智能深度联合学习模型用于提高物联网安全性,启用边缘计算环境
1Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia. nalbugami@kau.edu.sa.
Scientific reports
|February 3, 2025
概括
本研究介绍了一种用于增强安全的智能深度联合学习模型 (IDFLM-ES),以保护物联网 (IoT) 环境. IDFLM-ES方法有效地识别入侵,在边缘计算安全方面达到98.24%的准确性.
科学领域:
- 网络安全和隐私 网络安全和隐私
- 人工智能和机器学习
- 物联网 (IoT) 和边缘计算
背景情况:
- 物联网和边缘计算的快速进步带来了重大的安全和隐私挑战,特别是在个人数据泄露方面.
- 集中式机器学习 (ML) 方法与物联网设备生成的庞大,分布式数据作斗争,需要分散的解决方案.
- 联合学习 (FL) 提供了一种保护隐私的方法,但物联网边缘环境中的系统异质性 presents实施障碍.
研究的目的:
- 为增强安全 (IDFLM-ES) 开发一个智能深度联合学习模型,适用于支持物联网的边缘计算环境.
- 通过检测入侵来解决隐私问题并提高物联网系统的安全性.
- 在异质物联网环境中提高联合学习模型的效率和融合.
主要方法:
- IDFLM-ES方法使用联合混合深信网络 (FHDBN) 模型与FL在物联网边缘设备的时间序列数据上.
- 预处理涉及使用Golden Jackal Optimization (GJO) 的数据规范化和功能选择.
- 该模型学习分布式特征表示来加速融合,超参数由虫优化器 (DBO) 优化.
主要成果:
- IDFLM-ES方法在基准数据集上表现出卓越的性能.
- 实验验证显示准确率为98.24%,超过现有模型.
- 该方法有效地识别不必要的入侵,提高物联网环境安全.
结论:
- IDFLM-ES方法为增强物联网边缘计算环境中的安全性和隐私提供了有效的解决方案.
- 联合学习,结合深度学习和优化技术,可以克服数据异质性带来的挑战.
- 拟议的模型为分布式物联网系统中安全和保护隐私的数据分析提供了一个有希望的方向.
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