# The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

arXiv: https://arxiv.org/abs/2303.15860 · HTML: https://mlsanity.com/paper/2303.15860/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 62 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [On Variational Bounds of Mutual Information](https://mlsanity.com/paper/1905.06922/) (2019)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Advances in Variational Inference](https://mlsanity.com/paper/1711.05597/) (2017)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [A Minimax Approach to Supervised Learning](https://mlsanity.com/paper/1606.02206/) (2016)
- [Variational Information Bottleneck for Unsupervised Clustering: Deep Gaussian Mixture Embedding](https://mlsanity.com/paper/1905.11741/) (2019)
- [Robust Wireless Fingerprinting via Complex-Valued Neural Networks](https://mlsanity.com/paper/1905.09388/) (2019)
- [Common Information Components Analysis](https://mlsanity.com/paper/2002.00779/) (2020)
- [Penetrating RF Fingerprinting-based Authentication with a Generative Adversarial Attack](https://mlsanity.com/paper/2011.01538/) (2020)
- [Cross-Layer Authentication Protocol Design for Ultra-Dense 5G HetNets](https://mlsanity.com/paper/1802.01719/) (2018)

## Built on by (most cited first, 1 of 1)

- [An Efficient Difference-of-Convex Solver for Privacy Funnel](https://mlsanity.com/paper/2403.04778/) (2024)

## Abstract (arXiv)

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.