使用可取消的ECG识别来保护医疗物联网网络的安全
Samia A El-Moneim Kabel1, Ghada M El-Banby2, Lamiaa A Abou Elazm3
1Tanta High Institute of Engineering and Technology (THIET), Tanta, Egypt.
Scientific reports
|May 13, 2024
概括
这项研究引入了一种新的框架,用于使用心电图 (ECG) 生物识别来保护医疗物联网 (IoMT) 网络. 该系统采用可取消的模板,从ECG信号中获得,增强了对未经授权访问和数据泄露的安全性.
科学领域:
- 生物识别和网络安全
- 信号处理和机器学习
背景情况:
- 医疗物联网 (IoMT) 网络需要强大的安全性来传输敏感的患者数据.
- 生物识别认证,特别是使用心电图 (ECG) 信号,为安全访问提供了一个有希望的解决方案.
- 保护生物识别模板免受损害至关重要,这导致需要可取消生物识别系统.
研究的目的:
- 为基于ECG的识别制定一个全面的框架,并为IoMT网络访问提供可取消模板.
- 引入一种创新的方法来进行非可逆的ECG信号修改,使用盲信号分离和轻量级加密.
- 通过使生物识别模板在受到损害时被改变来增强安全性.
主要方法:
- 利用盲信号分离,通过最小化与辅助音频信号的相关性来创建ECG信号的扭曲,非可逆版本.
- 应用轻量级加密阶段,使用特定用户模式和XOR操作对扭曲的ECG信号进行生成可取消模板.
- 实施了混合安全模型,将不可逆转换和轻量级加密相结合,以提高安全性和减少处理负担.
主要成果:
- 拟议的框架在ECG-ID和MIT-BIH数据集上表现出高效.
- 在ECG-ID数据集上达到0.134的等错率 (EER),在MIT-BIH数据集上达到0.4.
- 获得了两个数据集的收发器运行特征曲线 (AROC) 下面面积为99.96%,表明了出色的性能.
结论:
- 开发的框架有效地使用可取消的ECG生物识别来保护IoMT网络.
- 非可逆转换和轻量级加密的混合方法提供了强大而高效的安全解决方案.
- 这些有前途的实验结果突显了这个框架在现实世界IoMT安全应用中的潜力.
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