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  • 1FAST School of Computing, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan.

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概括
此摘要是机器生成的。

物联网和传感器网络中的网络功能虚拟化 (NFV) 安全性通过异常检测得到增强. 这项调查确定了机器学习算法,以有效地减轻网络攻击并保护网络完整性.

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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
  • 网络工程 网络工程

背景情况:

  • 网络功能虚拟化 (NFV) 提供了显著的好处,如降低成本和灵活性.
  • 对于优化传感器和物联网 (IoT) 网络而言,NFV至关重要.
  • 采用NFV引入了复杂的安全挑战.

研究的目的:

  • 探索NFV的安全挑战,特别是在物联网和传感器网络中.
  • 提出异常检测技术,以减轻NFV环境中的网络威胁.
  • 评估机器学习算法,以有效检测NFV网络中的异常.

主要方法:

  • 对NFV安全现有文献的调查.
  • 对异常检测技术的分析,重点是机器学习.
  • 评估各种ML算法用于NFV的基于网络的异常检测.

主要成果:

  • 在NFV部署中确定了关键的安全漏洞.
  • 评估了不同机器学习算法的异常检测效率.
  • 突出了评估算法的优缺点.

结论:

  • 异常检测,特别是使用机器学习,对于确保NFV至关重要.
  • 为及时缓解威胁提供了对最有效算法的洞察.
  • 旨在加强强大的传感器和物联网系统的NFV安全性.