Reconstructing Robot Operations via Radio-Frequency Side-Channel

While active attacks can be deadly to the Connected teleoperated robotic systems play a key role in ensuring operating environment and subject(s) involved, passive attacks can operational workflows are carried out with high levels of accuracy result in huge losses that stem from stealthy, unintentional information and low margins of error. In recent years, a variety of attacks have leakage. For example, if an attacker is able to identify what been proposed that actively target the robot itself from the cyber workflows a robot is carrying out, such as the movement of packages domain. However, little attention has been paid to the capabilities of in a warehouse between belts, they could use this information a passive attacker. In this work, we investigate whether an insider to sell on to competitors that can understand how competing warehousing adversary can accurately fingerprint robot movements and operational facilities operate and use this information to a malicious warehousing workflows via the radio frequency side channel advantage [21, 27]. in a stealthy manner. Using an SVM for classification, we found In this work we seek to explore other mechanisms to passively that an adversary can fingerprint individual robot movements with learn about robotic workflows. Side channels have previously been at least 96% accuracy, increasing to near perfect accuracy when used in different technological domains as a means to learn sensitive reconstructing entire warehousing workflows.