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MESMERIC:用于车辆互联网的基于机器学习的信任管理机制

Yingxun Wang^1,2, Adnan Mahmood³, Mohamad Faizrizwan Mohd Sabri¹

¹Faculty of Engineering, Universiti Malaysia Sarawak, Kota Samarahan 94300, Sarawak, Malaysia.

Sensors (Basel, Switzerland)

|February 10, 2024

概括

一个新的机器学习模型MESMERIC通过准确识别恶意车辆来增强汽车互联网 (IoV) 的安全性. 这种信任管理机制改善了智能城市的道路安全和交通流量.

科学领域:

网络安全网络安全
运输工程运输工程
人工智能的人工智能

背景情况:

车辆互联网 (IoV) 对于智能城市至关重要,它可以实现车辆与一切的通信,以提高道路安全和交通管理.
在 IoV 网络中,安全关键信息的可靠交换至关重要;受损的信任可能会危及整个系统.
现有的信任机制不足以有效地识别和删除动态IoV环境中的恶意实体.

研究的目的:

为 IoV 网络提出基于机器学习的新型信任管理机制.
提高IoV系统内安全关键信息传播的可靠性和安全性.
有效地区分可靠的车辆和恶意的车辆,以确保网络完整性.

主要方法:

开发了MESMERIC,一种信任管理机制,集成直接信任 (交互成功,相似性,熟悉性,奖励/惩罚),间接信任 (邻近节点的信任) 和上下文 (车辆类型,场景).
利用机器学习建立最佳决策边界来分类车辆信任级别.
进行了全面的评估,将MESMERIC与现有的最先进的信任管理机制进行比较.

主要成果:

MESMERIC准确地确定了 IoV 网络中的车辆信任.
该机制有效地将可靠的车辆与不可靠的车辆分开.
评估表明,与其他领先的信任管理方法相比,MESMERIC的表现优越.

结论:

拟议的MESMERIC机制为IOV的信任管理提供了一个强大的解决方案.
实施MESMERIC可以显著提高IoV网络的安全性和可靠性.
这种方法有助于在智能城市环境中实现更安全的导航和更智能的交通流.

关键词:

汽车互联网的汽车互联网语境语境语境语境语境直接信任直接信任直接信任间接信托是一种间接的信托.机器学习是机器学习.最佳决策的边界是最佳决策的边界.信任管理机制信任管理机制

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