Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification

TL;DR

This paper investigates the vulnerability of deep learning-based radio frequency fingerprint identification systems to various adversarial attacks, demonstrating effective methods to compromise these systems in wireless IoT contexts.

Contribution

It provides a comprehensive analysis of adversarial attack methods on RFFI systems using different deep learning models, including practical attack scenarios in wireless environments.

Findings

UAP achieved an 81.7% success rate in attacks.

Attacks were effective against CNN, LSTM, and GRU models.

Real-time and long-term attack effectiveness demonstrated.

Abstract

Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits deep learning models to extract hardware impairments to uniquely identify wireless devices. Recent studies show deep learning-based RFFI is vulnerable to adversarial attacks. However, effective adversarial attacks against different types of RFFI classifiers have not yet been explored. In this paper, we carried out a comprehensive investigations into different adversarial attack methods on RFFI systems using various deep learning models. Three specific algorithms, fast gradient sign method (FGSM), projected gradient descent (PGD), and universal adversarial perturbation (UAP), were analyzed. The attacks were launched to LoRa-RFFI and the experimental results showed the generated perturbations were effective against convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRU). We further used UAP to launch practical attacks. Special factors were considered for the wireless context, including implementing real-time attacks, the effectiveness of the attacks over a period of time, etc. Our experimental evaluation demonstrated that UAP can successfully launch adversarial attacks against the RFFI, achieving a success rate of 81.7% when the adversary almost has no prior knowledge of the victim RFFI systems.