SideChannel-3D: Acoustic, Vibration, Magnetic, and Power Side-Channel 3D Printer Dataset

This dataset contains multimodal sensor data collected from side-channels while printing several types of objects on an Ultimaker 3 3D printer. Our related research paper titled "Sabotage Attack Detection for Additive Manufacturing Systems" can be found here: https://doi.org/10.1109/ACCESS.2020.2971947. In our work, we demonstrate that this sensor data can be used with machine learning algorithms to detect sabotage attacks on the 3D printer. By utilizing multiple side-channels, we improve system state estimation significantly in comparison to uni-modal techniques. Besides, in the paper we analyze the value of each side-channel for performing attack detection in terms of mutual information shared with the machine control parameters. We evaluate our system on real-world test cases and achieve an attack detection accuracy of 98.15%. Our dataset contains sets of G-codes synchronized with the corresponding sensor readings and sensor features, enabling highly accurate state estimation. This state estimation capability can be useful for tasks such as security, predictive maintenance, quality control, automated calibration, etc.Our testbed contains the following types and quantities of sensors: - 3x 3-axis magnetometer. - 3x 3-axis accelerometer. - 4x high-definition microphone. - 1x DC current clamp. - internal sensor data from the 3D printer.These sensors are placed in various locations around the 3D printer.Please kindly consider citing our paper if you find this dataset useful for your research:@article{yu2020sabotage, title={Sabotage attack detection for additive manufacturing systems}, author={Yu, Shih-Yuan and Malawade, Arnav Vaibhav and Chhetri, Sujit Rokka and Al Faruque, Mohammad Abdullah}, journal={IEEE Access}, volume={8}, pages={27218--27231}, year={2020}, publisher={IEEE}}For additional information or to contact us, please refer to our lab's website: https://aicps.eng.uci.edu/