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概括
此摘要是机器生成的。

本研究回顾了当前物联网 (IoT) 安全趋势,重点关注医疗物联网 (IoMT) 和车辆互联网 (IoV). 它强调需要入侵预防系统 (IPS) 和机器学习来保护这些关键的物联网应用程序.

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深度学习是一种深度学习.IPS (入侵防范系统) 是一个系统.医疗设备互联网 (IoMT) 是指医疗设备的互联网.物联网 (物联网) 是物联网 (物联网) 的一个词.汽车互联网 (IoV) 是指汽车的互联网.机器学习 机器学习

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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
  • 网络安全 网络安全

背景情况:

  • 物联网 (IoT) 设备的普及,特别是在医疗保健 (医疗物联网 - IoMT) 和交通 (车辆互联网 - IoV) 等关键领域,导致安全威胁的惊人增加.
  • 确保IoMT和IoV设备的身份验证,授权和数据隐私是至关重要的,因为它们在实时监控个人安全方面的作用至关重要.
  • 越来越复杂的安全攻击需要先进的解决方案,如入侵预防系统 (IPS),以保护这些相互连接的系统.

研究的目的:

  • 调查和分析物联网 (IoT) 安全方面的当前研究趋势.
  • 确定关键挑战,并提出未来的研究方向,以确保物联网生态系统的安全,特别关注IoMT和IoV.

主要方法:

  • 进行了系统文献审查 (SLR),涉及到搜索物联网安全领域的相关论文.
  • 选择了前50篇论文进行深入分析,并根据已识别的问题制定了研究问题.
  • 从数字档案中检索数据,并根据审查结果开发了物联网子域的分类学.

主要成果:

  • 该研究确定机器学习和深度学习技术对于检测和减轻IoMT和IoV设备中的安全威胁至关重要.
  • 建立了物联网安全子域的全面分类,为研究领域提供了结构化的概述.
  • 确定了物联网安全中需要进一步调查的关键挑战和领域.

结论:

  • 机器学习和深度学习对于提高IoMT和IoV等关键物联网应用程序的安全性至关重要.
  • 系统性审查为了解物联网安全研究的当前状态提供了宝贵的基础,并指导了未来的努力.
  • 建议进行进一步的研究,以解决已识别的安全挑战,并推进物联网系统的稳定性.