Research Article
VET: Autonomous Vehicular Credential Verification Using Trajectory and Motion Vectors
@INPROCEEDINGS{10.1007/978-3-031-64954-7_8, author={Ebuka Oguchi and Nirnimesh Ghose}, title={VET: Autonomous Vehicular Credential Verification Using Trajectory and Motion Vectors}, proceedings={Security and Privacy in Communication Networks. 19th EAI International Conference, SecureComm 2023, Hong Kong, China, October 19-21, 2023, Proceedings, Part II}, proceedings_a={SECURECOMM PART 2}, year={2024}, month={10}, keywords={Location and Motion based-Authentication Autonomous VANET Frequency-of-Arrival Direct Location Estimation}, doi={10.1007/978-3-031-64954-7_8} }- Ebuka Oguchi
Nirnimesh Ghose
Year: 2024
VET: Autonomous Vehicular Credential Verification Using Trajectory and Motion Vectors
SECURECOMM PART 2
Springer
DOI: 10.1007/978-3-031-64954-7_8
Abstract
There has been significant progress in autonomous vehicles: autonomous automobiles, unmanned aerial vehicles, and many more are improving our quality of life and making it safer. However, this also opens up a new attack paradigm: now, an adversary can take control of these autonomous systems to cause life-threatening scenarios. It becomes possible due to the broadcast nature of wireless communication, which connects autonomous vehicles in an ad-hoc network. Traditional crypto-algorithms alone cannot tackle the problem as the crypto-credentials can be compromised or even issued to adversarial parties. We propose VET: a framework that verifies the veracity of the crypto-credentials by authenticating them against physical trajectory and motion vectors (TMVs). The verifier implements a location and motion-based authentication to verify the crypto-credentials based on the acceptability of claimed TMVs against randomly estimated TMVs. This prevents any adversary from remotely injecting spoofed messages when it is not physically present. We formally analyze the correctness and robustness of VET using matching conversations. Finally, we attest to the findings of the theoretical analysis using an experimentally analyzed VET on the USRP platform. Our experiments show that VET has 97% true positives when operating without an adversary. Also, VET can detect advanced remote adversaries with 99.9% who is capable of manipulating signals with absolute channel knowledge.
