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.
- O. Akyol and Z. Duran. 2014. Low-cost laser scanning system design. Journal of Russian Laser Research 35, 3 (May 2014), 244--251.Google ScholarCross Ref
- S. Amin, X. Litrico, S. Sastry, and A.M. Bayen. 2013. Cyber security of Water SCADA systems --- Part I analysis and experimentation of stealthy deception attacks. IEEE Transactions on Control Systems Technology 21, 5 (2013), 1963--1970.Google ScholarCross Ref
- G. C. Anzalone, C. Zhang, B. Wijnen, P. G. Sanders, and J. M. Pearce. 2013. A Low-cost opensource metal 3-D printer. IEEE Access 1 (Dec. 2013), 803--810.Google Scholar
- M. Baker and J. Manweiler. 2014. From 3D printing to spy cats. IEEE Pervasive Computing 13, 4 (2014), 6--9.Google ScholarCross Ref
- Rafael Ballagas, Sarthak Ghosh, and James Landay. 2018. The design space of 3D printable interactivity. Proceedings of ACM Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 2, Article 61 (July 2018), 21 pages. Google ScholarDigital Library
- C. Bayens, T. Le, L. Garcia, R. Beyah, M. Javanmard, and S. Zonouz. 2017. See no evil, hear no evil, feel no evil, print no evil? Malicious fill patterns detection in additive manufacturing. In Proceedings of the 26th USENIX Security Symposium. 1181--1198. Google ScholarDigital Library
- Abdelkareem Bedri, Richard Li, Malcolm Haynes, Raj Prateek Kosaraju, Ishaan Grover, Temiloluwa Prioleau, Min Yan Beh, Mayank Goel, Thad Starner, and Gregory Abowd. 2017. EarBit: Using wearable sensors to detect eating episodes in unconstrained environments. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 37. Google ScholarDigital Library
- S. Belikovetsky, M. Yampolskiy, J. Toh, and Y. Elovici. 2016. dr0wned-cyber-physical attack with additive manufacturing. arXiv preprint: 1609.00133 (2016).Google Scholar
- J. T. Belter and A. M. Dollar. 2015. Strengthening of 3D printed fused deposition manufactured parts using the fill compositing technique. PLOS One (2015), 1--19.Google Scholar
- S. M. Bridges, K. Keiser, N. Sissom, and S. J. Graves. 2015. Cyber security for additive manufacturing. In Proceedings of the 10th Annual Cyber and Information Security Research Conference (CISR). ACM, 1--3. Google ScholarDigital Library
- C. Byung-Chul, L. Seoung-Hyeon, N. Jung-Chan, and L. Jong-Hyouk. 2016. Secure firmware validation and update for consumer devices in home networking. IEEE Transactions on Consumer Electronics 62, 1 (2016), 39--44.Google ScholarCross Ref
- J. Canny. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 6 (1986), 679--698. Google ScholarDigital Library
- S. R. Chhetri, A. Canedo, and M. A. Al Faruque. 2016. KCAD: Kinetic cyber attack detection method for cyber-physical additive manufacturing systems. In Procedings of the 35th International Conference On Computer-Aided Design (ICCAD). ACM, 74. Google ScholarDigital Library
- Jiska Classen, Daniel Wegemer, Paul Patras, Tom Spink, and Matthias Hollick. 2018. Anatomy of a vulnerable fitness tracking system: dissecting the fitbit cloud, App, and firmware. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 5. Google ScholarDigital Library
- A. Cui, M. Costello, and S.J. Stolfo. 2013. When firmware modifications attack: a case study of embedded exploitation. In Proceedings of the 20th Network and Distributed System Security Symposium (NDSS'13). 1--13.Google Scholar
- Q. Do, B. Martini, and K.K.R. Choo. 2016. A data exfiltration and remote exploitation attack on consumer 3D printers. IEEE Transactions on Information Forensics and Security 11, 10 (2016), 2174--2186.Google ScholarDigital Library
