Can you still see me? Identifying robot operations over end-to-end encrypted channels

Ryan Shah, Chuadhry Mujeeb Ahmed, Shishir Nagaraja

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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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 languageEnglish
Title of host publicationWiSec 2022 - Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks
Subtitle of host publicationProceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks
Place of PublicationNew York
Pages298-300
Number of pages3
ISBN (Electronic)9781450392167
DOIs
Publication statusPublished - 16 May 2022

Publication series

NameWiSec 2022 - Proceedings of the 15th ACM Conference on Security and Privacy in Wireless and Mobile Networks

Keywords

  • robotics
  • security
  • side channel
  • traffic analysis
  • neural network

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