Recent developments have made autonomous vehicles (AVs) closer to hitting our roads. However, their security is still a major concern among drivers as well as manufacturers. Although some work has been done to identify threats and possible solutions, a theoretical framework is needed to measure the security of AVs. In this paper, a simple security model based on defense graphs is proposed to quantitatively assess the likelihood of threats on components of an AV in the presence of available countermeasures. A Bayesian network (BN) analysis is then applied to obtain the associated security risk. In a case study, the model and the analysis are studied for GPS spoofing attacks, to demonstrate the effectiveness of the proposed approach for a highly vulnerable component.


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    Title :

    Risk Assessment of Autonomous Vehicles Using Bayesian Defense Graphs


    Contributors:


    Publication date :

    2018-08-01


    Size :

    902367 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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