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物联网-DH数据集用于物联网中的DDoS攻击的分类,识别和检测.

Syaifuddin Saif1,2, Widyawan Widyawan1, Ridi Ferdiana1

  • 1Department of Electrical and Information Technology, Universitas Gadjah Mada, Jl. Grafika 2, Yogyakarta 55281, Indonesia.

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概括

本研究介绍了IoT-DH数据集,用于检测物联网 (IoT) 生态系统中的分布式拒绝服务 (DDoS) 攻击. 它可以开发机器学习模型来分类,识别和减轻这些重大网络威胁.

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分类 分类 分类 分类.这是一种DDoS攻击.蜂箱中的蜂蜜标识 识别 识别 识别这就是为什么物联网是物联网物联网.

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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) 设备的快速扩张引入了重要的安全漏洞.
  • 分布式拒绝服务 (DDoS) 攻击对物联网生态系统的完整性和可用性构成重大威胁.
  • 现有的数据集可能无法完全捕捉现代物联网网络环境和攻击载体的复杂性和多样性.

研究的目的:

  • 介绍IoT-DH数据集,这是一个全面的资源,用于DDoS攻击分析物联网.
  • 促进机器学习和深度学习模型的开发和评估,以缓解物联网中的DDoS攻击.
  • 为在各种物联网场景中分类,识别和检测DDoS攻击提供现实的基准.

主要方法:

  • 开发和系统分析新的物联网-DH数据集,包括各种网络配置和攻击场景.
  • 探索数据集特征,反映现实世界的物联网复杂性和不断变化的DDoS威胁.
  • 关于缓解DDoS攻击的多方面方法的建议,包括分类,识别和检测算法.

主要成果:

  • 物联网-DH数据集提供了物联网环境的现实表示,具有不同的攻击向量和强度.
  • 提出的方法证明了在数据集内对DDoS攻击进行分类,识别和检测的有效性.
  • 实验评估证实了开发方法提高物联网安全态度的能力.

结论:

  • 物联网-DH数据集是推动物联网网络安全研究的宝贵资源,特别是用于缓解DDoS攻击.
  • 拟议的方法提供了一个强大的框架,用于开发有效的防御机制,以对抗物联网中的DDoS威胁.
  • 这些发现强调了全面数据集和先进算法的重要性,以确保相互连接的物联网环境.