With the continuous development of Autonomous Vehicles (AVs), Intrusion Detection Systems (IDSs) became essential to ensure the security of in-vehicle (IV) networks. In the literature, classic machine learning (ML) metrics used to evaluate AI-based IV-IDSs present significant limitations and fail to assess their robustness fully. To address this, our study proposes a set of cyber resiliency metrics adapted from MITRE's Cyber Resiliency Metrics Catalog, tailored for AI-based IV-IDSs. We introduce specific calculation methods for each metric and validate their effectiveness through a simulated intrusion detection scenario. This approach aims to enhance the evaluation and resilience of IV-IDSs against advanced cyber threats and contribute to safer autonomous transportation.


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    Title :

    Poster: Cyber Resiliency Metrics for AI-Based In-Vehicle Intrusion Detection Systems


    Contributors:


    Publication date :

    2024-05-29


    Size :

    505589 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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