机器和深度学习在IoMT安全方面.堆叠方法堆叠方法

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科学领域:

医疗保健技术中的网络安全
网络入侵检测系统网络入侵检测系统
在 IoMT 中的机器学习应用程序.

背景情况:

医疗物联网 (IoMT) 集成了相互连接的设备,用于先进的患者护理.
IoMT系统处理敏感数据,使其易受网络威胁.
有效的安全解决方案对于IoMT网络完整性至关重要.

研究的目的:

开发一种针对IoMT网络量身定制的新型入侵检测系统 (IDS).
通过利用机器学习和深度学习技术来增强网络安全.
确保在IoMT环境中实时检测和分类威胁.

主要方法:

实现一个堆叠组合方法,结合多个机器学习和深度学习分类器.
使用Kappa架构框架进行IoMT数据流的连续处理.
开发一种能够检测和分类各种网络攻击的IDS,如ARP伪造,DoS,Smurf和端口扫描.

主要成果:

拟议的IDS实现了高检测准确性:在二进制分类中为0.991,在多类分类中为0.993.
该系统通过Kappa架构展示了有效的实时性能.
我们成功地检测和分类了各种类型的网络攻击.

结论:

结合先进的ML,DL和集体学习,为IoMT网络安全提供了强大的解决方案.
开发的IDS提供了一种可靠和可扩展的方法来保护医疗保健服务.
这项研究解决了在不断变化的IoMT环境中的关键安全挑战.