rf jamming classification using relative speed estimation in vehicular wireless networks

• DOI: 10.48550/arxiv.1812.11886
• Publication Date: 2021-08-26
• Authors: Dimitrios Kosmanos; Dimitrios Karagia...; Antonios Argyriou; Spyros Lalis; Leandros Maglaras
• Keywords: FOS: Computer and information sciences; Computer Science - Cryptography and Security; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Cryptography and Security (cs.CR)

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.