# Reconstructing Robot Operations via Radio-Frequency Side-Channel

arXiv: https://arxiv.org/abs/2209.10179 · HTML: https://mlsanity.com/paper/2209.10179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Multi-Scale Convolutional Neural Networks for Time Series Classification](https://mlsanity.com/paper/1603.06995/) (2016)
- [To Make a Robot Secure: An Experimental Analysis of Cyber Security Threats Against Teleoperated Surgical Robots](https://mlsanity.com/paper/1504.04339/) (2015)
- [Scanning the Internet for ROS: A View of Security in Robotics Research](https://mlsanity.com/paper/1808.03322/) (2018)
- [Leveraging Electromagnetic Side-Channel Analysis for the Investigation of IoT Devices](https://mlsanity.com/paper/1904.02089/) (2019)

## 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.