Impact Statement:Based on the research of this article, the data-driven PMSs experience great dangers in the era of artificial intelligence (AI). Therefore, the study on the robustness of...Show More
Abstract:
With the rapid development of information technology, intelligent upgrading of the manufacturing industry has broken the closed environment of traditional industrial cont...Show MoreMetadata
Impact Statement:
Based on the research of this article, the data-driven PMSs experience great dangers in the era of artificial intelligence (AI). Therefore, the study on the robustness of data-driven models should be and will continue to act as the driving force for PMS with the ever-increasing interest in the security of AI. It is our hope for this article to serve as a taxonomy for the attack of PMSs from a multitude of works on the latent variable process monitoring methods, as well as provide industrial control and informatics communities with a reminder on potential risks due to wide deployments of intelligent algorithms in ICS.
Abstract:
With the rapid development of information technology, intelligent upgrading of the manufacturing industry has broken the closed environment of traditional industrial control systems (ICS); thus, the information security of ICS has been seriously threatened. As part of ICS, the process monitoring system (PMS) is heavily subject to external risks. Data-driven PMSs have been widely used as initial lines of defense to ensure ICS safety. Once the PMS is under attack, the consequences on the whole ICS will be unimaginable. Unfortunately, the safety issues of the PMS have received inadequate attention. This article reveals PMS’s vulnerabilities through effective attacks. A novel method called subspace transfer network (STN) is proposed to conduct adversarial and poisoning attacks on the PMS simultaneously. Then the attack task flow is defined and explained to make online adversarial attacks and data poisoning on PMS. Meanwhile, aiming at two poisoning goals, targeted and untargeted attacks of STN are designed, respectively. Finally, the PMS’s fragility is verified in two industrial benchmarks.
Published in: IEEE Transactions on Artificial Intelligence ( Volume: 3, Issue: 3, June 2022)