Risk assessment of autonomous vehicles using Bayesian defense graphs
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Behfarnia, Ali; Eslami, Ali. 2019. Risk assessment of autonomous vehicles using Bayesian defense graphs. 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall):Article 8690732
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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