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: Jack Barker

Overview Investigates using side channel methods to determine what is being 3D printed. Uses a Nexus 5 smartphone and it’s sensors Is it possible to infer IP information when a smartphone is placed nearby and record side-channel signals during the 3D printing process? Paper looks into 3d printing and how mobile phones could be used to steal intellectual property Used a nexus 5 phone and its sensors to investigate this, -magnetic field generated by motors -sounds generated by motors The goal of the paper: Is it possible to record side channel signals from a printer and recreate what is it printing? Can be an issue if IP sensitive objecst are printing

3D Printing A form of additive manufacturing Objects are CADed CAM (computer aided manufacturing) software is used to slice the object into multiple layers Builds an object up layer by layer by extruding hot plastic Three axis of movement, X Y and Z A common form of additive manufacturing Objects are designed in CAD software, such as Autodesk Fusion or Creo Once the design is complete, CAM software is used to slice the object into multiple 2d layers with a thickness and encode it into G-CODE The gcode is used commonly in manufacturing and is basically a set of instructions for a machine The plastic used comes in a reel, and is heated to around 200 degrees celcius and placed layer by layer The printer has a print bed, which is usually heated which can move up and down in the Z axis. The nozel which the plastic is melted and extruded from moves in the XY axis. The printer has 3 axis, and prints each layer up one by one until the entire object is created

3D Printing Used in industry widely for prototyping currently Fast and cost efficient with less waste than alternatives Printers are accessible and affordable, making them widely used By 2021, it is estimated that the market will be worth $20 billion Increase in use means increase in IP sensitive products being printed Need to consider security of printed objects Used in industry mainly for prototyping currently It is very fast compared to traditional manufacturing techniques, such as injection moulding or milling, minimal setup time Printers are affordable, with some models being lesse than $500, while industrial printers can range in the tens of thousands The market is expanding rapidly, and by 2021 it is predicted the makket will be worth over 20B. This means that we really need to consider the security of intellectual property when printing, as sensitive objects could be printed. Imagine for example if apple printed prototype frames of the new Iphone and the information were to be leaked.

Side Channel Attacks An attack based on information based in information gained from physical implementation Acoustic and Magnetic signals are considered To gather IP from a 3D printer: X,Y,Z movement and extrusion must be gathered A side channel attack is any attack that Is based on information gained from the physical world. In this case, a printer gives off magnetic field changes as well as sounds as it print. The magnetic field information is from the magnets and electricity flowing through the stepper motors, and the sounds are from the physical structure moving. To gather IP from a printer, we need to know information about how the printer is moving in all 3 axis as well as how fast the plastic is being extruded. A smart phone has a compass which can be used to detect magnetic shifts, and a microphone which we can use to gather acoustic data.

Information Gathering Acoustic side channel: Each axis of movement has its own distinguishable sound Each motor is in different structures Used to determine speed Magnetic side channel: Patterns in magnetic fields are observed Magnetic data in each coordinate changes when motor activated Used to determine direction Because the printer has 3 motors, each axis will have its own distinct sound when printing due to the structure of the printer. Each motor is in a different part of the printer, so we can determine which motor is running and how fast it is running. We can observe patterns in the magnetic field and can determine which motors are operational through this information as well, determining the direction the nozel is moving in. Drift in pattern when nozlle moves from one side to another is neglidgible

Support Vector Machine Information gathered from acoustic and magnetic analysis is used in an Support Vector Machine (SVM) Supervised machine learning model Needs to be trained for a specific printer Experiment trained with 1000 audio frames and 2000 magnetic frames This is important in converting the data gathered back to G-Code. The acoustic and magnetic data gathered is then fed into a SVM machine. A SVM machine is a type of machine learning model that takes the data and uses it for classification and regression analysis. The SVM machine was trained for a specific printer with over 1000 audio frames and 2000 magnetic data fromes. The SVM determines the shape of the printed object layer by layer by determining the direaction of printer movement This is important for converting the data back into GCODe.

Processing of Data Signal noise needs to be removed as well as white noise. A Savitzky-Golay (data smoothing) filter is used on collected data. Layer Movement Analysis Determine which plane the movement is on (X/Y plane or vertical) Acoustic analysis used Z axis is not belt driven, distinct acoustic sound There is a lot of noise in the information gathered which needs to be filtered out A Savitzky–Golay filter is a digital filter that can be applied to a set of digital data points for the purpose of smoothing the data There was a few methods used to analyse the data gathered. Firs tbeing layer movement analysis Acoustic data is used to determine if the printer is moving on the XY plane or the vertical plane In the specific printer used, the Z axis was driven by a threaded rod, where the X and Y axis were driven by belts. This makes determining the plane of movement easy through acoustic analysis.

