Wireless fingerprinting refers to a device identification method leveraging hardware imperfections and wireless channel variations as signatures. Beyond physical layer characteristics, recent studies demonstrated that user behaviours could be identified through network traffic, e.g., packet length, without decryption of the payload. Inspired by these results, we propose a multi-layer fingerprinting framework that jointly considers the multi-layer signatures for improved identification performance. In contrast to previous works, by leveraging the recent multi-view machine learning paradigm, i.e., data with multiple forms, our method can cluster the device information shared among the multi-layer features without supervision. Our information-theoretic approach can be extended to supervised and semi-supervised settings with straightforward derivations. In solving the formulated problem, we obtain a tight surrogate bound using variational inference for efficient optimization. In extracting the shared device information, we develop an algorithm based on the Wyner common information method, enjoying reduced computation complexity as compared to existing approaches. The algorithm can be applied to data distributions belonging to the exponential family class. Empirically, we evaluate the algorithm in a synthetic dataset with real-world video traffic and simulated physical layer characteristics. Our empirical results show that the proposed method outperforms the state-of-the-art baselines in both supervised and unsupervised settings.

翻译：无线指纹识别是一种利用硬件缺陷和无线信道变化作为特征进行设备识别的方法。除了物理层特性外，最新研究表明，无需对负载进行解密即可通过网络流量（例如数据包长度）识别用户行为。受这些结果启发，我们提出了一种多层指纹识别框架，该框架联合考虑多层特征以提升识别性能。与以往工作不同，我们利用最新的多视角机器学习范式（即多种形式的数据），能够无监督地聚类多层特征间共享的设备信息。我们提出的信息论方法可自然地扩展到有监督和半监督场景。在求解所构建的问题时，我们利用变分推断获得了一个紧致的代理下界以实现高效优化。在提取共享设备信息的过程中，我们基于Wyner公共信息方法开发了一种算法，与现有方法相比计算复杂度更低。该算法适用于属于指数族分布的数据。实验部分，我们在包含真实视频流量与模拟物理层特征的合成数据集上评估了该算法。实验结果表明，所提方法在有监督和无监督场景下均优于现有最优基线方法。