Dataset for the paper: Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel
收藏资源简介：
This repository contains data related to "Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel," by Riccardo Spolaor, Hao Liu, Federico Turrin, Mauro Conti, Xiuzhen Cheng, to appear in Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), 17-20 May 2023. This dataset includes the labels and features extracted from the energy consumption of 82 USB peripherals under different states (i.e., Boot, On) and actions (e.g., Read, Write, Upload, Download). The dataset contains more than 175.000 segments extracted from around 20.000 power traces. We have collected the raw power traces with a National Instruments USB-6210 DAQ at a sampling rate of 10kHz. Each segment is one second long. Please, find more details about the data collection in the paper. We identify a USB peripheral by its type (Device_Type), model (Device_Model), and physical device with such type and model (Device_Id). For each power trace's segment, we assign a unique identifier (Segment_Id), and we indicate the action performed (Action) and the activity/inactivity proportions (Activity_Ratio and Inactive_Ratio). The remaining columns (with the prefix "EC__") are the features extracted from segments using the tsfresh libraries for python V0.19.0 (https://tsfresh.readthedocs.io) Please, support our work by citing our paper: Riccardo Spolaor, Hao Liu, Federico Turrin, Mauro Conti, Xiuzhen Cheng, "Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel," In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), 2023. Contact info: Riccardo Spolaor (rspolaor@sdu.edu.cn, Shandong University, Qingdao, China) and Federico Turrin (turrin@math.unipd.it, University of Padua, Padua, Italy).
本仓库收录与论文"即插即用供电：基于功率侧信道的USB供电外设指纹识别"相关的数据集，该论文作者为Riccardo Spolaor、Hao Liu、Federico Turrin、Mauro Conti、Xiuzhen Cheng，将发表于2023年5月17日至20日举办的IEEE国际计算机通信会议（INFOCOM）论文集。本数据集包含82款USB供电外设（USB Powered Peripherals）在不同工作状态（即启动态、工作态）及执行各类操作（如读取、写入、上传、下载）时的能耗数据所提取的标签与特征。本数据集包含从约20000条功率轨迹中提取的超过175000个数据段。我们采用美国国家仪器（National Instruments）USB-6210型数据采集设备（DAQ）以10kHz的采样率采集原始功率轨迹。每个数据段时长为1秒。有关数据采集的详细细节，请参阅该论文。我们通过外设类型（Device_Type）、型号（Device_Model）以及具备该类型与型号的物理设备（Device_Id）来标识一款USB外设。针对每条功率轨迹数据段，我们分配唯一标识符（Segment_Id），并标注其所执行的操作（Action）以及活动占比（Activity_Ratio）与非活动占比（Inactive_Ratio）。其余以"EC__"为前缀的列均为使用Python V0.19.0版本的tsfresh库从数据段中提取的特征，库文档地址为https://tsfresh.readthedocs.io。恳请引用我们的论文以支持本研究工作：Riccardo Spolaor、Hao Liu、Federico Turrin、Mauro Conti、Xiuzhen Cheng，"即插即用供电：基于功率侧信道的USB供电外设指纹识别"，发表于IEEE国际计算机通信会议（INFOCOM）论文集，2023年。联系方式：Riccardo Spolaor（rspolaor@sdu.edu.cn，中国山东大学青岛校区）与Federico Turrin（turrin@math.unipd.it，意大利帕多瓦大学）。
