车辆网络物理系统的加密安全性增强的联合学习
Himanshi Babbar1, Shalli Rani1, Mohammad Shabaz2
1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Scientific reports
|August 5, 2025
概括
本研究介绍了一种新的联合学习设计,以提高车辆网络物理系统 (VCPS) 的数据安全性. 该方法在分散的培训和聚合过程中保护敏感数据,提高运输安全和效率.
科学领域:
- 网络安全 网络安全
- 智能运输系统 智能运输系统
- 机器学习 机器学习
背景情况:
- 车辆网络物理系统 (VCPS) 对智能交通系统 (ITS) 至关重要,它能够实现自动驾驶汽车和实时交通控制等功能.
- 由VCPS处理的敏感数据带来了重大安全风险,包括对公共安全的威胁和财务损失.
- 现有的安全措施往往难以平衡强大的保护与现代运输网络的性能要求.
研究的目的:
- 提出一个新的联合学习框架,旨在提高车辆网络物理系统 (VCPS) 中的数据安全性.
- 在分散型模型培训期间确保数据隐私,并将本地模型安全聚合到全球模型中.
- 为解决在不断发展的ITS基础设施中强大安全的关键需求.
主要方法:
- 实施了联合学习方法,使车辆和路边单位 (RSU) 的分散模型培训成为可能.
- 整合了拉普拉斯方法来添加噪声到模型更新,进一步保护全球模型聚合期间的隐私.
- 建立了一个涉及车辆,RSU和中央服务器的协作框架,以防止信息泄露.
主要成果:
- 拟议的联合学习设计在CICIDS2017数据集上显示出与传统加密技术相比更高的性能.
- 该方法有效地保持了高水平的数据机密性和系统安全性.
- 在不影响计算效率或模型准确性的情况下实现了这些安全增强.
结论:
- 新的联合学习框架为VCPS中保护敏感数据提供了一个有希望的解决方案.
- 这种方法对于保持智能运输系统的完整性和可靠性至关重要.
- 实施这种复杂的安全措施最终将有助于提高运输安全和效率.
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