IEEE Access (Jan 2023)

Secure and Privacy-Preserving Trust Management System for Trustworthy Communications in Intelligent Transportation Systems

  • Ikram Ud Din,
  • Kamran Ahmad Awan,
  • Ahmad Almogren

DOI
https://doi.org/10.1109/ACCESS.2023.3290911
Journal volume & issue
Vol. 11
pp. 65407 – 65417

Abstract

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As Intelligent Cyber-Physical Transportation Systems (ICPTS) become increasingly complex and interconnected, the quest for secure and robust communication between diverse components and entities emerges as a significant challenge. This paper presents an innovative Context-Aware Cognitive Memory Trust Management System (CACMTM) tailored for ICPTS. By utilizing game theory to model trust interactions, our system amalgamates various trust constituents, such as evaluation, decision, update, and knowledge modules, into an integrated and dependable trust management solution, specifically addressing the unique demands of Customer Centric Communication and Networked Control for ICPTS (CNC-ICTS). The proposed approach integrates a cognitive memory-based trust management method designed explicitly for IoT in the metaverse, leveraging past experiences and adapting to the evolving behaviors of IoT entities for enhanced trust evaluation. Our approach also employs a multi-dimensional trust evaluation model considering historical behavior, reputation, and contextual information to furnish a thorough assessment of IoT entity trustworthiness, thereby minimizing the risk of false trust evaluation outcomes. Furthermore, a blockchain-secured logging mechanism integration into our trust management system, thereby bolstering security, transparency, and accountability. The working mechanism utilizes three algorithms that collectively offer an efficient, trust-aware, and adaptable framework for interactions between IoT devices and service providers. The proposed modular CACMTM architecture consists of four main modules: Trust Evaluation, Trust Decision, Trust Update, and Knowledge. A rigorous performance assessment of the CACMTM was carried out using diverse metrics and parameters such as execution time, scalability, and capability in detecting varying types of attacks. The empirical evidence gathered clearly illustrates that our approach transcends existing trust management solutions in effectively identifying and mitigating a wide range of attacks in the context of CNC-ICTS.

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