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RF Jamming Classification Using Relative Speed Estimation in Vehicular Wireless Networks

Kosmanos, Dimitrios; Karagiannis, Dimitrios; Argyriou, Antonios; Lalis, Spyros; Maglaras, Leandros


Dimitrios Kosmanos

Dimitrios Karagiannis

Antonios Argyriou

Spyros Lalis


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. e 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. e 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.

Journal Article Type Article
Acceptance Date Aug 11, 2022
Online Publication Date Aug 26, 2021
Publication Date Aug 26, 2021
Deposit Date Nov 30, 2022
Publicly Available Date Nov 30, 2022
Journal Security and Communication Networks
Print ISSN 1939-0114
Electronic ISSN 1939-0122
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 2021
Article Number 9959310
Keywords Computer Networks and Communications; Information Systems
Public URL


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