在医疗保健物联网 (H-IoT) 环境中增强安全性,数据完整性和操作性能的混合深度学习支持框架
Nithesh Naik1, Neha Surendranath2, Sai Annamaiah Basava Raju3
1Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Scientific reports
|August 23, 2025
概括
这项研究引入了基于信任的混合框架,使用深度学习来增强以人为中心的物联网 (H-IoT) 系统的安全性. 它改善了实时异常检测和生理数据的自适应性访问控制,确保了系统的弹性.
科学领域:
- 网络安全
- 人工智能
- 生物医学工程
背景情况:
- 在医疗保健和智能环境中以人为中心的物联网 (H-IoT) 系统面临着严峻的安全挑战.
- 传统的安全方法对于动态,实时的生理数据流是不够的,使H-IoT设备脆弱.
研究的目的:
- 为H-IoT系统的自适应性安全提出一种新的信任感的混合框架.
- 增强实时异常检测和适应性访问控制,以确保生理数据的完整性.
主要方法:
- 整合卷积神经网络 (CNN),长短期记忆 (LSTM) 和变化自编码器 (VAE) 进行生理信号分析.
- 开发一个动态的信任感控制器 (TAC),用于实时的信任评分计算.
- 在各种攻击和噪音条件下对基准和专有H-IoT数据集进行评估,包括边缘设备的部署.
主要成果:
- 在异常检测方面获得了94.3%的F1平均得分,在访问分类方面获得了96.1%的准确性.
- 与传统基线相比,检测灵敏度有12- 18% 的改善.
- 在边缘硬件上保持实时推断延迟在160ms以下,在不利条件下显示高稳定性.
结论:
- 拟议的框架为H-IoT网络的自适应性安全提供了科学基础和可扩展的解决方案.
- 深度学习和信任建模的融合提高了H-IoT安全的响应能力和弹性.
- 这为下一代健康和可穿戴生态系统铺平了道路.
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