Title: Social network perspective on false information detection in vehicular ad hoc networks: combining spatial inference with historical behaviour analysis

Authors: Youke Wu

Addresses: School of Economics and Management, Wuyi University, Jiangmen City, Guangdong Province, China

Abstract: The rapid development of vehicular ad hoc networks (VANETs) has raised concerns about the spread of false information, which threatens road safety and the stability of VANET ecosystems. This study proposes an innovative hybrid detection strategy that combines spatial inference and historical behaviour analysis to detect false information in VANET environments. By optimising data collection techniques and computational algorithms, the research examines the effectiveness of spatial inference models across VANET social networks of varying scales. A vehicle historical behaviour model is developed to improve detection accuracy. The findings show that the hybrid strategy enhances detection efficiency, making it suitable for monitoring false information in VANETs. Policy recommendations include strengthening data privacy measures, improving communication infrastructure, and establishing regulatory mechanisms to further enhance false information detection and prevention in VANET systems.

Keywords: vehicular ad hoc network; VANET; false information; hybrid detection; security.

DOI: 10.1504/IJCSE.2025.148741

International Journal of Computational Science and Engineering, 2025 Vol.28 No.5, pp.487 - 497

Received: 17 May 2024
Accepted: 07 Aug 2024
Published online: 22 Sep 2025 *

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