Connected Autonomous Vehicle (CAV) applications have shown the promise of transformative impact on road safety, transportation experience, and sustainability. However, they open large and complex attack surfaces: an adversary can corrupt sensory and communication inputs with catastrophic results. A key challenge in development of security solutions for CAV applications is the lack of effective infrastructure for evaluating such solutions. In this paper, we address the problem by designing an automated, flexible evaluation infrastructure for CAV security solutions. Our tool, CAVELIER, provides an extensible evaluation architecture for CAV security solutions against compromised communication and sensor channels. The tool can be customized for a variety of CAV applications and to target diverse usage models. We illustrate the framework with a number of case studies for security resiliency evaluation in Cooperative Adaptive Cruise Control (CACC).


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    Titel :

    CAVELIER: Automated Security Evaluation for Connected Autonomous Vehicle Applications


    Beteiligte:


    Erscheinungsdatum :

    2022-10-08


    Format / Umfang :

    2654055 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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