Processing of Data Header Movement Analysis Extruder feeds filament through at a constant speed. Extrusion speed determined by the layer height and material. When not extruding, printer moves faster Acoustic used to determine if there is extrusion in a frame Acoustic data gathered can be used to determine is there is extrusion in a given frame. This is important because not all of the time the printer is extruding, and we need to know where it happens. When printing something, the extrusion speed is set by the layer height and material. If we know the extrusion speed, we can use this information to help toward deciding what layer height the printer is printing.

Processing of Data Axial Movement Analysis When on XY plane, need to determine which axis Acoustic channel is again used to determine which axis movement is on. Directional Movement Analysis Need to determine direction of movement Magnetic channel is used to determine this Once layer movement analysis has determined that the movement is on the XY plane, we then need to determine which axis are moving. We can again use the acoustic channel to determine which motors are running, and thus which axis are moving. When two axis are moving at once, we could be moving on a diagonal. This is diffifult to determine using acoustic analysis as both motors are making sounds at the same time. The magnetic channel can be used to determine the direction of movement. Once we have gathered information from all of these sources, we can use them together in the SVM to determine the shape that has been printed. Integration is used to rebuild GCODe

Accuracy Authors devised a model, Mean Tendency Error (MTE) to determine how accurate a reconstructed object is MTE assesses a geometric reconstruction of shape difference using points Calculates the directional consistency between design and reconstructed design A metric that takes points in the design and the reconstructed design and takes into account the error of points Because this is research that hasn’t been done before, the researchers devised a model to determine how alike two shapes are in printing. It asseses the geometric reconstruction of shape difference using points, basically it takes a set of points in the first one and compares how off they are in the second.

IP Reconstruction We must convert the gathered data to G-Code Using the information from the SVM, the authors could recreate the following To be able to recreate a stolen object, the attackers have to be able to recreate the gcode used to produce the object. The SVM gives enough data to do this, in the form of header velocities and directions. This can be integrated to give the distance travelled in directions. This here is an example of a simple 4 layer square being printed. The red line represents the original shape, and the bnlack line the reconstructed shape. As you can see there is a resembelence, but it is not perfect. This error could potentially build up many layers and produce an object quite different. Red – original Black - reconstructed

IP Reconstruction A way to counter this is to filter multiple layers at once. This would only work if an object was printed multiple times, or if multiple layers take the same movement. This example here shows how a svaixky golay filter can be used to take the data gathered and smooth them to get something which resembles the original shape. Again the original is shown in red, and the reconstructed in black. Because the velicities are integrated, to find distance, small error can accumulate and here we can see difference in the prints. The right image shows the actual prints, with the left being the original shape and the right being the replicated shape. You can see the subtle differences, but overall the shape resembles the original one. Similar to original once it is smoothed A Savitzky-Golay

Issues Distance Print Speed Phone Position Ambient Noise The paper overviews some issues with the technique used. The first one being the distance of the phone to the printer. The accuracy deteriotes rapidly with changes in distance, as you can see a change of just 20cm can increase the MTE from 5.87 percent to 34 percent. This could pose a challenge for attackers actually getting close to the printer and placing their phones, especially in secured fascilities. The paper suggests that you could use multiple phones and smooth the data to improve this accuracy at distances, which is an area of reseach which could be looked into in the future and I believe could be plausible. The print speed is also a major factor in gathering accurate data. If the printer is moving slower, each frame will be longer as it will move in a single direction for a longer amount of time. If the print speed is too fast, there may not be enough data in each frame to determine the direction and speed of the print head. The phone position is another issue with this approach. In this research, the SVM machine learning model was trained with the phone in a specific position in relation to the printer, and the print being recreated was recorded in the same position. When stealing someones intellectual property, it would be very difficult to train the SVM given a specific location that is undefined, since you wont be able to run training movements on it. Ambient noise is another issue which the paper overviews, where audio and magnetic interference could be picked up. Some household applicances, such as fridges and microwaves can give off significant magnetic field changes, as well as noises that you can find in general life could interfere with the data acquisition. Distance – deteriorates rapidly USE MULTIPLE PHONES Faster prints means gathering data is more difficult, less time in movements Position: Trained in one position, need to account for phone being in different places Ambient acoustic and magnetic noise can interfere

Defences Dynamic Path Planning Dummy Task Injection Hardware Shielding change speed during print Dummy Task Injection Dummy movements outside object Hardware Shielding Shield sound and magnetic information from intruders Side Channel Interference Home appliances (eg, microwave) can produce magnetic field You could change the speed of the print while it is printing, the approach used assumes a static print speed that it can integrate to find distance travelled. You could inject dummy tasks for the printer, which could be movements outside or inside the model to interfere with the recording. Hardware shielding, the printer could be shieled from any intruders by being in an enclosure that could reduce acoustic sounds and magnetic fields escaping it. You could also intentionally spoil the side channels, such as generating magnetic fields or sounds which would interfere with the recording. Because acoustic is so important, simply playing music could be a defense.

Questions? Similar to original once it is smoothed