Estimating the Relative Speed of RF Jammers in VANETs

Vehicular Ad Hoc Networks (VANETs) aim at enhancing road safety and providing a comfortable driving environment by delivering early warning and infotainment messages to the drivers. Jamming attacks, however, pose a significant threat to their performance. In this paper, we propose a novel Relative Speed Estimation Algorithm (RSEA) of a moving vehicle that approaches a transmitter ()-receiver () pair that interferes with their radio frequency (RF) communication by conducting a denial of service (DoS) attack. Our scheme is completely passive and uses a pilot-based received signal without hardware or computational cost to, firstly, estimate the combined channel between the transmitter-receiver and jammer-receiver and, secondly, to estimate the jamming signal and the relative speed between the jammer-receiver using the RF Doppler shift. Moreover, the relative speed metric exploits the angle of projection (AOP) of the speed vector of the jammer in the axis of its motion in order to form a two-dimensional representation of the geographical area. Our approach can effectively be applied for any form of the jamming signal and is proven to have quite accurate performance, with a mean absolute error (MAE) value of approximately compared to the optimal zero MAE value under different jamming attack scenarios

Intrusion Detection System for Platooning Connected Autonomous Vehicles

The deployment of Connected Autonomous Vehicles (CAVs) in Vehicular Ad Hoc Networks (VANETs) requires secure wireless communication in order to ensure reliable connectivity and safety. However, this wireless communication is vulnerable to a variety of cyber atacks such as spoofing or jamming attacks. In this paper, we describe an Intrusion Detection System (IDS) based on Machine Learning (ML) techniques designed to detect both spoofing and jamming attacks in a CAV environment. The IDS would reduce the risk of traffic disruption and accident caused as a result of cyber-attacks. The detection engine of the presented IDS is based on the ML algorithms Random Forest (RF), k-Nearest Neighbour (k-NN) and One-Class Support Vector Machine (OCSVM), as well as data fusion techniques in a cross-layer approach. To the best of the authors’ knowledge, the proposed IDS is the first in literature that uses a cross-layer approach to detect both spoofing and jamming attacks against the communication of connected vehicles platooning. The evaluation results of the implemented IDS present a high accuracy of over 90% using training datasets containing both known and unknown attacks

RF Jamming Classification Using Relative Speed Estimation in Vehicular Wireless Networks

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. e 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. e 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

An Efficient Localization and Avoidance Method of Jammers in Vehicular Ad Hoc Networks

Jamming is a terrifying attack that could harm 802.11p-based vehicular communications by occupying the communication channels by overwhelming the network with jamming packets, especially for self-driving cars, as it is essential to send/receive messages without any interruptions to control the vehicles remotely. In wireless vehicular ad hoc networks (VANET), the attacker’s mission is more accessible due to the network’s open nature, way of communication, and lack of security measures. Most of the existing studies have focused on jamming detection approaches. However, few of them have addressed the jammer localization challenge. Moreover, even in these limited studies, the solutions’ assumptions, the proposed countermeasures, and their complexity were also missing. Therefore, this paper introduces a new approach to detecting, localizing, and avoiding jamming attacks in VANETs with high efficiency in terms of accuracy, implementation and complexity. The proposed approach uses the signal strength of the jammer for estimating only the distance between jammer and receiver, while then a less complex algorithm is proposed for localizing the jammer and then redirecting the vehicles away from the roads the attacker is using. This approach was simulated using real-life maps and specialized network environments. Additionally, the performance of the new approach was evaluated using different metrics. These evaluation metrics include (1) the estimated position of the jammer, (2) the handling of the jammer by announcing its location to normal vehicles (3) the avoidance of the jammed routes by increasing their weight, which forces the cars to reroute and evade the jamming area. The high localization accuracy, measured by the Euclidean distance, and the successful communication of the attacker’s position and its avoidance have highly increased the packet delivery ratio (PDR) and the signal-to-interference-plus-noise ratio (SINR). This was noticed significantly before and after avoiding the jamming area when for example, the PDR increased from 0% to 100% before and after bypassing the jammer’s routes

A novel intrusion detection system against spoofing attacks in connected electric vehicles

