Vehicular networks are vulnerable to Distributed Denial of Service (DDoS), an extension of a Denial of Service (DoS) attack. The existing solutions for DDoS detection in vehicular networks use various Machine Learning (ML) algorithms. However, these algorithms are applicable only in a single layer in a vehicular network environment and are incapable of detecting DDoS dynamics for different layers of the network infrastructure. The recently reported attacks on transport networks reveal the fact that a research gap exists between the existing solutions and the multi-layer DDoS detection strategy requirements. Additionally, the majority of the current detection methods fail in the consideration of traffic heterogeneity and are not rate-adaptive, where both the mentioned parameters are important for an effective detection system.

中文翻译：

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VAIDANSHH：车辆环境中异构主机的自适应 DDoS 检测


车载网络容易受到分布式拒绝服务 (DDoS) 攻击，这是拒绝服务 (DoS) 攻击的延伸。车载网络中 DDoS 检测的现有解决方案使用各种机器学习 (ML) 算法。然而，这些算法仅适用于车辆网络环境中的单层，并且无法检测网络基础设施不同层的 DDoS 动态。最近报道的针对传输网络的攻击揭示了现有解决方案与多层 DDoS 检测策略要求之间存在研究差距的事实。此外，当前的大多数检测方法都未能考虑流量异构性并且不具有速率自适应性，其中提到的两个参数对于有效的检测系统都很重要。