A Model-based Cyber Attack Detection and Identification for Networked Vehicle Systems
Primary Investigator:
Byung-Cheol Min
Sangjun Lee and Byung-Cheol Min
Abstract
This study proposes a Direction of Arrival (DoA)-aided attack detection scheme to identify cyberattacks on networked multi-vehicle systems. For each agent, a local estimator is designed to generate robust residuals, and a parametric statistical tool corresponding to the residuals is elaborated to build sensitive decision rules. These locally stored residuals and thresholds are shared between vehicles via a wireless network, allowing a multi-vehicle system to complete its mission in the presence of one or more compromised agents. The proposed DoA-aided attack detection scheme is tested on a multi-vehicle testbed with a team of 10 robots. Experimental results demonstrate that the proposed detection scheme enables each robot to identify malicious activities without shearing the global coordination.