  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.
