  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.
