This paper introduces a novel intrusion detection system (IDS) suitable for both CAN and CAN-FD protocols, which are widely used in vehicle networks. Since most existing machine learning-based IDS are designed without considering the CAN-FD data frame, their application to CAN-FD systems increases model complexity or complicate implementation. This issue is particularly noticeable in models that use data payloads as input features, since CAN allows data payloads of up to 8 bytes, whereas CAN-FD is capable of up to 64 bytes. To address this problem, we propose a novel approach that utilizes CAN ID and CRC fields. This is because the CRC field is calculated based on the data field in both CAN and CAN-FD protocols, which eliminates the need to separately adopt the data field as a feature. We evaluated the performance of an LSTM-based IDS trained with these features. Experimental results confirm that our IDS achieves near-perfect detection performance and is significantly more lightweight compared to existing models.


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    Titel :

    A Novel Intrusion Detection System Using Cyclic Redundancy Check Field for In-Vehicle Networks


    Beteiligte:
    Im, Hyungchul (Autor:in) / Park, Naeun (Autor:in) / Lee, Seongsoo (Autor:in)


    Erscheinungsdatum :

    07.10.2024


    Format / Umfang :

    1101610 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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