- S. K. Everton, M. Hirsch, P. Stravroulakis, R. K. Leach, and A. T. Clare. 2016. Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing. Materials 8 Design 95 (2016), 431--445.Google Scholar
- Petko Georgiev, Sourav Bhattacharya, Nicholas D Lane, and Cecilia Mascolo. 2017. Low-resource multitask audio sensing for mobile and embedded devices via shared deep neural network representations. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 50. Google ScholarDigital Library
- F. Goldenberg. 2006. Geomagnetic navigation beyond the magnetic compass. In Proceedings of Position, Location, And Navigation Symposium (PLANS). IEEE, 684--694.Google ScholarCross Ref
- B. Gozick, K. P. Subbu, R. Dantu, and T. Maeshiro. 2011. Magnetic maps for indoor navigation. IEEE Trans. Instrum. Meas. 60, 12 (2011), 3883--3891.Google ScholarCross Ref
- Andreas Grammenos, Cecilia Mascolo, and Jon Crowcroft. 2018. You are sensing, but are you biased?: A user unaided sensor calibration approach for mobile sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 11. Google ScholarDigital Library
- T. Greene. 2016. U.S. 3D Printer Forecast, 2016--2020: New 3D Print/Additive Manufacturing Technologies Fuel Growth. Technical Report US41333516. IDC Research, Inc., Framingham, MA.Google Scholar
- M. Gross. 2013. Creating the magic with information technology. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp). 1--2. Google ScholarDigital Library
- A. Hojjati, A. Adhikari, K. Struckmann, E. Chou, T.N. Tho Nguyen, K. Madan, M.S. Winslett, C.A. Gunter, and W.P. King. 2016. Leave your phone at the door: Side channels that reveal factory floor secrets. In Proceedings of the 23rd ACM Conference on Computer and Communications Security (CCS). ACM, 883--894. Google ScholarDigital Library
- J. Hong and M. Baker. 2014. 3D Printing, Smart Cities, Robots, and More. IEEE Pervasive Computing 13, 1 (2014), 6--9. Google ScholarDigital Library
- J. U. Hou, D. G. Kim, and H. K. Lee. 2017. Blind 3D mesh watermarking for 3D printed model by analyzing layering artifact. IEEE Transactions on Information Forensics and Security 12, 11 (Nov. 2017), 2712--2725.Google ScholarCross Ref
- D. P. Huttenlocher, G. A. Klanderman, and W.J. Rucklidge. 1993. Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 9 (1993), 850--863. Google ScholarDigital Library
- R. Jones, P. Haufe, E. Sells, P. Iravani, V. Olliver, C. Palmer, and A. Bowyer. 2011. RepRap - the replicating rapid prototyper. Robotica 29, 1 (January 2011), 177--191. Google ScholarDigital Library
- J. P. Kruth, M. C. Leu, and T. Nakagawa. 1998. Progress in additive manufacturing and rapid prototyping. CIRP Annals-Manufacturing Technology 47, 2 (1998), 525--540.Google ScholarCross Ref
- A. Liptak. 2017. The US Navy 3D printed a concept submersible in four weeks. https://www.theverge.com/2017/7/29/16062608/us-navy-3d-printing-submersible-manufacturing-military. (July 29 2017). {Online; accessed 20-July-2018}.Google Scholar
- J. Mireles, C. Terrazas, F. Medina, R. Wicker, and E. Paso. 2013. Automatic feedback control in electron beam melting using infrared thermography. In Proceedings of the Solid Freeform Fabrication Symposium.Google Scholar
- Mark Mirtchouk, Drew Lustig, Alexandra Smith, Ivan Ching, Min Zheng, and Samantha Kleinberg. 2017. Recognizing eating from body-Worn sensors: combining free-living and laboratory data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 85. Google ScholarDigital Library
- S. B. Moore, W. B. Glisson, and M. Yampolskiy. 2017. Implications of malicious 3D printer firmware. In Proceedings of the 50th Hawaii International Conference on System Sciences.Google Scholar
