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