# Can You Still See Me?: Reconstructing Robot Operations Over End-to-End Encrypted Channels

arXiv: https://arxiv.org/abs/2205.08426 · HTML: https://mlsanity.com/paper/2205.08426/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 93 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Convergence Analysis of Two-layer Neural Networks with ReLU Activation](https://mlsanity.com/paper/1705.09886/) (2017)
- [Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning](https://mlsanity.com/paper/1709.02656/) (2017)
- [Deep Learning for Encrypted Traffic Classification: An Overview](https://mlsanity.com/paper/1810.07906/) (2018)
- [Device Fingerprinting in Wireless Networks: Challenges and Opportunities](https://mlsanity.com/paper/1501.01367/) (2015)
- [To Make a Robot Secure: An Experimental Analysis of Cyber Security Threats Against Teleoperated Surgical Robots](https://mlsanity.com/paper/1504.04339/) (2015)
- [IoTSense: Behavioral Fingerprinting of IoT Devices](https://mlsanity.com/paper/1804.03852/) (2018)
- [Scanning the Internet for ROS: A View of Security in Robotics Research](https://mlsanity.com/paper/1808.03322/) (2018)
- [Toward an Efficient Website Fingerprinting Defense](https://mlsanity.com/paper/1512.00524/) (2015)
- [SROS: Securing ROS over the wire, in the graph, and through the kernel](https://mlsanity.com/paper/1611.07060/) (2016)
- [Padding Ain't Enough: Assessing the Privacy Guarantees of Encrypted DNS](https://mlsanity.com/paper/1907.01317/) (2019)

## Built on by (most cited first, 1 of 1)

- [Fingerprinting Robot Movements via Acoustic Side Channel](https://mlsanity.com/paper/2209.10240/) (2022)

## Abstract (arXiv)

Connected robots play a key role in Industry 4.0, providing automation and higher efficiency for many industrial workflows. Unfortunately, these robots can leak sensitive information regarding these operational workflows to remote adversaries. While there exists mandates for the use of end-to-end encryption for data transmission in such settings, it is entirely possible for passive adversaries to fingerprint and reconstruct entire workflows being carried out -- establishing an understanding of how facilities operate. In this paper, we investigate whether a remote attacker can accurately fingerprint robot movements and ultimately reconstruct operational workflows. Using a neural network approach to traffic analysis, we find that one can predict TLS-encrypted movements with around ~60% accuracy, increasing to near-perfect accuracy under realistic network conditions. Further, we also find that attackers can reconstruct warehousing workflows with similar success. Ultimately, simply adopting best cybersecurity practices is clearly not enough to stop even weak (passive) adversaries.