A scalable and computationally efficient framework is designed to fingerprint real-world Bluetooth devices. We propose an embedding-assisted attentional framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its generalization capability is analyzed in different settings and the effect of sample length and anti-aliasing decimation is demonstrated. The embedding module serves as a dimensionality reduction unit that maps the high dimensional 3D input tensor to a 1D feature vector for further processing by the ATN module. Furthermore, unlike the prior research in this field, we closely evaluate the complexity of the model and test its fingerprinting capability with real-world Bluetooth dataset collected under a different time frame and experimental setting while being trained on another. Our study reveals a 9.17x and 65.2x lesser memory usage at a sample length of 100 kS when compared to the benchmark - GRU and Oracle models respectively. Further, the proposed Mbed-ATN showcases 16.9x fewer FLOPs and 7.5x lesser trainable parameters when compared to Oracle. Finally, we show that when subject to anti-aliasing decimation and at greater input sample lengths of 1 MS, the proposed Mbed-ATN framework results in a 5.32x higher TPR, 37.9% fewer false alarms, and 6.74x higher accuracy under the challenging real-world setting.

翻译：我们设计了一种可扩展且计算高效的框架，用于对真实蓝牙设备进行指纹识别。提出了一种适用于实际蓝牙设备指纹识别的嵌入辅助注意力框架（Mbed-ATN），分析了其在不同设置下的泛化能力，并展示了样本长度与抗混叠降采样的效果。嵌入模块作为降维单元，将高维三维输入张量映射为一维特征向量，供注意力模块进一步处理。此外，与现有研究不同，我们严格评估了模型复杂度，并利用在另一时间段和实验设置下采集的真实蓝牙数据集测试其指纹识别能力（模型在另一数据集上训练）。研究表明，在采样长度为100千样本时，模型内存使用量较基准模型（门控循环单元和神谕模型）分别降低9.17倍和65.2倍。此外，与神谕模型相比，所提Mbed-ATN的浮点运算次数减少16.9倍，可训练参数减少7.5倍。最后，在抗混叠降采样条件下，当输入样本长度增至1兆样本时，所提Mbed-ATN框架在挑战性真实场景中实现了5.32倍的真阳性率提升、37.9%的误报率降低，以及6.74倍的准确率提升。