CTMF: Context-Aware Trust Management Framework for Internet of Vehicles
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 obt...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9819910/ |
| _version_ | 1818194160543334400 |
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| author | Abdul Rehman Mohd Fadzil Hassan Yew Kwang Hooi Muhammad Aasim Qureshi Saurabh Shukla Erwin Susanto Saddaf Rubab Abdel-Haleem Abdel-Aty |
| author_facet | Abdul Rehman Mohd Fadzil Hassan Yew Kwang Hooi Muhammad Aasim Qureshi Saurabh Shukla Erwin Susanto Saddaf Rubab Abdel-Haleem Abdel-Aty |
| author_sort | Abdul Rehman |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T00:57:53Z |
| format | Article |
| id | doaj.art-a3d8e45d23f3481890422b79dcb7f51a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T00:57:53Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a3d8e45d23f3481890422b79dcb7f51a2022-12-22T00:43:49ZengIEEEIEEE Access2169-35362022-01-0110736857370110.1109/ACCESS.2022.31893499819910CTMF: Context-Aware Trust Management Framework for Internet of VehiclesAbdul Rehman0https://orcid.org/0000-0001-8328-0148Mohd Fadzil Hassan1https://orcid.org/0000-0001-9912-6890Yew Kwang Hooi2https://orcid.org/0000-0003-4569-0482Muhammad Aasim Qureshi3https://orcid.org/0000-0002-6312-5797Saurabh Shukla4https://orcid.org/0000-0002-3335-373XErwin Susanto5https://orcid.org/0000-0001-5283-8604Saddaf Rubab6Abdel-Haleem Abdel-Aty7https://orcid.org/0000-0002-6763-2569Department of Information Technology, Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Quetta, PakistanCenter for Research in Data Science (CeRDaS), Universiti Teknologi PETRONAS, Seri Iskandar, Perak Darul Ridzuan, MalaysiaHigh Performance Cloud Computing Centre (HPC3), Universiti Teknologi PETRONAS, Seri Iskandar, Perak Darul Ridzuan, MalaysiaDepartment of Computer Sciences, Bahria University Lahore Campus, Lahore, PakistanInsight SFI Centre of Data Analytics, Unit of Semantic Web Data Science Institute (DSI), National University of Ireland Galway (NUIG), Galway, IrelandSchool of Electrical Engineering, Telkom University, Bandung, IndonesiaDepartment of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, United Arab EmiratesDepartment of Physics, College of Sciences, University of Bisha, Bisha, Saudi ArabiaSecure 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.https://ieeexplore.ieee.org/document/9819910/Internet of Vehicles (IoV)trust management (TM)vehicular ad hoc network (VANET)context awareness |
| spellingShingle | Abdul Rehman Mohd Fadzil Hassan Yew Kwang Hooi Muhammad Aasim Qureshi Saurabh Shukla Erwin Susanto Saddaf Rubab Abdel-Haleem Abdel-Aty CTMF: Context-Aware Trust Management Framework for Internet of Vehicles IEEE Access Internet of Vehicles (IoV) trust management (TM) vehicular ad hoc network (VANET) context awareness |
| title | CTMF: Context-Aware Trust Management Framework for Internet of Vehicles |
| title_full | CTMF: Context-Aware Trust Management Framework for Internet of Vehicles |
| title_fullStr | CTMF: Context-Aware Trust Management Framework for Internet of Vehicles |
| title_full_unstemmed | CTMF: Context-Aware Trust Management Framework for Internet of Vehicles |
| title_short | CTMF: Context-Aware Trust Management Framework for Internet of Vehicles |
| title_sort | ctmf context aware trust management framework for internet of vehicles |
| topic | Internet of Vehicles (IoV) trust management (TM) vehicular ad hoc network (VANET) context awareness |
| url | https://ieeexplore.ieee.org/document/9819910/ |
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