Deep Learning Methods for Device Identification Using Symbols Trace Plot

TL;DR

This paper introduces a novel RF fingerprinting method called density trace plot (DTP) for device authentication, leveraging IQ imbalance features and deep learning classifiers to improve security in communication systems.

Contribution

It proposes a new DTP-based RF fingerprinting approach using IQ imbalance features and evaluates multiple deep learning classifiers for device identification.

Findings

DTP effectively extracts device-specific fingerprints from symbol trajectories.

Deep learning classifiers achieve high accuracy with DTP features.

The method is validated on real SDR data.

Abstract

Devices authentication is one crucial aspect of any communication system. Recently, the physical layer approach radio frequency (RF) fingerprinting has gained increased interest as it provides an extra layer of security without requiring additional components. In this work, we propose an RF fingerprinting based transmitter authentication approach density trace plot (DTP) to exploit device-identifiable fingerprints. By considering IQ imbalance solely as the feature source, DTP can efficiently extract device-identifiable fingerprints from symbol transition trajectories and density center drifts. In total, three DTP modalities based on constellation, eye and phase traces are respectively generated and tested against three deep learning classifiers: the 2D-CNN, 2D-CNN+biLSTM and 3D-CNN. The feasibility of these DTP and classifier pairs is verified using a practical dataset collected from the ADALM-PLUTO software-defined radios (SDRs).