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Published VersionCC BY V4.0, Open Access
Journal article
Blockchain driven trust management for intelligent transportation systems in VANETs
Scientific Reports, Vol.15, pp.1-17
2025
PMID: 41224824
Abstract
Vehicular Ad Hoc Networks (VANETs) are a key component of Intelligent Transportation Systems (ITS), enabling vehicle-to-everything (V2X) communication to improve traffic efficiency and road safety. However, maintaining strong network performance and user privacy requires robust security mechanisms. Unlike existing blockchain-VANET solutions that address isolated aspects, our framework uniquely integrates Physical Unclonable Functions (PUFs) for lightweight authentication, an Enhanced Quantified role-based Practical Byzantine Fault Tolerance (EQPBFT) consensus mechanism for efficient and secure block creation, and a Deep Neural Network (DNN)-based intrusion detection system into a unified trust management pipeline. The EQPBFT algorithm improves network resilience by efficiently identifying and mitigating malicious nodes during block creation. In addition, a DNN-based intrusion detection system enhances threat classification and strengthens attack detection accuracy. Simulation results demonstrate reduced computation time (up to 25% lower than PBFT), faster block creation (5.2 ms vs. 6.6 ms for 200 nodes), and higher intrusion detection accuracy (95%) compared to baseline models. These quantitative outcomes validate the robustness and efficiency of the proposed system.
Details
- Title
- Blockchain driven trust management for intelligent transportation systems in VANETs
- Authors
- V. Hariharasudhan - Vellore Institute of Technology UniversityP. Vetrivelan (Corresponding Author) - Vellore Institute of Technology UniversityElizabeth Chang - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Scientific Reports, Vol.15, pp.1-17
- Publisher
- Nature Publishing Group
- Date published
- 2025
- DOI
- 10.1038/s41598-025-23351-x
- ISSN
- 2045-2322
- PMID
- 41224824
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Data Availability
- Publicly available datasets were analyzed in this study. The data can be accessed at: https://www.kaggle.com/datasets/chethuhn/network-intrusion-dataset.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991192144502621
- Output Type
- Journal article
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