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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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在软件定义的车辆网络中使用统计流量分析和机器学习进行有效的DDoS攻击检测.

Himanshi Babbar1, Shalli Rani1, Maha Driss2,3

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, Rajpura, India.

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概括

本研究介绍了使用机器学习 (ML) 在软件定义车载网络 (SDVN) 中检测分布式拒绝服务 (DDoS) 攻击的新方法. 随机森林模型在识别恶意流量方面表现出卓越的性能.

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科学领域:

  • 网络安全 网络安全
  • 网络工程 网络工程
  • 人工智能的人工智能

背景情况:

  • 车辆网络 (VN) 对于交通优化和安全至关重要.
  • 软件定义网络 (SDN) 增强了无线网络的能力.
  • 越南越来越容易受到分布式拒绝服务 (DDoS) 攻击的影响.

研究的目的:

  • 提出新的方法来检测软件定义车载网络 (SDVN) 中的DDoS攻击.
  • 在SDN入侵检测系统 (IDS) 中实施机器学习 (ML) 算法,用于车辆环境.
  • 为了应对不平衡数据集的挑战,并区分不同类型的攻击.

主要方法:

  • 统计流量分析和计算.
  • 在BoT-IoT数据集上实施ML算法 (K-最近邻居,随机森林,后勤回归).
  • 特性子集选择以优化模型准确性和评估数据集属性影响.

主要成果:

  • 随机森林分类器实现了高性能指标:92%的精度,92%的F1得分,91%的准确性和90%的回忆在五次代中.
  • 该研究确定了最佳样本大小,并评估了数据集属性对性能的影响.
  • 拟议的方法有效地区分了侦察,DoS和DDoS流量.

结论:

  • 机器学习,特别是随机森林,对于在SDVN中检测DDoS是有效的.
  • 高效的数据处理和潜在的边缘计算对于实时性能至关重要.
  • 开发的方法为增强车辆网络安全提供了一个可扩展的解决方案.