RF Jamming Classification using Relative Speed Estimation in Vehicular Wireless Networks
This addresses security in safety-critical vehicular networks, but is incremental as it applies existing methods with a new feature.
The paper tackles RF jamming detection in vehicular networks by introducing a supervised learning scheme using KNN and Random Forests with a novel relative speed variation metric, achieving efficient detection of DoS attacks and differentiation from interference.
Wireless communications are vulnerable against radio frequency (RF) jamming 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 communication and defend it 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 machine-learning algorithms, KNearest Neighbors (KNN) and Random Forests (RF), utilize a series of features among which is the metric of the variations of relative speed (VRS) between the jammer and the receiver that is passively estimated from the combined value of the useful and the jamming signal at the receiver. To the best of our knowledge, this metric has never been utilized before in a machine-learning detection scheme in the literature. Through offline training and the proposed KNN-VRS, RF-VRS classification algorithms, we are able to efficiently detect various cases of Denial of Service Attacks (DoS) jamming attacks, differentiate them from cases of interference as well as foresee a potential danger successfully and act accordingly.