Wireless communications are vulnerable against radio frequency (RF) interference which might be caused either intentionally or unintentionally. A particular subset of wireless networks, Vehicular Ad-hoc NETworks (VANET), which incorporate a series of safety-critical applications, may be a potential target of RF jamming with detrimental safety effects. To ensure secure communications between entities and in order to make the network robust against this type of attacks, an accurate detection scheme must be adopted. In this paper, we introduce a detection scheme that is based on supervised learning. The k-nearest neighbors (KNN) and random forest (RaFo) methods are used, including features, among which one is the metric of the variations of relative speed (VRS) between the jammer and the receiver. VRS is estimated from the combined value of the useful and the jamming signal at the receiver. The KNN-VRS and RaFo-VRS classification algorithms are able to detect various cases of denial-of-service (DoS) RF jamming attacks and differentiate those attacks from cases of interference with very high accuracy.

中文翻译：

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在车载无线网络中使用相对速度估计进行射频干扰分类

无线通信容易受到可能有意或无意引起的射频 (RF) 干扰。无线网络的一个特定子集，车载自组织网络 (VANET)，其中包含一系列安全关键应用程序，可能是具有有害安全影响的 RF 干扰的潜在目标。为了确保实体之间的安全通信，并使网络能够抵御此类攻击，必须采用准确的检测方案。在本文中，我们介绍了一种基于监督学习的检测方案。使用了 k 最近邻 (KNN) 和随机森林 (RaFo) 方法，包括特征，其中之一是干扰器和接收器之间相对速度 (VRS) 变化的度量。VRS 是根据接收器处有用信号和干扰信号的组合值来估计的。KNN-VRS 和 RaFo-VRS 分类算法能够检测各种拒绝服务 (DoS) 射频干扰攻击案例，并以非常高的准确度将这些攻击与干扰案例区分开来。