DETECTION AND MITIGATION OF ERRONEOUS AND MALICIOUS DATA IN VEHICLE SENSOR NETWORKS

This paper describes research into the applicability of anomaly detection algorithms using machine learning and time-magnitude thresholding to determine when an autonomous vehicle sensor network has been subjected to a cyber-attack or sensor error. While the research community has been active in autonomous vehicle vulnerability exploitation, there are often no well-established solutions to address these threats. In order to better address the lag, it is necessary to develop generalizable solutions which can be applied broadly across a variety of vehicle sensors. The current measured results achieved for time-magnitude thresholding during this research shows a promising aptitude for anomaly detection on direct sensor data in autonomous vehicle platforms. The results of this research can lead to a solution that fully addresses concerns of cyber-security and information assurance in autonomous vehicles.

Citation: R. McBee, J. Wolford, A. Garza, “Detection and Mitigation of Erroneous and Malicious Data in Vehicle Sensor Networks,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 16-18, 2022