- S. Mueller. 2018. Toward direct manipulation for personal fabrication. IEEE Pervasive Computing 17, 1 (Jan 2018), 75--81. Google ScholarDigital Library
- K. Nomizu and T. Sasaki. 1994. Affine differential geometry: geometry of affine immersions. Cambridge University Press.Google Scholar
- Kazuya Ohara, Takuya Maekawa, and Yasuyuki Matsushita. 2017. Detecting state changes of indoor everyday objects using Wi-Fi channel state information. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 3 (2017), 88. Google ScholarDigital Library
- J. M. Pearce, C. M. Blair, K. J. Laciak, R. Andrews, A. Nosrat, and I. Zelenika-Zovko. 2010. 3D printing of open source appropriate technologies for self-directed sustainable development. J. Sustain. Development 3, 4 (2010), 17--29.Google ScholarCross Ref
- P. K. Rao, J. P. Liu, D. Roberson, Z. J. Kong, and C. Williams. 2015. Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors. Journal of Manufacturing Science and Engineering 137, 6 (2015), 061007.Google ScholarCross Ref
- GE Global Research. 2016. 3D printing creates new parts for aircraft engines. http://www.geglobalresearch.com/innovation/3d-printing-creates-new-parts-aircraft-engines/. (2016). {Online; accessed 1-August-2017}.Google Scholar
- A. Schmidt, T. Döring, and A. Sylvester. 2011. Changing how we make and deliver smart devices: when can I print out my new phone? IEEE Pervasive Computing 10, 4 (2011), 6--9. Google ScholarDigital Library
- D. M. Shila, P. Geng, and T. Lovett. 2016. I can detect you: Using intrusion checkers to resist malicious firmware attacks. In Proceedings of the IEEE Symposium on Technologies for Homeland Security (HST). IEEE, 1--6.Google Scholar
- Chen Song, Zhengxiong Li, Wenyao Xu, Chi Zhou, Zhanpeng Jin, and Kui Ren. 2018. My smartphone recognizes genuine QR codes!: Practical unclonable QR code via 3D printing. Proceedings of ACM Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 2, Article 83 (July 2018), 20 pages. Google ScholarDigital Library
- Chen Song, Feng Lin, Zhongjie Ba, Kui Ren, Chi Zhou, and Wenyao Xu. 2016. My smartphone knows what you print: Exploring smartphone-based side-channel attacks against 3D printers. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 895--907. Google ScholarDigital Library
- SpaceX. 2014. SpaceX lauches 3D-printed part to space, creates printed engine chamber. http://www.spacex.com/news/2014/07/31/spacex-launches-3d-printed-part-space-creates-printed-engine-chamber-crewed/. (2014). {Online; accessed 20-July-2018}.Google Scholar
- J. Straub. 2017. 3D printing cybersecurity: detecting and preventing attacks that seek to weaken a printed object by changing fill level. In Proceedings of SPIE, Dimensional Optical Metrology and Inspection for Practical Appl. VI, Vol. 10220. 1--15.Google Scholar
- J. Straub. 2017. An approach to detecting deliberately introduced defects and micro-defects in 3D printed objects. In Proceedings of SPIE, Pattern Recognition and Tracking XXVIII, Vol. 10203. 1--14.Google Scholar
- J. Straub. 2017. A combined system for 3D printing cybersecurity. In Proceedings of SPIE, Dimensional Optical Metrology and Inspection for Practical Appl. VI, Vol. 10220. 1--13.Google Scholar
- J. Straub. 2017. Identifying positioning-based attacks against 3D printed objects and the 3D printing process. In Proceedings of SPIE, Pattern Recognition and Tracking XXVIII, Vol. 10203. 1--13.Google Scholar
