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 \textasciitilde60\% 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.

翻译：连接的机器人在工业4.0中发挥着关键作用,为许多工业工作流程提供自动化和更高的效率。 不幸的是,这些机器人可以向边远对手泄露有关这些操作工作流程的敏感信息。虽然在这种环境下对数据传输有使用端对端加密的授权,但被动对手完全有可能指纹,重建正在展开的整个工作流程 -- -- 建立对设施运作方式的理解。在本文件中,我们调查远程攻击者能否准确指纹机器人移动并最终重建操作工作流程。使用神经网络方法进行交通分析,我们发现可以预测TLS加密的移动,在现实网络条件下,其准确性接近于 \ textasciitilde60 。此外,我们还发现攻击者可以重建仓储工作流程,并取得类似的成功。最终,仅仅采用最佳网络安全做法显然不足以阻止(被动的)弱敌。