Open Access

ARTICLE

Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

Abdul Rehman^1,*, Mohd Fadzil Hassan¹, Yew Kwang Hooi¹, Muhammad Aasim Qureshi², Tran Duc Chung³, Rehan Akbar⁴, Sohail Safdar⁵

1 Computer and Information Science Department, Centre for Research and Data Science (CeRDaS), Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia
2 Department of Computer Science, Bahria University, Pakistan
3 Computing Fundamental Department, FPT University, Hoa Lac Hi-Tech Park, Hanoi, Vietnam
4 Department of Information Systems, Universiti Tunku Abdul Rahman, Malaysia
5 Information Technology Department College of IT, AHLIA University, Bahrain

* Corresponding Author: Abdul Rehman. Email:

(This article belongs to the Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Computers, Materials & Continua 2021, 68(3), 4125-4142. https://doi.org/10.32604/CMC.2021.017620

Received 05 February 2021; Accepted 22 March 2021; Issue published 06 May 2021

Abstract

Trust is one of the core components of any ad hoc network security system. Trust management (TM) has always been a challenging issue in a vehicular network. One such developing network is the Internet of vehicles (IoV), which is expected to be an essential part of smart cities. IoV originated from the merger of Vehicular ad hoc networks (VANET) and the Internet of things (IoT). Security is one of the main barriers in the on-road IoV implementation. Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements. Trust plays a vital role in ensuring security, especially during vehicle to vehicle communication. Vehicular networks, having a unique nature among other wireless ad hoc networks, require dedicated efforts to develop trust protocols. Current TM schemes are inflexible and static. Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks. The vehicular network requires agile and adaptive solutions to ensure security, especially when it comes to critical messages. The vehicle network's wireless nature increases its attack surface and exposes the network to numerous security threats. Moreover, internet involvement makes it more vulnerable to cyber-attacks. The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics. Machine learning is the best solution to utilize the big data produced by vehicle sensors. To handle the uncertainty Bayesian machine learning statistical model is used. The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available. The results indicated better performance than existing TM methods. Furthermore, for future work, a high-level machine learning model is proposed.

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Citations