基于机器学习的区块链技术用于安全的V2X通信:开放的挑战和解决方案
Yonas Teweldemedhin Gebrezgiher1, Sekione Reward Jeremiah2, Xianjun Deng3
1Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
保护互联和自动驾驶汽车 (CAV) 需要强大的解决方案,以实现车辆到一切 (V2X) 通信. 这项调查探讨了机器学习,区块链和边缘计算,以增强V2X对网络威胁的安全性.
科学领域:
- 网络安全 网络安全
- 智能运输系统 智能运输系统
- 网络通讯 网络通讯 网络通讯
背景情况:
- 车辆与一切 (V2X) 通信对于联网和自动驾驶汽车 (CAV) 至关重要,提高了安全性和效率.
- V2X网络面临来自对抗性攻击的重大网络安全威胁,影响CAV安全和隐私.
- 现有的安全措施与车辆网络的动态性和实时处理需求作斗争.
研究的目的:
- 调查和分析CAV的V2X通信当前的安全挑战.
- 评估机器学习 (ML) 和基于区块链的解决方案在应对这些挑战中的有效性.
- 识别漏洞和改善V2X安全的机会,特别是通过综合方法.
主要方法:
- 关于V2X安全性的综合文献审查,重点关注ML,区块链和多访问边缘计算 (MEC).
- 分析现有研究,将ML,区块链和MEC集成为V2X安全.
- 开发一个概念框架,将ML,区块链和MEC协同用于增强V2X安全.
主要成果:
- 目前用于V2X安全的ML和区块链解决方案存在重大差距,需要进一步改进.
- 机器学习,区块链和MEC的整合显示了V2X网络中低延迟,强大和动态安全的潜力.
- 拟议的概念框架强调了实时异常检测,分散的数据共享和可扩展性的可能性.
结论:
- 结合ML,区块链和MEC的协同方法为确保V2X通信提供了一个有希望的方向.
- 需要进行进一步的研究,以解决V2X安全方面的剩余挑战和不断变化的威胁.
- 综合框架可以增强智能运输系统的安全性,可扩展性和实时功能.
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