Can You Still See Me?: Reconstructing Robot Operations Over End-to-End Encrypted Channels
This reveals a critical security vulnerability for industrial automation systems, indicating that standard encryption practices are insufficient against passive attacks.
The paper tackled the problem of sensitive information leakage from connected robots in Industry 4.0, showing that passive adversaries can predict TLS-encrypted robot movements with ~60% accuracy and near-perfect accuracy under realistic conditions, and reconstruct warehousing workflows similarly.
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.