ArticleSensors (Basel, Switzerland)2023
Towards a Machine Learning Driven Trust Management Heuristic for the Internet of Vehicles.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- A comprehensive survey on securing the social internet of things: protocols, threat mitigation, technological integrations, tools, and performance metrics.Scientific reports · 2025Article
- Machine learning based dynamic trust estimation framework for Securing wireless sensor networks.Scientific reports · 2025Article
- MESMERIC: Machine Learning-Based Trust Management Mechanism for the Internet of Vehicles.Sensors (Basel, Switzerland) · 2024Article
- Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions.Sensors (Basel, Switzerland) · 2024Review
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Authors and funding
5 authors.
Funding
Abstract
The rapid proliferation of the emerging yet promising notion of the Internet-of-Vehicles (IoV) has led to the development of a variety of conventional trust assessment schemes to tackle insider attackers. The primary reliance of these frameworks is on the accumulation of individual trust attributes. While aggregating these influential parameters, weights are often associated with each individual attribute to reflect its impact on the final trust score. It is of paramount importance that such weights be precise to lead to an accurate trust assessment. Moreover, the value of the minimum acceptable trust threshold employed for the identification of dishonest vehicles needs to be carefully defined to avoid delayed or erroneous detection. This paper employs an IoT data set from CRAWDAD by suitably transforming it into an IoV format. This data set encompasses information regarding 18,226 interactions among 76 nodes, both honest and dishonest. First, the influencing parameters (i.e., packet delivery ratio, familiarity, timeliness and interaction frequency) were computed, and two feature matrices were formed. The first matrix (
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