The Electric Vehicles (EVs) market has seen rapid growth recently despite the anxiety about driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging that enables power exchange between the vehicle and the grid while the vehicle is moving. Specifically, part of the literature focuses on the intelligent routing of EVs in need of charging. Inter-Vehicle communications (IVC) play an integral role in intelligent routing of EVs around a static charging station or dynamic charging on the road network. However, IVC is vulnerable to a variety of cyber attacks such as spoofing. In this paper, a probabilistic cross-layer Intrusion Detection System (IDS), based on Machine Learning (ML) techniques, is introduced. The proposed IDS is capable of detecting spoofing attacks with more than accuracy. The IDS uses a new metric, Position Verification using Relative Speed (PVRS), which seems to have a significant effect in classification results. PVRS compares the distance between two communicating nodes that is observed by On-Board Units (OBU) and their estimated distance using the relative speed value that is calculated using interchanged signals in the Physical (PHY) layer

An Efficient Localization and Avoidance Method of Jammers in Vehicular Ad Hoc Networks

Jamming is a terrifying attack that could harm 802.11p-based vehicular communications by occupying the communication channels by overwhelming the network with jamming packets, especially for self-driving cars, as it is essential to send/receive messages without any interruptions to control the vehicles remotely. In wireless vehicular ad hoc networks (VANET), the attacker’s mission is more accessible due to the network’s open nature, way of communication, and lack of security measures. Most of the existing studies have focused on jamming detection approaches. However, few of them have addressed the jammer localization challenge. Moreover, even in these limited studies, the solutions’ assumptions, the proposed countermeasures, and their complexity were also missing. Therefore, this paper introduces a new approach to detecting, localizing, and avoiding jamming attacks in VANETs with high efficiency in terms of accuracy, implementation and complexity. The proposed approach uses the signal strength of the jammer for estimating only the distance between jammer and receiver, while then a less complex algorithm is proposed for localizing the jammer and then redirecting the vehicles away from the roads the attacker is using. This approach was simulated using real-life maps and specialized network environments. Additionally, the performance of the new approach was evaluated using different metrics. These evaluation metrics include (1) the estimated position of the jammer, (2) the handling of the jammer by announcing its location to normal vehicles (3) the avoidance of the jammed routes by increasing their weight, which forces the cars to reroute and evade the jamming area. The high localization accuracy, measured by the Euclidean distance, and the successful communication of the attacker’s position and its avoidance have highly increased the packet delivery ratio (PDR) and the signal-to-interference-plus-noise ratio (SINR). This was noticed significantly before and after avoiding the jamming area when for example, the PDR increased from 0% to 100% before and after bypassing the jammer’s routes

MIMO Techniques for Jamming Threat Suppression in Vehicular Networks

Vehicular ad hoc networks have emerged as a promising field of research and development, since they will be able to accommodate a variety of applications, ranging from infotainment to traffic management and road safety. A specific security-related concern that vehicular ad hoc networks face is how to keep communication alive in the presence of radio frequency jamming, especially during emergency situations. Multiple Input Multiple Output techniques are proven to be able to improve some crucial parameters of vehicular communications such as communication range and throughput. In this article, we investigate how Multiple Input Multiple Output techniques can be used in vehicular ad hoc networks as active defense mechanisms in order to avoid jamming threats. For this reason, a variation of spatial multiplexing is proposed, namely, vSP4, which achieves not only high throughput but also a stable diversity gain upon the interference of a malicious jammer

Route Optimization of Electric Vehicles based on Dynamic Wireless Charging

open access articleOneofthebarriersfortheadoptionofelectricvehicles(EVs)istheanxietyaroundthelimited driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging which enables power exchange between the vehicle and the grid while the vehicle is moving. In this paper, we focus on the intelligent routing of EVs in need of charging so that they can make most efficient use of the so-called mobile energy disseminators (MEDs) which operate as mobile charging stations. We present a methodforroutingEVsaroundMEDsontheroadnetwork,whichisbasedonconstraintlogicprogramming and optimization using a graph-based shortest path algorithm. The proposed method exploits inter-vehicle communications in order to eco-route electric vehicles. We argue that combining modern communications betweenvehiclesandstateofthearttechnologiesonenergytransfer,thedrivingrangeofEVscanbeextended without the need for larger batteries or overtly costly infrastructure. We present extensive simulations in city conditions that show the driving range and consequently the overall travel time of electric vehicles is improved with intelligent routing in the presence of MEDs