Towards A Distributed Ledger Based Verifiable Trusted Protocol For VANET

Aftab, Muhammad Usman; Hussain, Mehdi; Lindgren, Anders; Ghafoor, Abdul

doi:10.1109/icodt252288.2021.9441531

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…This model is analyzed considering the recent history of interactions but not considering any attacker model. In [17], the authors introduce a blockchainbased trust management protocol for VANETs to update of trust of vehicles as low, medium, and high at the Trust Authority (TA) using tamper-proof logs periodically. However, this is not validated using any known attack.…”

Section: Related Workmentioning

confidence: 99%

A Trust Management Framework for Vehicular Ad Hoc Networks

Shahariar¹,

Phillips²

2023

IJSPTM

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Vehicular Ad Hoc Networks (VANETs) enable road users and public infrastructure to share information that improves the operation of roads and driver experience. However, these are vulnerable to poorly behaved authorized users. Trust management is used to address attacks from authorized users in accordance with their trust score. By removing the dissemination of trust metrics in the validation process, communication overhead and response time are lowered. In this paper, we propose a new Tamper-Proof Device (TPD) based trust management framework for controlling trust at the sender side vehicle that regulates driver behaviour. Moreover, the dissemination of feedback is only required when there is conflicting information in the VANET. If a conflict arises, the Road-Side Unit (RSU) decides, using the weighted voting system, whether the originator is to be believed, or not. The framework is evaluated against a centralized reputation approach and the results demonstrate that it outperforms the latter.

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Section: Related Workmentioning

confidence: 99%

A Trust Management Framework for Vehicular Ad Hoc Networks

Shahariar¹,

Phillips²

2023

IJSPTM

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“…Blockchain, implemented on application layer, has been considered as a potential solution to resolve security and privacy issues in vehicular networks [6]. It has been proven resilient against various attacks, such as repudiation [7], DoS, Sybil [8] and poisoning attack [9]. A permissioned blockchain not only ensures security but also privacy, by allowing only authorized nodes to join and communicate in a network [10].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-enabled FD-NOMA based Vehicular Network with Physical Layer Security

Ayaz

Sheng

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Vehicular networks are vulnerable to large scale attacks. Blockchain, implemented upon application layer, is recommended as one of the effective security and privacy solutions for vehicular networks. However, due to an increasing complexity of connected nodes, heterogeneous environment and rising threats, a robust security solution across multiple layers is required. Motivated by the Physical Layer Security (PLS) which utilizes physical layer characteristics such as channel fading to ensure reliable and confidential transmission, in this paper we analyze the impact of PLS on a blockchain-enabled vehicular network with two types of physical layer attacks, i.e., jamming and eavesdropping. Throughout the analysis, a Full Duplex Non-Orthogonal Multiple Access (FD-NOMA) based vehicle-to-everything (V2X) is considered to reduce interference caused by jamming and meet 5G communication requirements. Simulation results show enhanced goodput of a blockckchain enabled vehicular network integrated with PLS as compared to the same solution without PLS.

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“…An efficient VANET solution is obtained with trust. The security threats in a dynamic and highly distributed situation such as VANETs deal with the trust model (Nadarajan and Kaliyaperumal, 2021; Raut and Vishwamitra, 2021; Li et al , 2021; Krishnan and Kanmani, 2014; Aftab et al , 2021; Abinaya and Arulkumaran, 2017; Shajin and Rajesh, 2020).…”

Section: Introductionmentioning

confidence: 99%

An optimized deep learning-based trust mechanism In VANET for selfish node detection

Jyothi¹,

Patil²

2021

IJPCC

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Purpose This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection. Design/methodology/approach The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to identify the type of vehicle in the non-line of sight (nLoS) condition. This authentication scheme satisfies both the security and privacy goals of the VANET environment. The message authenticity and integrity are verified using the vehicle location to determine the trust level. The location is verified via distance and time. It identifies whether the sender is in its actual location based on the time and distance. Findings A deep learning-based optimized Trust model is used to detect the obstacles that are present in both the line of sight and nLoS conditions to reduce the accident rate. While compared to the previous methods, the experimental results outperform better prediction results in terms of accuracy, precision, recall, computational cost and communication overhead. Practical implications The experiments are conducted using the Network Simulator Version 2 simulator and evaluated using different performance metrics including computational cost, accuracy, precision, recall and communication overhead with simple attack and opinion tampering attack. However, the proposed method provided better prediction results in terms of computational cost, accuracy, precision, recall, and communication overhead than other existing methods, such as K-nearest neighbor and Artificial Neural Network. Hence, the proposed method highly against the simple attack and opinion tampering attacks. Originality/value This paper proposed a deep learning-based optimized Trust framework for trust prediction in VANET. A deep learning-based optimized Trust model is used to evaluate both event message senders and event message integrity and accuracy.

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Towards A Distributed Ledger Based Verifiable Trusted Protocol For VANET

Cited by 3 publications

References 19 publications

A Trust Management Framework for Vehicular Ad Hoc Networks

A Trust Management Framework for Vehicular Ad Hoc Networks

Blockchain-enabled FD-NOMA based Vehicular Network with Physical Layer Security

An optimized deep learning-based trust mechanism In VANET for selfish node detection

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