联合学习和EEL-Levy优化在CPS的ShieldNet融合:网络物理安全的新范式
Nalini Manogaran1, Yamini Bhavani Shankar2, Malarvizhi Nandagopal3
1Department of Computer Science and Business Systems, S.A. Engineering College (Autonomous), Chennai 600077, Tamil Nadu, India.
Sensors (Basel, Switzerland)
|June 27, 2025
概括
网络物理系统 (CPS) 面临着不断变化的网络威胁. 通过使用联合学习和优化,CPS ShieldNet Fusion模型增强了CPS的安全性,改善了威胁检测,同时保护了数据隐私.
科学领域:
- 网络物理系统安全 网络物理系统安全
- 深度学习应用程序
- 联邦学习学习 (Federated Learning) 是一种学习方式.
背景情况:
- 网络物理系统 (CPS) 越来越重要,集成工业控制,智能电网和医疗设备.
- 由于CPS的复杂性和互联性,它们容易受到复杂的网络攻击.
- 现有的CPS安全的深度学习解决方案面临着可扩展性,数据隐私和适应动态环境的挑战.
研究的目的:
- 引入CPS ShieldNet Fusion,这是一个新的安全框架,旨在保护CPS免受先进的网络威胁.
- 开发一个强大而可扩展的解决方案,解决当前CPS网络安全方法的局限性.
- 在CPS环境中增强威胁检测和响应能力.
主要方法:
- 集成联邦剩余卷积网络 (FedRCNet) 进行分散的,维护隐私的模型培训.
- 应用EEL-Levy融合优化 (ELFO) 来提高威胁检测的效率和有效性.
- 结合FedRCNet和ELFO,创建了一个全面的CPS ShieldNet Fusion模型.
主要成果:
- 在多个基准数据集 (CICIoT-2023,Edge-IIoTset-2023,UNSW-NB) 中,CPS ShieldNet Fusion表现出卓越的准确性和有效性.
- 该模型在检测各种CPS设置中的各种网络威胁方面实现了最先进的性能.
- 通过联合学习进行的分散培训成功地保护了数据隐私.
结论:
- 通过CPS ShieldNet Fusion框架,在保护网络物理系统免受当前和未来威胁方面取得了重大进展.
- 联合学习和高级优化集成提供了一个可扩展和保护隐私的网络安全解决方案.
- 拟议的模型具有提高关键基础设施和日常技术的弹性和安全性的巨大潜力.
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