On trust models for communication security in vehicular ad-hoc networks

Fan, Na; Wu, Chase Q.

doi:10.1016/j.adhoc.2018.08.010

Cited by 51 publications

(34 citation statements)

References 15 publications

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“…One of the limitations of this work is generating extra public key (PU) and private key (PR) pair is unnecessary for newly joined vehicle. The proposed trust model in [2] considers communication behavior where the communication behavior is observed for measuring the direct reputation of the nodes through certain factor method. The indirect reputation (collected from neighbor vehicles and RSU) then combined with direct reputation through uncertain deductive method.…”

Section: Related Workmentioning

confidence: 99%

“…The automotive car onboard network is a subclass of VANET where one subclass of mobile ad hoc network (MANET) is VANET [1]. Dedicated short range communication (DSRC) occurs among the entities of the network through IEEE 802.11p [2]. The number of vehicles is increasing day by day and the probable number is forecasted to be 2 billion within 10 or 20 years.…”

Section: Introductionmentioning

confidence: 99%

“…The above issue refers to the fact that the network is highly dynamic and comprises of time-varying topology. However, this characteristic makes the network vulnerable to several types of cybersecurity threats such as message forging, wormhole attack, and black hole attack [2]. Utilizing wireless communication, limited power, error-prone transmission media makes the network vulnerable to different cybersecurity threats.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Malicious vehicle detection based on beta reputation and trust management for secure communication in smart automotive cars network

Hossain

Reza

2021

TELKOMNIKA

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High reliance on wireless network connectivity makes the vehicular ad hoc network (VANET) vulnerable to several kinds of cyber security threats. Malicious vehicles accessing the network can lead to hazardous situation by disseminating misleading information or data in the network or by performing cyber-attacks. It is a requirement that the information must be originated from the authentic and authorized vehicle and confidentiality must be maintained. In these circumstances, to protect the network from malicious vehicles, reputation system based on beta probability distribution with trust management model has been proposed to differentiate trustworthy vehicles from malicious vehicles. The trust model is based on adaptive neuro fuzzy inference system (ANFIS) which takes trust metrics as input to evaluate the trustworthiness of the vehicles. The simulation platform for the model is in MATLAB. Simulation results show that the vehicles need at least 80% trustworthiness to be considered as a trusted vehicle in the network.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Malicious vehicle detection based on beta reputation and trust management for secure communication in smart automotive cars network

Hossain

Reza

2021

TELKOMNIKA

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“…Many scholars have done a lot of research in this field. The proposed trust models are mainly based on vehicle entity-centered trust model, 7 incentive model for suppressing malicious nodes, 8 hybrid trust model for vehicle reliability evaluation, 9 solution for virtual machine migration of vehicle cloud, 10 cooperative adaptive resource scheduling method for vehicle cloud, 11 alliance game model based on bilateral matching theory, 12 and so on.…”

Section: Introductionmentioning

confidence: 99%

Research of vehicular cloud storage resource management based on trust mechanism

Wang

Zhang

et al. 2021

International Journal of Distributed Sensor Networks

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This article focuses on how to effectively make full use of the storage resources in vehicular cloud. A trust mechanism called DI-Trust (Trust Model Based on Dynamic Incentive Mechanism) is proposed to schedule vehicular cloud storage resource. The discussion is under the scenario of the parking lot where vehicle nodes are in static state. The model can reasonably arrange the suitable scheduling algorithm according to the attribute characteristics of different kind of service requirements. The trust value of a vehicle is updated according to the model to fully utilize the vehicle idle storage resources. The simulation experiment results show that the model can work effectively. It can objectively evaluate trust values of vehicle nodes, and construct the effective resource schedule of the vehicular cloud storage resource to meet needs from users.

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“…These sensors are used for obtaining the vehicles' speed and location. Due to these characteristics, implementing secure routing in VANETs, which leads to secure communication is a challenging issue [16]- [19].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles

et al. 2020

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Detection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be routed converting a secure and reliable route into a compromised one. However, instead of passing packets to a neighbouring node, malicious nodes bypass them and drop any data packets that could contain emergency alarms. We introduce an intelligent black hole attack detection scheme (IDBA) tailored to ACV. We consider four key parameters in the design of the scheme, namely, Hop Count, Destination Sequence Number, Packet Delivery Ratio (PDR), and End-to-End delay (E2E). We tested the performance of our IDBA against AODV with Black Hole (BAODV), Intrusion Detection System (IdsAODV), and EAODV algorithms. Extensive simulation results show that our IDBA outperforms existing approaches in terms of PDR, E2E, Routing Overhead, Packet Loss Rate, and Throughput.

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On trust models for communication security in vehicular ad-hoc networks

Cited by 51 publications

References 15 publications

Malicious vehicle detection based on beta reputation and trust management for secure communication in smart automotive cars network

Malicious vehicle detection based on beta reputation and trust management for secure communication in smart automotive cars network

Research of vehicular cloud storage resource management based on trust mechanism

Intelligent Detection of Black Hole Attacks for Secure Communication in Autonomous and Connected Vehicles

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