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 behaviors 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：无线指纹识别是指利用硬件缺陷和无线信道变化作为签名进行设备识别的方法。除物理层特征外，近期研究表明，用户行为可通过网络流量（例如数据包长度）进行识别，而无需解密有效载荷。受此启发，本文提出一种多层指纹识别框架，联合考虑多层签名以提升识别性能。与先前工作不同，通过利用最新多视图机器学习范式（即具有多种形式的数据），该方法能够无监督地聚类多层特征间共享的设备信息。本文的信息论方法可通过直接推导扩展至有监督与半监督场景。在求解所构建问题时，我们利用变分推断获得紧致的代理下界以实现高效优化。在提取共享设备信息过程中，基于Wyner公共信息方法开发了一种算法，相较于现有方法显著降低了计算复杂度。该算法适用于指数族类数据分布。通过包含真实视频流量与模拟物理层特征的合成数据集进行实证评估，结果表明所提方法在有监督与无监督场景下均优于现有基线模型。