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加强医疗保健的网络安全:评估集体学习模型,用于医疗物联网的入侵检测
Theyab Alsolami1,2, Bader Alsharif1,3, Mohammad Ilyas1
1Department of Electrical Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA.
机器学习模型显著提高了医疗物联网的网络安全. 堆叠组合学习在检测网络攻击方面实现了98.88%的准确性,为敏感的医疗数据提供了强大的保护.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 医疗事物的互联网 医疗事物的互联网
背景情况:
- 医疗物联网 (IoMT) 正在扩大,增加了敏感医疗数据的攻击面.
- 强有力的网络安全措施对于保护患者隐私和确保医疗器械完整性至关重要.
研究的目的:
- 评估整体机器学习算法在IoMT环境中对入侵检测的有效性.
- 为了比较堆叠,包装和增强组合方法在识别网络威胁方面的性能.
主要方法:
- 利用WUSTL-EHMS-2020数据集进行入侵检测模型的培训和测试.
- 实施集体学习技巧:堆叠,包装和提升.
- 采用随机森林和支持矢量机器作为整体框架内的基本模型.
主要成果:
- 堆叠集体学习在检测和分类网络攻击方面达到最高准确率98.88%.
- 包装组合学习表现出强的表现,准确率为97.83%.
- 与堆叠和包装相比,增强组合学习的准确率较低,为88.68%.
结论:
- 整体机器学习,特别是堆叠方法,为IoMT的入侵检测提供了一个非常有效的解决方案.
- 这些发现突显了先进机器学习技术的潜力,以加强医疗保健部门的网络安全防御.
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