Context and machine learning based trust management framework for internet of vehicles

• Main Authors: Rehman, A., Hassan, M.F., Hooi, Y.K., Qureshi, M.A., Chung, T.D., Akbar, R., Safdar, S.
• Format: Article
• Published: Tech Science Press 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105607500&doi=10.32604%2fCMC.2021.017620&partnerID=40&md5=a18901010db67ae00ccea9b9f353d8cc; http://eprints.utp.edu.my/23927/
• Institution: Universiti Teknologi Petronas

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 TMmodels 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 cyberattacks. The proposedTMframework 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. © 2021 Tech Science Press. All rights reserved.