# Risk Assessment of Autonomous Vehicles Using Bayesian Defense Graphs

## TL;DR

This paper introduces a Bayesian defense graph framework to quantitatively assess security risks in autonomous vehicles, focusing on threat likelihood and countermeasures, demonstrated through a GPS spoofing case study.

## Contribution

It presents a novel security assessment model combining defense graphs and Bayesian networks for autonomous vehicle components.

## Key findings

- Effective risk quantification for AV threats
- Demonstrated approach on GPS spoofing attack
- Provides a framework for security evaluation in AVs

## 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.