CTMF: Context-Aware Trust Management Framework for Internet of Vehicles

【Author】 Rehman, Abdul; Hassan, Mohd Fadzil; Hooi, Yew Kwang; Qureshi, Muhammad Aasim; Shukla, Saurabh; Susanto, Erwin; Rubab, Saddaf; Abdel-Aty, Abdel-Haleem

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Secure communication is the top concern of the Internet of Vehicles (IoV). The trust between nodes can have a considerable impact on ensuring IoV security. Therefore, the trustworthiness of a received message must be evaluated before acting upon it. A malicious node can broadcast bogus events to obtain network control. False reports and malicious vehicles render the network unreliable during emergencies. In this study, a unique trust framework is presented that considers most of the aspects of trust in IoV to accurately identify malicious nodes and events. Previous studies have proposed some trust models for VANETs, which have many deficiencies in serving IoV. In particular, they lack dynamism and practical implementations. All the existing models have two things in common, first they work on fixed parameters, and second, they use static scenarios. In contrast, the proposed framework is based on a context-awareness cognitive approach with artificial intelligence (AI) properties. The framework cognitively learns the environment from the received report and creates a context around an event. In addition to trust management (TM), the proposed framework offers a novel process for detecting and screening malicious nodes using anomaly outliers. The performance of the framework was examined using an experimental simulation. The proposed framework was compared with top benchmarks in the field. The results show inclining performance indicators. The proposed trust-management framework has the potential to serve as a component of IoV security.

【Keywords】Security; Context modeling; Data models; Blockchains; Vehicular ad hoc networks; Trust management; Computational modeling; Internet of Vehicles (IoV); trust management (TM); vehicular ad hoc network (VANET); context awareness

【发表时间】2022

【收录时间】2022-08-28

【文献类型】实证数据

【主题类别】

区块链应用-实体经济-交通领域