Session: 16-01-01: Government Agency Student Poster Competition

Paper Number: 150569

150569 - Assessing the Cybersecurity of Connected 3d Printers 

Motivation: Additive manufacturing or 3D printing has increasingly gained popularity in various industries, offering significant benefits for prototyping and producing small machine parts. From 2023 to 2030, scanning and rapid prototyping software is anticipated to experience the highest compound annual growth rate (CAGR) of 24% [1]. However, the 3D printing process is vulnerable to numerous cyber-attacks that can compromise the performance and quality of the final product. In this paper, we conduct a comprehensive analysis of the cybersecurity concerns associated with 3D printing, focusing on their potential impact and mitigation strategies.

Method: We first perform an extensive survey of existing literature and current practices in the industry, identifying the most common cybersecurity threats targeting the 3D printing processes. We highlight how malicious actors can exploit these vulnerabilities to alter design files, disrupt production processes, and ultimately degrade the quality of manufactured parts. Based on this preliminary analysis, we identify several key steps of the 3D printing process that need to be secured to ensure the safety of the manufacturing system.
In order to practically assess these threats, we developed a 3D printing testbed consisting of a commercial 3D printer that is wirelessly connected to the command computer. Based on this test bed, we present a proof of concept involving a specific type of cyberattack known as a Man-in-the-Middle (MitM) attack on a 3D printing device. This attack illustrates how an adversary can intercept and alter the data sent to the printer, resulting in compromised prints that are not immediately detectable. Commands are sent to the 3D printer in the form of G-code. The MitM can alter these G-code commands, leading to bad prints and maybe even causing damage to the equipment.
To mitigate this attack, we also developed a continuous 3D monitoring system for the 3D printer. We use depth cameras to obtain point clouds of the object from three directions. We then fuse the 3D data from these cameras to reconstruct the 3D part while it is being printed. We then compare this 3D point cloud with the expected part or the digital twin after a user-defined number of layers are printed. The 3D printer can be stopped if the deviation of the actual print from the digital twin is larger than a user-defined tolerance. This approach ensures that there is no material wastage due to incorrect parts being printed. The same system can also be easily modified to detect other printing errors, such as incorrect adhesion or the formation of blobs in parts.

Results: Our results underscore the need for enhanced security measures in 3D printing environments. We discuss the role of digital twin technologies in monitoring and verifying theintegrity of the printing process, suggesting that these technologies can serve as a robust defense mechanism against cyber threats. By creating a virtual replica of the physical printing process, digital twins can detect anomalies in real-time, ensuring that any deviations from the expected output are promptly addressed.

Conclusions: This survey and proof of concept provide valuable insights for researchers and industry practitioners, emphasizing the importance of integrating advanced cyber-security solutions into 3D printing workflows. Our findings contribute to a deeper understanding of the critical role of cybersecurity in maintaining the reliability and quality
of 3D printed products, laying the foundation for more secure manufacturing practices in the future.

References
[1] Grand View Research. 3D printing industry analysis, 2023. URL https://www.
grandviewresearch.com/industry-analysis/3d-printing-industry-analysis.
Accessed: 2024-07-11.
 

Presenting Author: Shi Yong Goh Iowa State University

Presenting Author Biography: An M.S. student in Computer Engineering at Iowa State University, specializes in cybersecurity and additive manufacturing.

Authors:

Ankush Mishra Iowa State University
Shi Yong Goh Iowa State University
Adarsh Krishnamurthy Iowa State University