优化的Adaboost支持向量基于机器的加密,用于保护物联网云医疗数据
Yoosef B Abushark1, Shabbir Hassan2, Asif Irshad Khan2
1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了一种新方法,以保护物联网 (IoT) 医疗保健系统中的医疗数据. 具有灰狼优化和国际数据加密算法 (基于ASVM的GWO-IDEA) 的AdaBoost支持矢量机器提高了数据安全性和传输效率.
科学领域:
- 医疗信息学 医疗信息学
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 医疗保健中的物联网 (IoT) 能够实现远程监控,改善患者的治疗结果和诊断.
- 存在重大数据安全挑战,包括维护机密性,完整性和可用性 (CIA三位一体) 和保护数据传输.
- 现有的物联网医疗保健系统容易受到网络攻击,未经授权的访问和数据改.
研究的目的:
- 提出一个新的AdaBoost支持矢量机器 (ASVM),集成灰狼优化和国际数据加密算法 (GWO-IDEA),用于在物联网医疗保健中保护医疗数据.
- 防止网络攻击,未经授权的访问和改物联网支持的医疗保健管理系统.
- 提高医疗数据传输的安全性和效率.
主要方法:
- 开发一种基于ASVM的新型GWO-IDEA方案,用于在传输之前对医疗保健数据进行编码.
- 使用Python实现拟议的方案.
- 使用 Kaggle 的公共卫生数据集评估该计划的效率.
主要成果:
- 拟议的基于ASVM的GWO-IDEA方案证明了改进的数据传输流程.
- 实现了97.86%的高吞吐量.
- 在数据传输过程中报告了98.45%的改善资源利用率和93.45%的高效率.
结论:
- 基于ASVM的GWO-IDEA方案有效地提高了物联网医疗系统中医疗数据的安全性.
- 拟议的方法提供了针对网络威胁和未经授权访问的强有力的解决方案.
- 该计划显著提高了医疗保健物联网环境中的数据传输效率和资源利用率.
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