NOTRINO: A NOvel Hybrid TRust Management Scheme for INternet-of-Vehicles

Farhan Ahmad, Fatih Kurugollu, Chaker Abdelaziz Kerrache, Sakir Sezer, Lu Liu

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)

Abstract

Internet-of-Vehicles (IoV) is a novel technology to ensure safe and secure transportation by enabling smart vehicles to communicate and share sensitive information with each other. However, the realization of IoV in real-life depends on several factors, including the assurance of security from attackers and propagation of authentic, accurate and trusted information within the network. Further, the dissemination of compromised information must be detected and vehicle disseminating such malicious messages must be revoked from the network. To this end, trust can be integrated within the network to detect the trustworthiness of the received information. However, most of the trust models in the literature relies on evaluating node or data at the application layer. In this study, we propose a novel hybrid trust management scheme, namely, NOTRINO, which evaluates trustworthiness on the received information in two steps. First step evaluates trust on the node itself at transport layer, while second step computes trustworthiness of the data at application layer. This mechanism enables the vehicles to efficiently model and evaluate the trustworthiness on the received information. The performance and accuracy of NOTRINO is rigorously evaluated under various realistic trust evaluation criteria (including precision, recall, F-measure and trust). Furthermore, the efficiency of NOTRINO is evaluated in presence of malicious nodes and its performance is benchmarked against three hybrid trust models. Extensive simulations indicate that NOTRINO achieve over 75% trust level as compared to benchmarked trust models where trust level falls below 60% for a network with 35% malicious nodes. Similarly, 92% precision and 87% recall are achieved simultaneously with NOTRINO for the same network, comparing to benchmark trust models where precision and recall falls below 87% and 85% respectively.

Original languageEnglish
Article number9314234
Pages (from-to)9244-9257
Number of pages14
JournalIEEE Transactions on Vehicular Technology
Volume70
Issue number9
Early online date5 Jan 2021
DOIs
Publication statusPublished - 17 Sep 2021
Externally publishedYes

Cite this