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 placed in various locations around the 3D printer:3x 3-axis magnetometer.3x 3-axis accelerometer.4x high-definition microphone.1x DC current clamp.internal sensor data from 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/ NOTE: 3D printer sensor data is currently being uploaded. Please check back later to download the full dataset.

本数据集收录了从Ultimaker 3 3D打印机打印多种物体时，侧通道所采集的多模态传感器数据。相关研究论文《针对增材制造系统的破坏攻击检测》可在此处查阅：https://doi.org/10.1109/ACCESS.2020.2971947。在研究中，我们展示了如何利用机器学习算法结合该传感器数据来检测3D打印机的破坏攻击。通过运用多个侧通道，我们显著提升了系统状态估计的精度，相较于单模态技术有显著改进。此外，在论文中，我们基于与机器控制参数共享的互信息，分析了每个侧通道在执行攻击检测中的价值。我们以实际测试案例评估了我们的系统，并实现了98.15%的攻击检测准确率。本数据集包含与相应传感器读数和传感器特征同步的G代码集，这为高度精确的状态估计提供了可能。此状态估计能力在安全、预测性维护、质量控制、自动化校准等任务中具有潜在应用价值。我们的测试平台包括以下类型和数量的传感器，它们被放置在3D打印机周围的多个位置：3个三轴磁力计、3个三轴加速度计、4个高清麦克风、1个直流电流钳以及来自3D打印机的内部传感器数据。如若本数据集对您的研究有所帮助，敬请引用我们的论文：@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}}。如需更多信息或联系作者，请访问我们的实验室网站：https://aicps.eng.uci.edu/。注意：3D打印机传感器数据正在上传中，请稍后查看并下载完整数据集。