A Hybrid Trust Management Model for Secure and Resource Efficient Vehicular Ad hoc Networks

Considerable innovative advancements in vehicular communication have led to the emerging yet promising paradigm of Internet-of-Vehicles, wherein vehicles exchange safety-critical information with minimal delay for ensuring road safety as well as efficacious traffic flows. It is, therefore, indispensable that these safety messages are authentic and reliable, and have originated from a legitimate vehicle. This demands establishing trust among vehicles such that the malicious and dishonest vehicles (and their malicious content) could be flagged and subsequently eliminated from the network. The risk manifolds if a malicious vehicle gets elected as the cluster head of other vehicles thereby compromising the safety of vehicular passengers and pedestrians on the road. Hence, intelligent algorithms should be in place to opt for the trusted and resource efficient cluster heads which could enhance the overall security and efficiency of their respective clusters. To this end, in this paper, we have proposed a scalable hybrid trust model which takes into account a composite metric encompassing the weighted trust score and available resources of each vehicle for identification of multiple malicious vehicles in real-time, and for meeting the stringent performance requirements of vehicular safety applications. Moreover, an optimal role assignment scheme based on the Hungarian algorithm has been proposed for electing the optimal cluster head, proxy cluster head, and followers among the members of a vehicular cluster so as to maximize its overall efficiency. Preliminary simulations have been carried out and are also presented in this paper.

[1]  Anis Laouiti,et al.  Trust model for secure group leader-based communications in VANET , 2019, Wirel. Networks.

[2]  F. Richard Yu,et al.  A Deep Reinforcement Learning-based Trust Management Scheme for Software-defined Vehicular Networks , 2018, DIVANet'18.

[3]  Xiuzhen Cheng,et al.  An enhanced low overhead and stable clustering scheme for crossroads in VANETs , 2016, EURASIP J. Wirel. Commun. Netw..

[4]  Ali Balador,et al.  Malicious Node Detection in Vehicular Ad-Hoc Network Using Machine Learning and Deep Learning , 2018, 2018 IEEE Globecom Workshops (GC Wkshps).

[5]  Chinnappan Jayakumar,et al.  Trust based authentication technique for cluster based vehicular ad hoc networks (VANET) , 2018, Wirel. Networks.

[6]  Rong Yu,et al.  Distributed Reputation Management for Secure and Efficient Vehicular Edge Computing and Networks , 2017, IEEE Access.

[7]  Chai Kiat Yeo,et al.  Adaptive trust and privacy management framework for vehicular networks , 2018, Veh. Commun..

[8]  Virginia N. L. Franqueira,et al.  TEAM: A Trust Evaluation and Management Framework in Context-Enabled Vehicular Ad-Hoc Networks , 2018, IEEE Access.

[9]  F. Richard Yu,et al.  A Machine Learning Approach for Software-Defined Vehicular Ad Hoc Networks with Trust Management , 2018, 2018 IEEE Global Communications Conference (GLOBECOM).

[10]  Gang Qu,et al.  BARS: A Blockchain-Based Anonymous Reputation System for Trust Management in VANETs , 2018, 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE).

[11]  Joel J. P. C. Rodrigues,et al.  Secure and stable Vehicular Ad Hoc Network clustering algorithm based on hybrid mobility similarities and trust management scheme , 2018, Veh. Commun..