HC-TUS: Human Cognition-based Trust Update Scheme for AI-enabled VANET

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IEEE Network


Recently, Artificial Intelligence (AI) has received more attention for being used in many applications. It is expected to play a key role in Vehicular Ad Hoc Networks (VANET). On the other hand, AI-enabled VANET (AI-VANET) has become an emerging field. However, its cyber security is facing enormous challenges. Although many trust schemes are proposed for addressing these issues, the reliance on only trust updates could increase the risk of long-term attacks before being detected. In this article, we design a human cognition-based trust update scheme (HC-TUS) for AI-VANET. Significantly, the novel trust update scheme is designed by strategically incorporating the Ebbinghaus forgetting theory. The simulation results indicate that 1) HC-TUS could better meet the principle of “Hard to get, easy to lose” for trust than BRSN and BTDS; 2) HC-TUS could detect and resist the collusion attack more quickly than BRSN and BTDS. The open issues in terms of trust for AI-VANET are also investigated and highlighted.



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Artificial intelligence, Artificial Intelligence (AI), Blockchains, Collusion Attack, Computational modeling, Resists, Security, Trust management, Trust Model, Vehicular Ad Hoc Network (VANET), Vehicular ad hoc networks

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