Latent Dirichlet Allocation (LDA) and Variational Inference are applied in near real-time to detect anomalies in ground vehicle network traffic for a ground vehicle network. The technical approach, that utilizes the Natural Language Processing (NLP) technique to detect potential malicious attacks and network configuration issues, is described and the results of a proof of concept implementation are provided. Potential use cases for applying the technique in the aircraft and avionics domain are provided.


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    Titel :

    Latent Dirichlet Allocation (LDA) for Anomaly Detection in Avionics Networks


    Beteiligte:
    Thornton, Adam (Autor:in) / Meiners, Brandon (Autor:in) / Poole, Donald (Autor:in)


    Erscheinungsdatum :

    11.10.2020


    Format / Umfang :

    1159986 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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