Open AccessRF Jamming Classification Using Relative Speed Estimation in Vehicular Wireless Networks
Author(s) -
Dimitrios Kosmanos,
Dimitrios Karagiannis,
Antonios Argyriou,
Spyros Lalis,
Λέανδρος Μαγλαράς
Publication year - 2021
Publication title -
security and communication networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.446
H-Index - 43
eISSN - 1939-0114
pISSN - 1939-0122
DOI - 10.1155/2021/9959310
Subject(s) - computer science , jamming , denial of service attack , wireless , computer network , interference (communication) , wireless ad hoc network , metric (unit) , vehicular ad hoc network , real time computing , computer security , telecommunications , channel (broadcasting) , operations management , physics , the internet , world wide web , economics , thermodynamics
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.