一个安全而高效的区块链使得汽车Ad-hoc网络的联合Q学习模型成为可能
Huda A Ahmed1, Hend Muslim Jasim2, Ali Noori Gatea3
1College of Computer Science and Information Technology, University of Basrah, Basrah, Iraq.
Scientific reports
|December 29, 2024
概括
这项研究通过将区块链与联合Q学习集成来增强车辆特设网络 (VANET) 的安全性,从而改善连接汽车的数据保护和网络性能.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 由于自动化车辆的兴起,车辆特设网络 (VANET) 面临越来越多的安全威胁.
- 现有的联合学习 (FL) 方法为VANET数据交换提供有限的隐私保护.
- 安全的数据传输对于维护VANET完整性至关重要.
研究的目的:
- 通过将区块链技术与联合Q学习结合起来,为VANETs提出一个新的安全框架.
- 通过先进的加密和存储技术,增强VANET中的数据安全性和隐私.
- 提高VANETs的整体安全性和性能.
主要方法:
- 实现扩展圆曲线加密 (EX-ECC) 用于数据加密.
- 利用联合Q学习进行隐私保护数据培训.
- 集成的星际文件系统 (IPFS) 用于安全的区块链存储.
- 员工授权实用拜占庭式故障容忍 (DPBFT) 用于网络验证.
主要成果:
- 拟议的框架大大提高了VANET的安全性和隐私.
- 实现了高吞吐量 (102465.8 KB/s) 和低通讯开销 (360.57 Mb).
- 与现有的VANET安全方法相比,其表现优越.
结论:
- 区块链与联合Q学习的集成为VANET安全提供了强大的解决方案.
- 拟议的基于EX-ECC和DPBFT的框架有效地解决了隐私和安全方面的问题.
- 这种方法在提高智能运输系统的安全性和效率方面具有重大潜力.
关键词:
区块链系统 区块链系统授权的实际拜占庭式故障耐受性 (DPBFT).扩展圆曲线加密技术 (EX-ECC)联合的Q-学习.星际档案系统 (IPFS) 是一个国际档案系统.车辆特设网络 (VANETs) 的使用.更多相关视频
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