Towards faster big data analytics for anti‐jamming applications in vehicular ad‐hoc network

Bangui, Hind; Ge, Mouzhi; Bühnová, Barbora; Trang, Le Hong

doi:10.1002/ett.4280

Cited by 10 publications

(3 citation statements)

References 69 publications

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“…The most effective signal transmission model that best combines the receiver should also be selected if efficient jamming is accomplished in a VANET. researchers, including [17,65], examined some strategies to minimize the impact of jamming on ad hoc mobile networks.…”

Section: Location Privacy Attack Methods and Prevention For Iot In Vanetmentioning

confidence: 99%

A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks

Ahmed

Deng

Memon

et al. 2022

Wireless Communications and Mobile Computing

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Vehicular ad hoc networks (VANETs) connect two or more vehicles wirelessly to enable data exchange in an Internet of Things (IoT) environment. In VANETs, location privacy is the most crucial piece of information, and its protection is the top priority. However, the location privacy threats have not been adequately addressed in positioning for IoT in VANETs. This paper provides an overview of location privacy attacks and their solutions to address the problems caused by attacks in any IoT environment. Secondly, we have analyzed specific solutions based on anonymity (pseudonym) and cryptographic solutions using a digital signature technique. This enables to improve user privacy and security of location-based services for IoT in VANET. Moreover, we have proposed a faster 5G solution for the VANETs as it rapidly disseminates the data in fast-moving vehicles.

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Section: Location Privacy Attack Methods and Prevention For Iot In Vanetmentioning

confidence: 99%

A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks

Ahmed

Deng

Memon

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…The authors of [12] propose a jamming detection scheme that can be used to protect vehicle platoon applications. The purpose of jamming detection is to ensure that vehicles are not disturbed by a jammer by sending control messages to maintain the inter vehicle spacing.…”

Section: Related Workmentioning

confidence: 99%

VANET Jamming and Adversarial Attack Defense for Autonomous Vehicle Safety

Kim¹,

Chung²

2022

Computers, Materials &Amp; Continua

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The development of Vehicular Ad-hoc Network (VANET) technology is helping Intelligent Transportation System (ITS) services to become a reality. Vehicles can use VANETs to communicate safety messages on the road (while driving) and can inform their location and share road condition information in real-time. However, intentional and unintentional (e.g., packet/frame collision) wireless signal jamming can occur, which will degrade the quality of communication over the channel, preventing the reception of safety messages, and thereby posing a safety hazard to the vehicle's passengers. In this paper, VANET jamming detection applying Support Vector Machine (SVM) machine learning technology is used to classify jamming and non-jamming situations. The analysis is based on two cases which include normal traffic and heavy traffic conditions, where the results show that the probability of packet dropping will increase when many vehicles are using the wireless channel simultaneously. When using SVM classification, the most appropriate feature set applied in determining a jamming situation shows an accuracy of 98% or higher. Furthermore, more advanced jamming attacks need to be considered for preparation of more reliable and safer autonomous ITS services. Such research can use vehicular communication transmission and reception data based on selected published datasets. In this paper, an additional adversarial defense algorithm using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is proposed, which assumes that evolutionary attacks of the jammer will attempt to confuse the trained classifier. The simulation results show that applying DBSCAN can improve the accuracy by elimination of outliers before conducting classification testing.

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“…Due to the emergence and significance of this field, various solutions have been put forward to prevent RF jamming attacks [11], [12], [13]. An efficient detection algorithm is essential in anti-jamming schemes to ensure secure wireless communication.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Towards faster big data analytics for anti‐jamming applications in vehicular ad‐hoc network

Cited by 10 publications

References 69 publications

A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks

A Survey on Location Privacy Attacks and Prevention Deployed with IoT in Vehicular Networks

VANET Jamming and Adversarial Attack Defense for Autonomous Vehicle Safety

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

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