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一个基于深度学习的安全路由协议,以避免VANET中的黑洞攻击.

Amalia Amalia1, Yushintia Pramitarini1, Ridho Hendra Yoga Perdana1

  • 1Department of Software and Communications Engineering in Graduate School, Hongik University, Sejong City 30016, Republic of Korea.

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概括

本研究介绍了基于深度学习的安全路由协议,用于车辆特设网络,以防止黑洞攻击. 拟议的方法通过识别恶意节点并优化路由决策来提高网络安全性和效率.

关键词:
黑洞是一个黑洞.集群集成是指集群集成.深度学习是一种深度学习.安全的路由安全的路由.车辆特设网络是车辆特设网络.

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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:

  • 车辆特设网络 (VANET) 对智能交通系统 (ITS) 至关重要,改善了交通流量和安全.
  • 范网面临着重大的安全挑战,特别是破坏通信的黑洞攻击.
  • 现有的安全协议往往难以有效地减轻这些威胁在动态的网络环境.

研究的目的:

  • 为VANETs提出一种基于深度学习的新型安全路由 (DLSR) 协议.
  • 通过使用深度学习技术,增强网络安全,防止黑洞攻击.
  • 通过基于深度学习的集群 (DLC) 协议来提高连接性和减少控制开销.

主要方法:

  • 开发了一个使用深度学习 (DL) 的DLSR协议,用于安全的路线选择和恶意节点识别.
  • 实现了一个DLC协议作为一个底层结构,以提高节点连接,减少控制开销.
  • 设计了一个深度神经网络 (DNN) 模型,以优化DLSR和DLC协议中的健身功能,考虑能量,距离和跳数等参数.

主要成果:

  • DLSR协议有效地识别恶意节点,并根据健身功能选择安全路线.
  • DLC协议增强了网络连接,并减少了控制开销.
  • 性能评估表明,数据包交付比率有所改善,路由延迟减少,数据包损失减少.

结论:

  • 拟议的DLSR和DLC协议为保护VANET免受黑洞攻击提供了一个强大的解决方案.
  • 深度学习的整合大大提高了智能运输系统的安全性和效率.
  • 该研究强调了在不同移动模式 (RPGM,RWP) 下提出的协议的有效性.