Reconstructing Robot Operations via Radio-Frequency Side-Channel
This addresses security vulnerabilities in teleoperated robotic systems for industries relying on precise operations, though it is incremental as it builds on existing side-channel analysis methods.
The paper tackled the problem of passive attackers fingerprinting robot movements and warehousing workflows via radio-frequency side-channels, achieving at least 96% accuracy for individual movements and near-perfect accuracy for entire workflows.
Connected teleoperated robotic systems play a key role in ensuring operational workflows are carried out with high levels of accuracy and low margins of error. In recent years, a variety of attacks have been proposed that actively target the robot itself from the cyber domain. However, little attention has been paid to the capabilities of a passive attacker. In this work, we investigate whether an insider adversary can accurately fingerprint robot movements and operational warehousing workflows via the radio frequency side channel in a stealthy manner. Using an SVM for classification, we found that an adversary can fingerprint individual robot movements with at least 96% accuracy, increasing to near perfect accuracy when reconstructing entire warehousing workflows.