Connected and Autonomous Vehicles (CAVs) provide significant societal benefits but pose serious security risks due to their high connectivity and openness. Traditional security measures like cryptography and intrusion detection systems (IDSs) are reactive and passive, posing significant challenges to securing CAVs. We propose a proactive and collaborative threat-sharing framework to tackle the above challenges and enhance CAV security through vehicular honeypots. The proposed framework leverages federated learning, which allows CAVs to share threat information decentralized while preserving their privacy. Additionally, we design an optimal incentive mechanism that considers three private information of CAVs, including deployment, training, and communication costs. Specifically, we leverage the self-disclosure property of the contract theory, which can effectively address information asymmetry and incentive mismatches between CAVs and the IDS server, motivating CAVs to participate in threat sharing. Finally, through a series of simu- lation experiments, we validate the feasibility of the contract and evaluate the effectiveness of our proposed incentive mechanism.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Federated Learning based Vehicular Threat Sharing: A Multi-Dimensional Contract Incentive Approach


    Beteiligte:
    He, Chao (Autor:in) / Luan, Tom H. (Autor:in) / Cheng, Nan (Autor:in) / Wei, Guiyi (Autor:in) / Su, Zhou (Autor:in) / Liu, Yiliang (Autor:in)


    Erscheinungsdatum :

    10.10.2023


    Format / Umfang :

    1177200 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Contract Theory Based Incentive Mechanism for Clustered Vehicular Federated Learning

    Wang, Siyang / Zhao, Haitao / Wen, Wanli et al. | IEEE | 2024



    Incentive Based Federated Learning Data Dissemination for Vehicular Edge Computing Networks

    Bute, Muhammad Saleh / Fan, Pingzhi / Luo, Quyuan | IEEE | 2023


    Multi-dimensional Contract Matching Design for Federated Learning in UAV Networks

    Lim, Wei Yang Bryan / Ng, Jer Shyuan / Xiong, Zehui et al. | Springer Verlag | 2022


    Blockchain-Enabled Federated Learning Approach for Vehicular Networks

    Sultana, Shirin / Hossain, Jahin / Billah, Maruf et al. | ArXiv | 2023

    Freier Zugriff