Crowdsourced navigation is becoming the prevalent automobile navigation solution with the widespread adoption of smartphones over the past decade, which supports a plethora of intelligent transportation system services. However, it is subjected to Sybil attacks that inject carefully designed adversarial GPS trajectories to compromise the data aggregation system and cause false traffic jams. Successful Sybil attacks have been launched against real crowdsourced navigation systems, yet defending such critical threats has seldom been studied. In this work, a novel deep generative model based on Bayesian deep learning is devised for Sybil attack identification. The proposed model exploits time-series features to embed trajectories in a latent distribution space, which serves as a basis for identifying ones generated by Sybil attacks. Case studies on three real-world vehicular trajectory datasets reveal that the proposed model improves the performance of state-of-the-art baselines by at least 76.6%. Additionally, a hyper-parameter test develops guidelines for parameter selection, and a fast training scheme is proposed and assessed to boost the model training efficiency.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Sybil Attack Identification for Crowdsourced Navigation: A Self-Supervised Deep Learning Approach


    Beteiligte:


    Erscheinungsdatum :

    01.07.2021


    Format / Umfang :

    1970846 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Dealing with Sybil Attack in VANET

    Pattanayak, Binod Kumar / Pattnaik, Omkar / Pani, Sasmita | Springer Verlag | 2020


    Fast tracing method for Sybil attack in VANETs

    Zhang, Zhaoyi / Lai, Yingxu / Chen, Ye et al. | IEEE | 2023


    CROWDSOURCED NAVIGATION SYSTEMS AND METHODS

    SULLIVAN DANIEL / ISSAC JAMES / LERNER JEREMY et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    CROWDSOURCED NAVIGATION SYSTEMS AND METHODS

    SULLIVAN DANIEL / ISSAC JAMES / LERNER JEREMY et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Trust-Aware Sybil Attack Detection for Resilient Vehicular Communication

    Thomas, Mortan / Borah, Abinash / Paranjothi, Anirudh | ArXiv | 2024

    Freier Zugriff