一个新的医疗物联网混合模型,用于网络安全异常检测
Mohammad Zubair Khan1,2, Abdulhakim Sabur2,3, Hamza Ghandorh4
1Department of Computer Science and Information, Applied College, Taibah University, Madinah 42353, Saudi Arabia.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
医疗物联网 (IoMT) 设备需要增强安全性. 使用图形卷积网络和变压器的新混合模型有效检测网络攻击,保护患者数据和设备完整性.
科学领域:
- 网络安全 网络安全
- 医疗信息学 医疗信息学
- 网络安全 网络安全
背景情况:
- 医疗物联网 (IoMT) 设备收集敏感的患者数据.
- 对IoMT设备的安全性不足使它们容易受到网络攻击.
- 攻击可能会损害数据完整性和患者安全.
研究的目的:
- 为IoMT网络开发一种新的混合异常检测模型.
- 提高IoMT设备和患者数据的安全性和安全性.
- 解决IoMT设备对数据完整性攻击的脆弱性.
主要方法:
- 这是一个混合模型,结合了图形卷积网络 (GCN) 和变压器架构.
- 在IoMT数据中,GCN捕捉了结构关系.
- 变压器模型在异常中的顺序依赖.
- 使用CICIOMT24数据集与真实世界IoMT流量和网络攻击进行评估.
主要成果:
- 混合模型有效地检测到IoMT网络上的各种网络攻击.
- 展示了早期检测能力.
- 优于基线机器学习模型的表现,如物流回归,随机森林和自适应提升.
结论:
- 拟议的混合模型为IoMT安全提供了一个可扩展和强大的解决方案.
- 提高关键患者数据的安全性和安全性.
- 解决IoMT设备对网络威胁的脆弱性日益增长的挑战.
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