With the ongoing efforts toward autonomous driving, modern vehicles become increasingly digital and smart. Hence, the vehicle architecture including smart sensors, ECUs, and in-vehicle communication also faces new challenges to satisfy the ever-changing safety and security requirements. The complexity of the system naturally exposes many attack surfaces that demand for sound security solutions to protect the vehicle from potential intrusions. State-of-the-art approaches such as intrusion detection and intrusion response systems require lots of training and testing against various attack scenarios. However, implementing such attacks in real environments is difficult, expensive, and involves many legal and safety considerations. With Simutack, we present an open-source attack simulation framework that is capable of generating realistic attack scenarios for comprehensive security testing in the automotive development process. The framework integrates several classes of attacks, for instance, smart sensor attacks, V2X attacks, and attacks targeting the in-vehicle networks, which are all among the most commonly exploited attack vectors. We evaluate three common attack scenarios that showcase the applicability and capabilities of our work. In each scenario, the generated attack data is processed and returned to the simulation to visualize the attack’s effect on the vehicle and its environment. Furthermore, a custom autopilot application demonstrates the attack’s impact on autonomous driving systems.


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    Titel :

    Simutack - An Attack Simulation Framework for Connected and Autonomous Vehicles


    Beteiligte:


    Erscheinungsdatum :

    01.06.2023


    Format / Umfang :

    5734728 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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