Uncovering the Portability Limitation of Deep Learning-Based Wireless Device Fingerprints

TL;DR

This paper investigates the limitations of deep learning-based wireless device fingerprinting in maintaining performance across different deployment domains and explores potential solutions to improve domain robustness.

Contribution

It identifies the domain portability challenge in deep learning-based device fingerprinting and proposes initial ideas to enhance resilience against domain variability.

Findings

Performance drops significantly across different deployment domains

Experimental validation of domain variability challenges

Initial ideas for improving domain robustness

Abstract

Recent device fingerprinting approaches rely on deep learning to extract device-specific features solely from raw RF signals to identify, classify and authenticate wireless devices. One widely known issue lies in the inability of these approaches to maintain good performances when the training data and testing data are collected under varying deployment domains. For example, when the learning model is trained on data collected from one receiver but tested on data collected from a different receiver, the performance degrades substantially compared to when both training and testing data are collected using the same receiver. The same also happens when considering other varying domains, like channel condition and protocol configuration. In this paper, we begin by explaining, through testbed experiments, the challenges these fingerprinting techniques face when it comes to domain portability. We will then present some ideas on how to go about addressing these challenges so as to make deep learning-based device fingerprinting more resilient to domain variability.