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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.
PloS one
|December 18, 2024
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.
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.
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