Projects per year
Abstract
Connected robots play a key role in automating industrial workflows. Robots can expose sensitive operational information to remote adversaries. Despite the use of end-to-end encryption, a passive adversary could fingerprint and reconstruct the entire workflows being carried out and developing a detailed understanding of how facilities operate. In this paper, we investigate whether a remote passive attacker can accurately fingerprint robot movements and reconstruct operational workflows. Using a neural network-based traffic analysis approach, we found that attackers can predict TLS-encrypted robot movements with around ~60% accuracy, increasing to near perfect accuracy in realistic settings. Ultimately, simply adopting best cybersecurity practices is not enough to stop even weak (passive) adversaries.
Original language | English |
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Title of host publication | WiSec 2022 - Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks |
Subtitle of host publication | Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks |
Place of Publication | New York |
Pages | 298-300 |
Number of pages | 3 |
ISBN (Electronic) | 9781450392167 |
DOIs | |
Publication status | Published - 16 May 2022 |
Publication series
Name | WiSec 2022 - Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks |
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Keywords
- robotics
- security
- side channel
- traffic analysis
- neural network
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Dive into the research topics of 'Can you still see me? Identifying robot operations over end-to-end encrypted channels'. Together they form a unique fingerprint.Projects
- 1 Finished
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Industrial CASE Account - University of Strathclyde 2017 | Shah, Ryan
Nagaraja, S., Revie, C., Ahmed, C. M., Weir, G. & Shah, R.
EPSRC (Engineering and Physical Sciences Research Council)
1/10/18 → 15/02/23
Project: Research Studentship Case - Internally allocated
Datasets
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Traffic Samples for Traffic Analysis Side Channel Attack for Teleoperated Robots
Shah, R. (Creator) & Revie, C. (Supervisor), University of Strathclyde, 6 Mar 2023
DOI: 10.15129/95ca9dd4-13ac-4ff1-b42d-360930a7f598
Dataset