Identifying the Cyber-Incidents in Additive Manufacturing Systems via Multimedia Signals

Abstract

Abstract Additive Manufacturing (AM) is an emerging manufacturing technology that plays a growing role in both industrial and consumer settings. However, security concerns of the AM have been raised among researchers. In this paper, we present an online detection mechanism for the malicious attempts on AM system, which taps into both audios and videos collected during the actual printing process. For audio signals, we propose to monitor the characteristics or patterns in the spectrogram via the Wasserstain metric. For video signals, we present a path reconstruction method which effectively monitors the motion of the printer extruder. We then show the effectiveness of our methods in a case study using Ender 3D printer, where the cyber-incidence of modifying the internal fill density can be easily identified in an online manner.