# RF Jamming Classification using Relative Speed Estimation in Vehicular Wireless Networks

## TL;DR

This paper presents a novel RF jamming detection method for vehicular networks using machine learning algorithms that incorporate a unique relative speed variation metric, improving attack detection accuracy.

## Contribution

It introduces the first use of relative speed variation as a feature in machine learning-based RF jamming detection for VANETs, enhancing detection capabilities.

## Key findings

- Effective detection of DoS jamming attacks using RF and KNN/RF algorithms.
- Ability to differentiate jamming from interference.
- Successful prediction of potential threats.

## Abstract

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.