Emphasized Spectrum-Based BLE Signal Fingerprinting Dataset for IoT Device Authentication

This dataset contains emphasized spectral feature data extracted from Bluetooth Low Energy (BLE) signals for IoT device authentication research.The data were generated as part of the experimental evaluation presented in the accompanying paper, which proposes a signal fingerprinting framework based on emphasized frequency regions to capture hardware-dependent characteristics of BLE transmission devices.The dataset is organized by filter bank type applied to the emphasized frequency regions. Each top-level directory corresponds to a specific filter bank configuration (e.g., Linear Scale, Mel Scale, Inverse Mel Scale, and Gammatone Scale). Within each directory, subfolders contain data associated with individual BLE transmission modules.Each NumPy (<code>.npy</code>) file represents emphasized spectral features extracted from a single BLE module. For each module, multiple BLE signals were collected and processed through framing, FFT, and emphasized filter bank application. The resulting feature matrices are stored by vertically stacking the features from multiple signals into a single NumPy array.Specifically, each <code>.npy</code> file contains emphasized spectral features derived from 500 BLE signals transmitted by the same module. By partitioning the stored array into equal segments, users can recover the emphasized spectral representation corresponding to each individual signal.This dataset is intended to support reproducibility and comparative evaluation of BLE signal fingerprinting, anomaly detection, and device authentication methods at the physical layer. While the source code used to generate the dataset is not publicly released due to security and intellectual property considerations, the dataset provides sufficient structure and documentation for independent analysis and validation.macdd l

本数据集包含从低功耗蓝牙（Bluetooth Low Energy, BLE）信号中提取的增强频谱特征数据，用于物联网（Internet of Things, IoT）设备认证研究。该数据源自配套论文中呈现的实验评估环节，该论文提出了一种基于增强频率区域的信号指纹框架，用以捕捉BLE传输设备的硬件依赖特性。
本数据集按照应用于增强频率区域的滤波器组类型进行组织。每个顶级目录对应一种特定的滤波器组配置（例如线性刻度、梅尔刻度、逆梅尔刻度以及伽马通刻度）。每个目录下的子文件夹均包含与单个BLE传输模块相关的数据。
每个NumPy (<code>.npy</code>) 格式文件代表从单个BLE模块提取的增强频谱特征。针对每个模块，研究人员采集了多组BLE信号，并通过分帧、快速傅里叶变换（Fast Fourier Transform, FFT）以及增强型滤波器组处理流程进行加工。最终得到的特征矩阵通过将多组信号的特征纵向堆叠为单个NumPy数组进行存储。
具体而言，每个<code>.npy</code>文件包含由同一模块传输的500组BLE信号衍生出的增强频谱特征。用户可通过将存储的数组划分为均等分段，还原出对应每组单独信号的增强频谱表示。
本数据集旨在支持物理层BLE信号指纹识别、异常检测以及设备认证方法的可复现性研究与对比评估。由于安全与知识产权考量，生成该数据集所用的源代码未对外公开，但本数据集提供了充足的结构与文档资料，可供独立开展分析与验证工作。macdd l