research-article

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

Authors:

Zhanpeng JinAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 3

Article No.: 108, Pages 1 - 27

https://doi.org/10.1145/3264918

Published: 18 September 2018 Publication History

Abstract

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.

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Cited By

Kayan HHeartfield RRana OBurnap PPerera C(2024)CASPER: Context-Aware IoT Anomaly Detection System for Industrial Robotic ArmsACM Transactions on Internet of Things10.1145/36704145:3(1-36)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3670414
Pan QLi SLi NZhang PTan L(2024)Security analysis and monitoring assessment of networked printers: A reportElectronics Letters10.1049/ell2.1309760:3Online publication date: 15-Feb-2024
https://doi.org/10.1049/ell2.13097
Yu ZChang YZhai SDeily NJu TWang XJammalamadaka UZhang NCalandrino JTroncoso C(2023)XCheckProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620395(2815-2832)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620395
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Index Terms

Watching and Safeguarding Your 3D Printer: Online Process Monitoring Against Cyber-Physical Attacks
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 3

September 2018

1536 pages

EISSN:2474-9567

DOI:10.1145/3279953

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Publication History

Published: 18 September 2018

Accepted: 01 September 2018

Revised: 01 July 2018

Received: 01 February 2018

Published in IMWUT Volume 2, Issue 3

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Cited By

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https://dl.acm.org/doi/10.1145/3670414
Pan QLi SLi NZhang PTan L(2024)Security analysis and monitoring assessment of networked printers: A reportElectronics Letters10.1049/ell2.1309760:3Online publication date: 15-Feb-2024
https://doi.org/10.1049/ell2.13097
Yu ZChang YZhai SDeily NJu TWang XJammalamadaka UZhang NCalandrino JTroncoso C(2023)XCheckProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620395(2815-2832)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620395
Wylie BMoore C(2023)Optical Methods of Error Detection in Additive Manufacturing: A Literature ReviewJournal of Manufacturing and Materials Processing10.3390/jmmp70300807:3(80)Online publication date: 23-Apr-2023
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Si HZhang ZHuseynov OFidan IHasan SMahmoud M(2023)Machine Learning-Based Investigation of the 3D Printer Cooling Effect on Print Quality in Fused Filament Fabrication: A Cybersecurity PerspectiveInventions10.3390/inventions80100248:1(24)Online publication date: 16-Jan-2023
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Ahsan MRais MAhmed I(2023)SOK: Side Channel Monitoring for Additive Manufacturing - Bridging Cybersecurity and Quality Assurance Communities2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP57164.2023.00071(1160-1178)Online publication date: Jul-2023
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