Risk Assessment of Autonomous Vehicles Using Bayesian Defense Graphs
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- March 2019
- DOI:
- 10.48550/arXiv.1903.02034
- arXiv:
- arXiv:1903.02034
- Bibcode:
- 2019arXiv190302034B
- Keywords:
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- Computer Science - Cryptography and Security
- E-Print:
- IEEE 88th Vehicular Technology Conference: VTC2018-Fall