ADSecData Platform: An Open-Source Data Platform for Autonomous Driving Cybersecurity

Autonomous driving (AD) software needs to be secure, and its decision control must be robust against cyber threats. The development of cybersecurity solutions for legacy and connected vehicles has been supported by an array of open-source datasets, mainly focused on the CAN Bus protocol. There exists a lack of open-source cybersecurity data and community-driven platforms that enable fair and reproducible evaluations of AD algorithms from a cybersecurity perspective and defensive mechanisms. This study addresses this problem by conducting an in-depth analysis of the data ecosystem for AD cybersecurity and introducing an initial open-source data platform, ADSecData. ADSecData offers the community a comprehensive 4-stage method for the creation of AD cybersecurity datasets, along with an initial common dataset. We evaluate the utility of ADSecData through a case study featuring diverse malicious injection attacks, including GPS spoofing, LiDAR point-cloud manipulation, and sensor interference. The results demonstrate the viability of ADSecData in generating AD cybersecurity datasets and supporting community research and development.

ADSecData Platform: An Open-Source Data Platform for Autonomous Driving Cybersecurity

Andrew Roberts, Mohsen Malayjerdi, Tallinn University of Technology; Mauro Bellone, FinEst Centre for Smart Cities; Raivo Sell, Tallinn University of Technology; Olaf Maennel, University of Adelaide; Mohammad Hamad, Sebastian Steinhorst, Technical University of Munich