提高智能医疗保健系统的安全性:使用智能边缘计算,使用新的Salp Swarm优化和辐射基础神经网络算法
Abdulmohsen Almalawi1, Aasim Zafar2, Bhuvan Unhelkar3
1Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Heliyon
|July 23, 2024
概括
本研究介绍了智能医疗保健系统的智能边缘计算框架,增强了安全性和数据隐私. 这种新的方法有效地识别威胁,并确保安全的数据传输,实现高精度.
科学领域:
- 计算机科学 计算机科学
- 医疗保健技术 技术 医疗保健 技术
- 网络安全 网络安全
背景情况:
- 智能医疗保健系统 (SHS) 利用物联网和可穿戴设备等先进技术进行动态健康管理.
- 边缘计算 (EC) 对于SHS中的实时数据处理至关重要,可以减少延迟并改善响应时间.
- 将患者数据 (EHR) 集成到SHS中引发了重大安全和隐私问题.
研究的目的:
- 为智能医疗保健系统开发一个智能边缘计算框架.
- 准确识别安全威胁并确保SHS内部的安全数据传输.
- 在联网医疗环境的背景下,加强数据隐私和安全性.
主要方法:
- 提出了一个智能EC框架,集成Salp Swarm优化和辐射基功能神经网络 (SS-RBFN).
- 雇佣数据预处理以确保数据库的一致性和质量.
- 使用SS-RBFN算法来区分正常与恶意数据流,并使用Rivest-Shamir-Adelman (RSA) 算法来安全传输数据.
主要成果:
- 拟议的SS-RBFN模型实现了99.87%的准确性,99.76%的精度,99.49%的f测量和98.99%的回忆.
- 在实验验证中证明了高吞吐量 (97.37%) 和低延迟 (1.2s).
- 实验结果验证了与现有的安全增强方法相比,该模型的有效性.
结论:
- 智能EC框架有效地解决了智能医疗保健系统中的安全和隐私挑战.
- SS-RBFN算法提供了强大的威胁识别和持续监控能力.
- 拟议的系统确保了安全的数据传输,并提高了SHS的整体安全性.
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