融合多域和自适应变化模式分解电图特征提取用于轻量级生物启发的密钥生成和加密
Israel Edem Agbehadji1, Richard C Millham2, Emmanuel Freeman3
1Honorary Research Fellow, Faculty of Accounting and Informatics, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
本研究介绍了一种基于心电图的新型安全系统,使用融合的多域特征和自适应变化模式分解. 它增强了医疗物联网数据加密的稳定性.
科学领域:
- 生物识别和网络安全
- 信号处理和数据加密.
背景情况:
- 越来越多的人担心互联设备的安全问题,特别是在医疗保健领域 (医疗设备互联网).
- 传统安全方法 (指纹,密码) 和现有的心电图特征提取技术 (时间/频率域) 的局限性.
- 在可靠加密的变量模式分解中保持内部关联的挑战.
研究的目的:
- 为敏感数据开发一种强大而轻量级的加密技术.
- 为了利用人类独特的生理信号 (ECG) 来安全生成密钥和数据保护.
- 通过纳入多域分析来解决现有的ECG特征提取方法的局限性.
主要方法:
- 使用合并的多域心电图 (ECG) 功能.
- 应用自适应变化模式分解 (VMD) 来捕捉内部领先的相关性.
- 开发一种轻量级的加密方案,使用融合的心电图特征和生物启发的密钥生成技术.
- 通过平均值,标准偏差,斜率,kurtosis和执行时间 (例如,Chacha20在27μs) 等指标来评估性能.
主要成果:
- 成功提取了具有特定统计特性的心电图特征 (平均值: 0.0004,std dev: 0.0391,斜率: 0.1562,kurtosis: 1.2205).
- 证明了融合心电图特征和自适应性VMD在减轻内部关联的损失方面的有效性.
- 提出了一种轻量级的加密方案,采用生物启发的密钥生成方法.
结论:
- 拟议的方法提供了使用ECG信号进行数据加密的可靠和高效方法.
- 将多域心电图功能与自适应性VMD融合,提高了加密算法的安全性和可靠性.
- 这种生物启发的加密技术适用于医疗物联网和其他敏感应用中的数据保护.
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