1. My Smartphone knows what you print exploring smartphone-based side-channel attacks against 3d Printers
Chen Song, feng lin, zongjie ba, kui ren, chi zhou, wenyao xu
Presented by Brittany walker

2. background
3D printing is now a highly popular manufacturing process in a wide range of different fields, due to its efficiency, accessibility, and the way it allows creativity.
The 3D printing process can be split into a cyber domain and physical domain.
The primitive physical printer operations are: layer, header, axial and directional movements.
Two different side-channel signals emitted: magnetic and acoustic.

3. problem
There are highly sensitive IP(intellectual property) products manufactured constantly.
While security in the cyber domain has been explored, there is little research into the physical domain.
Smartphones are so common now, that they could be used as an inconspicuous device to record side-channel data.
This produces the question:
Is it possible to infer IP information when a smartphone is placed nearby and records side-channel signals during the printing process?
Talk here about what the paper sets out to achieve…

4. Acoustic side channel
Axial movement can easily be deduced because the X and Y movements are performed by different structures, which produce defining sounds.
Directional movement is poorly detected, as actions and their reverse sound very similar.

5. Magnetic side channel
Clearly able to distinguish directional movement, due to patterns in the collected magnetic data.
Temporal and spectral features of the magnetic waveform are used. Temporal is extracted directly from the waveform, spectral by performing a P-point Fast Fourier Transform.

6. Side-channel attack
Data acquisition via smartphone is performed and pre-processing to remove noise interference with Savitzky-Golay filter.
Primitive operation analysis is performed to extract the mechanism parameters of the printer, and construct a printer parameter set in time series.
IP reconstruction is performed to convert the parameter set into G-Code.

7. Results
Smaller frame size increases detail but lowers classification accuracy. For this study, 200ms was chosen.
Attack struggled the most with identifying the direction of X-movements.
Overall, lowest accuracy was with identifying X-Left movements, and highest predictions were for Layer movement.

8. Evaluation
Ultimaker 2 Go is the printer used in the study. A Nexus 5 was the smartphone used to collect data, and was placed at a distance of 20cm from the printer.
Mean Tendency error assesses the reconstruction based on the relative shape difference.

9. Limitations and Defenses
Distance effect, print speed effect, position effect, and ambient noise effect are limitations on the study identified by the authors.
Carry-on attack and advanced shape exploration are discussed as future work.
The authors also offer four different methods to defend against side-channel attacks, two software-based and two hardware-based.

10. Criticisms
Smartphone needs to be within 20cm of the printer, not overly practical.
The chances of a smartphone being left in the same spot for the entire duration of the printing process doesn’t seem that high.
Easily influenced by interfering noise.
Limited to the models of printers that have been trained by the SVM. Not easily transferable to others.
Small inconsistencies may make the reconstructed model useless, as well as lack of detail. So has the possibility to not be useful unless 100% accurate.