This paper presents a neural network-based anomaly detection system for vehicular communications. The proposed system is able to detect in-vehicle data tampering in order to avoid the transmission of bogus or harmful information. We investigate the use of Long Short-term Memory (LSTM) and Multilayer Perceptron (MLP) neural networks to build two prediction models. For each model, an efficient architecture is designed based on appropriate hardware requirements. Then, a comparative performance analysis is provided to recommend the most efficient neural network model. Finally, a set of metrics are selected to show the accuracy of the proposed detection system under several types of security attacks.


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    Titel :

    Anomaly Detection in Vehicle-to-Infrastructure Communications


    Beteiligte:
    Russo, Michele (Autor:in) / Labonne, Maxime (Autor:in) / Olivereau, Alexis (Autor:in) / Rmayti, Mohammad (Autor:in)


    Erscheinungsdatum :

    2018-06-01


    Format / Umfang :

    993721 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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