  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.
