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Decentralized Trust Management Model to Detect Malicious Nodes in the Internet of Vehicles | ||
| Future Research on AI and IoT | ||
| مقاله 3، دوره 2، شماره 1، فروردین 2026، صفحه 23-45 اصل مقاله (1.18 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22080/frai.2026.30523.1030 | ||
| نویسندگان | ||
| Ali Moradi* 1؛ Nasser Yazdani2 | ||
| 1College of Engineering, School of Electrical & Computer engineering, university of Tehran, Tehran, Iran | ||
| 2College of Engineering, School of Electrical & Computer engineering, university of Tehran, Tehran, Iran | ||
| تاریخ دریافت: 23 آبان 1404، تاریخ بازنگری: 05 بهمن 1404، تاریخ پذیرش: 07 بهمن 1404 | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Internet of Vehicles؛ Graph Neural Networks؛ Malicious Node Detection؛ Trust Management؛ Trust Related Attacks | ||
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آمار تعداد مشاهده مقاله: 104 تعداد دریافت فایل اصل مقاله: 105 |
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