Decentralized Trust Management Model to Detect Malicious Nodes in the Internet of Vehicles

Document Type : Research Article

Authors

1 College of Engineering, School of Electrical & Computer engineering, university of Tehran, Tehran, Iran

2 College of Engineering, School of Electrical & Computer engineering, university of Tehran, Tehran, Iran

10.22080/frai.2026.30523.1030

Abstract

With the rapid expansion of the Internet of Vehicles, ensuring security and trust among nodes has emerged as a fundamental challenge in this domain. The open, dynamic, and distributed nature of these networks creates an environment conducive to malicious nodes that can compromise communication integrity and overall system security by disseminating false or misleading information. This research presents a hybrid, decentralized trust management model that, through a multilayer approach, can effectively detect and analyze malicious nodes in connected vehicular networks. The proposed framework adopts a two-layer structure: in the first layer, vehicles compute short-term local trust scores of their peers based on interaction data using the proposed LTrustAssess algorithm; while in the second layer, roadside units model the network as a graph and employ the proposed deep learning model, TemporalGATwithLSTM, to predict and update the global and long-term trust scores of nodes over time. Experimental evaluation on a dataset generated from simulated vehicular interaction logs demonstrates that the proposed model achieves higher accuracy and efficiency in the distribution of trust scores and in detecting malicious nodes than existing baseline approaches. Overall, by providing a scalable and adaptive mechanism, the proposed model enhances the security, trust, and efficiency of vehicular networks and represents a significant step toward realizing future intelligent and safe transportation systems.

Keywords

Future Research on AI and IoT
Volume 2, Issue 1
January 2026
Pages 23-45

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History

  • Receive Date: 14 November 2025
  • Revise Date: 25 January 2026
  • Accept Date: 27 January 2026
  • First Publish Date: 27 January 2026
  • Publish Date: 01 January 2026

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