论文标题
对现实世界的RF指纹的嵌入辅助注意力深度学习的蓝牙
Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth
论文作者
论文摘要
可扩展且有效的效率框架设计为指纹现实世界蓝牙设备。我们提出了一个嵌入辅助的注意框架（MBED-ATN），适用于指纹实际蓝牙设备。在不同的环境中分析了其概括能力，并证明了样品长度和抗叠液的效果。嵌入模块用作降低降低单元，该单元将高维3D输入张量映射到1D特征向量，以通过ATN模块进一步处理。此外，与该领域的先前研究不同，我们对模型的复杂性进行了仔细的评估，并通过在不同的时间范围内收集的现实世界蓝牙数据集来测试其指纹识别能力，同时接受了另一个时间范围和实验设置。我们的研究表明，与基准-GRU和Oracle模型相比，样本长度的记忆使用量减少了9.17倍和65.2倍。此外，与Oracle相比，该提议的MBED-ATN显示了16.9倍的拖鞋和7.5倍的训练参数。最后，我们表明，当受到抗敏性拆卸和更大的输入样品长度为1 ms时，提议的MBED-ATN框架会导致TPR提高5.32倍，错误警报少37.9％，而在挑战性的现实世界中，则较小的错误警报较小，准确度更高。
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.
