RF Jamming Classification Using Relative Speed Estimation in Vehicular Wireless Networks

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), incorporate a series safety-critical applications, may potential target RF jamming with detrimental safety effects. To ensure secure between entities and in order to make the network robust this type attacks, an accurate detection scheme must adopted. In paper, we introduce that is based on supervised learning. The k-nearest neighbors (KNN) random forest (RaFo) methods used, including features, among one metric variations relative speed (VRS) jammer receiver. VRS estimated from combined value useful signal at KNN-VRS RaFo-VRS classification algorithms able detect various cases denial-of-service (DoS) attacks differentiate those very high accuracy.