SAVIOR: Securing Autonomous Vehicles with Robust Physical Invariants
SAVIOR: Securing Autonomous Vehicles with Robust Physical Invariants
复制标题
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Raul Quinonez;J. Giraldo;Luis E. Salazar;Erick Bauman;A. Cárdenas;Zhiqiang Lin
中科院分区：
文献类型：
--
作者：
Raul Quinonez;J. Giraldo;Luis E. Salazar;Erick Bauman;A. Cárdenas;Zhiqiang Lin
Autonomous Vehicles (AVs), including aerial, sea, and ground vehicles, assess their environment with a variety of sensors and actuators that allow them to perform speci fi c tasks such as navigation a route, hovering, or avoiding collisions. So far, AVs tend to trust the information provided by their sensors to make navigation decisions without data validation or veri fi cation, and therefore, attackers can explore these limitations by feeding erroneous sensor data为了破坏或控制系统。
Autonomous Vehicles (AVs), including aerial, sea, and ground vehicles, assess their environment with a variety of sensors and actuators that allow them to perform speci fi c tasks such as navigating a route, hovering, or avoiding collisions. So far, AVs tend to trust the information provided by their sensors to make navigation decisions without data validation or veri fi cation, and therefore, attackers can exploit these limitations by feeding erroneous sensor data with the intention of disrupting or taking control of the system. In this paper we introduce SAVIOR: an architecture for securing autonomous vehicles with robust physical invariants. We implement and validate our proposal on two popular open-source controllers for aerial and ground vehicles, and demonstrate its effectiveness.