Reconstructing Robot Operations via Radio-Frequency Side-Channel
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 39 references that are in the map. First posted on arXiv 21 Sep 2022.
Builds on
6 of 39 references.
References · 6
- 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
- 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
- 2016Multi-Scale Convolutional Neural Networks for Time Series Classificationcited by 99
- 2015To Make a Robot Secure: An Experimental Analysis of Cyber Security Threats Against Teleoperated Surgical Robotscited by 8
- 2018Scanning the Internet for ROS: A View of Security in Robotics Researchcited by 6
- 2019Leveraging Electromagnetic Side-Channel Analysis for the Investigation of IoT Devicescited by 1
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