False data injection (FDI) attack is one of the major security threats to distributed estimation for adaptive networks, and could bypass the conventional residual-based detection and thus cause catastrophic performance deterioration. In this work, we develop a resilient distributed game-theoretic estimation algorithm comprising the Bayes-based FDI attack detection, the noncooperative game-based decision-making, and the resilient fusion strategy. Specifically, upon the detection of FDI attacks, we develop a distortionless instantaneous reward and distortionless long-term reward so as to facilitate the nodal decision-making on its action of whether to share its intermediate estimates. We further derive a best-response rule and benefit prediction scheme, such that each node could make its own independent decision on its current optimal action. Moreover, we develop a resilient fusion strategy to restrain the propagation of malicious data of each compromised node. Illustrative simulations validate the superior performance of the proposed algorithm for adversarial networks under FDI attacks.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Resilient Distributed Estimation Against FDI Attacks: A Game-Theoretic Approach


    Contributors:
    Xia, Wei (author) / Zhou, Mengqing (author) / Zhang, Yuhan (author)


    Publication date :

    2025-08-01


    Size :

    1334866 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    DRIFT: Resilient Distributed Coordinated Fleet Management Against Communication Attacks

    Owoputi, Richard / Boddupalli, Srivalli / Wilson, Jabari et al. | IEEE | 2024



    Resilient Distributed Information Fusion Under Multiple Malicious Attacks

    Hua, Yi / Wan, Fangyi / Liao, Bin et al. | IEEE | 2024