See No Evil, Hear No Evil, Feel No Evil, Print No Evil? Malicious Fill Patterns Detection in Additive Manufacturing


Christian Bayens, Georgia Institute of Technology; Tuan Le and Luis Garcia, Rutgers University; Raheem Beyah, Georgia Institute of Technology; Mehdi Javanmard and Saman Zonouz, Rutgers University


Additive Manufacturing is an increasingly integral part of industrial manufacturing. Safety-critical products, such as medical prostheses and parts for aerospace and automotive industries are being printed by additive manufacturing methods with no standard means of verification. In this paper, we develop a scheme of verification and intrusion detection that is independent of the printer firmware and controller PC. The scheme incorporates analyses of the acoustic signature of a manufacturing process, real-time tracking of machine components, and post production materials analysis. Not only will these methods allow the end user to verify the accuracy of printed models, but they will also save material costs by verifying the prints in real time and stopping the process in the event of a discrepancy. We evaluate our methods using three different types of 3D printers and one CNC machine and find them to be 100% accurate when detecting erroneous prints in real time. We also present a use case in which an erroneous print of a tibial knee prosthesis is identified.

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@inproceedings {203636,
author = {Christian Bayens and Tuan Le and Luis Garcia and Raheem Beyah and Mehdi Javanmard and Saman Zonouz},
title = {See No Evil, Hear No Evil, Feel No Evil, Print No Evil? Malicious Fill Patterns Detection in Additive Manufacturing},
booktitle = {26th USENIX Security Symposium (USENIX Security 17)},
year = {2017},
isbn = {978-1-931971-40-9},
address = {Vancouver, BC},
pages = {1181--1198},
url = {},
publisher = {USENIX Association},
month = aug

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