- J. Straub. 2017. Physical security and cyber security issues and human error prevention for 3D printed objects: detecting the use of an incorrect printing material. In Proceedings of SPIE, Dimensional Optical Metrology and Inspection for Practical Appl. VI, Vol. 10220. 1--16.Google Scholar
- L. D. Sturm, C. B. Williams, J. A. Camelio, J. White, and R. Parker. 2014. Cyber-physical vulnerabilities in additive manufacturing systems. Context 7, 2014 (2014), 951--963.Google Scholar
- L. D. Sturm, C. B. Williams, J. A. Camelio, J. White, and R. Parker. 2017. Cyber-physical vulnerabilities in additive manufacturing systems: A case study attack on the .STL file with human subjects. Journal of Manufacturing Systems 44, 1 (2017), 154--164.Google ScholarCross Ref
- H. Turner, J. White, J. A. Camelio, C. Williams, B. Amos, and R. Parker. 2015. Bad parts: Are our manufacturing systems at risk of silent cyberattacks? IEEE Security 8 Privacy 13, 3 (2015), 40--47.Google Scholar
- L. G. Valiant. 1979. The complexity of computing the permanent. Theoretical Computer Science 8, 2 (1979), 189--201.Google ScholarCross Ref
- H. Vincent, L. Wells, P. Tarazaga, and J. Camelio. 2015. Trojan detection and side-channel analyses for cybersecurity in cyber-physical manufacturing systems. Procedia Manufacturing 1 (2015), 77--85.Google ScholarCross Ref
- Chuyu Wang, Jian Liu, Yingying Chen, Lei Xie, Hong Bo Liu, and Sanclu Lu. 2018. RF-Kinect: A wearable RFID-based approach towards 3D body movement tracking. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 1 (2018), 41. Google ScholarDigital Library
- L. J. Wells, J. A. Camelio, C. B. Williams, and J. White. 2014. Cyber-physical security challenges in manufacturing systems. Manufacturing Letters 2, 2 (2014), 74--77.Google ScholarCross Ref
- R. Whited. 2017. Failure Analysis of 3D Printed Parts. Technical Report KSC-E-DAA-TN41114. NASA Kennedy Space Center, Cocoa Beach, FL.Google Scholar
- T. T. Wohlers and T. Caffrey. 2016. Wohlers Report 2016: 3D printing and additive manufacturing state of the industry annual worldwide progress report. Wohlers Associates.Google Scholar
- M. Wu, Z. Song, and Y. B. Moon. 2017. Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods. Journal of Intelligent Manufacturing (2017), 1--13.Google Scholar
- M. Yampolskiy, T. R. Andel, J. T. McDonald, W. B. Glisson, and A. Yasinsac. 2014. Intellectual property protection in additive layer manufacturing: Requirements for secure outsourcing. In Proceedings of the 4th Program Protection and Reverse Engineering Workshop. ACM, 7. Google ScholarDigital Library
- Mark Yampolskiy, Peter Horvath, Xenofon D Koutsoukos, Yuan Xue, and Janos Sztipanovits. 2012. Systematic analysis of cyberattacks on CPS-evaluating applicability of DFD-based approach. In Proceedings of the 5th International Symposium on Resilient Control Systems (ISRCS). IEEE, 55--62.Google Scholar
- M. Yampolskiy, A Skjellum, M Kretzschmar, R. A. Overfeit, K. R. Sloan, and A. Yasinsac. 2016. Using 3D printers as weapons. International Journal of Critical Infrastructure Protection 14 (2016), 58--71. Google ScholarDigital Library
- L. Yang, K. Hsu, B. Baughman, D. Godfrey, F. Medina, M. Menon, and S. Wiener. 2017. Additive manufacturing of metals: the technology, materials, design and production. (2017).Google Scholar
- S. E. Zeltmann, N. Gupta, N. G. Tsoutsos, M. Maniatakos, J. Rajendran, and R. Karri. 2016. Manufacturing and security challenges in 3D printing. JOM 68, 7 (July 2016), 1872--1881.Google ScholarCross Ref
Index Terms
- Watching and Safeguarding Your 3D Printer: Online Process Monitoring Against Cyber-Physical Attacks
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