多目标强化联合学习区块链启用了物联网和雾云基础设施,用于运输数据
Mazin Abed Mohammed1,2,3, Abdullah Lakhan4,2,3, Karrar Hameed Abdulkareem5
1Department of Artificial Intelligence, College of Computer Science and Information Technology, University of Anbar, Anbar, 31001, Iraq.
本研究介绍了使用多目标增强联合学习区块链 (MORFLB) 的安全,分散的基础设施,以增强智能城市交通应用的网络安全. 通过检测对车载数据的攻击,MORFLB最大限度地减少了延迟,并最大限度地提高了回报.
科学领域:
- 网络安全 网络安全
- 智能运输系统 智能运输系统
- 分散式系统 分散式系统 分散式系统
背景情况:
- 智能城市运输应用程序面临着由于异质云基础设施的网络安全挑战.
- 现有的集中安全和调度策略对这些应用程序产生了低于最佳的结果.
研究的目的:
- 在雾云网络中提供安全,分散的数据传输基础设施.
- 为交通基础设施引入多目标强化联合学习区块链 (MORFLB).
主要方法:
- MORFLB采用多代理策略,工作证明散列和分散的深度神经网络培训.
- 它使用区块链强化联合学习集成了车辆应用程序,分散的雾和云节点.
- 制定了一个组合式问题,以优化处理/传输延迟和奖励.
主要成果:
- MORFLB有效地减少了处理和传输的延迟.
- 与现有方法相比,该系统展示了增强的奖励最大化.
- MORFLB成功地识别了对车载数据的已知和未知攻击.
结论:
- MORFLB为智能运输系统的网络安全提供了一个有前途的解决方案.
- 分散的基础设施确保了运输数据的高效和安全执行.
- 这种方法在定义的约束条件下实现最佳结果.
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