Risk Assessment of Autonomous Vehicles Using Bayesian Defense Graphs
This work addresses security concerns for autonomous vehicle manufacturers and drivers, but it is incremental as it builds on existing threat identification methods.
The paper tackles the problem of assessing security risks in autonomous vehicles by proposing a defense graph model and applying Bayesian network analysis to quantify threat likelihood, demonstrating effectiveness in a GPS spoofing case study.
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.