Visible to the public Watching and Safeguarding Your 3D Printer: Online Process Monitoring Against Cyber-Physical Attacks

TitleWatching and Safeguarding Your 3D Printer: Online Process Monitoring Against Cyber-Physical Attacks
Publication TypeJournal Article
Year of Publication2018
AuthorsGao, Yang, Li, Borui, Wang, Wei, Xu, Wenyao, Zhou, Chi, Jin, Zhanpeng
JournalProc. ACM Interact. Mob. Wearable Ubiquitous Technol.
Keywords3D printing, composability, Cyber-physical security, Metrics, Online Process Monitoring, physical layer security, pubcrawl, resilience, Resiliency, sensor fusion

The increasing adoption of 3D printing in many safety and mission critical applications exposes 3D printers to a variety of cyber attacks that may result in catastrophic consequences if the printing process is compromised. For example, the mechanical properties (e.g., physical strength, thermal resistance, dimensional stability) of 3D printed objects could be significantly affected and degraded if a simple printing setting is maliciously changed. To address this challenge, this study proposes a model-free real-time online process monitoring approach that is capable of detecting and defending against the cyber-physical attacks on the firmwares of 3D printers. Specifically, we explore the potential attacks and consequences of four key printing attributes (including infill path, printing speed, layer thickness, and fan speed) and then formulate the attack models. Based on the intrinsic relation between the printing attributes and the physical observations, our defense model is established by systematically analyzing the multi-faceted, real-time measurement collected from the accelerometer, magnetometer and camera. The Kalman filter and Canny filter are used to map and estimate three aforementioned critical toolpath information that might affect the printing quality. Mel-frequency Cepstrum Coefficients are used to extract features for fan speed estimation. Experimental results show that, for a complex 3D printed design, our method can achieve 4% Hausdorff distance compared with the model dimension for infill path estimate, 6.07% Mean Absolute Percentage Error (MAPE) for speed estimate, 9.57% MAPE for layer thickness estimate, and 96.8% accuracy for fan speed identification. Our study demonstrates that, this new approach can effectively defend against the cyber-physical attacks on 3D printers and 3D printing process.

Citation Keygao_watching_2018