Collaborative Research: SaTC: CORE: Small: Towards Robust, Scalable, and Resilient Radio Fingerprinting

Project Information

• Award Number: CNS-2225160, CNS-2225161

• Award Amount: $614,681 (UC: $314,684)

• Principal Investigators: Dr. Boyang Wang (UC, lead) and Dr. Nirnimesh Ghose (UNL)

• Project Period: 02/15/2023 – 01/31/2026

• Graduate Students: Haipeng Li, Ryan Evans

• Undergrad Students: Mabon Ninan

Project Overview

This project develops new methods to enhance the robustness, scalability, and resilience of radio fingerprinting in wireless networks. Radio fingerprinting authenticates wireless devices over radio frequency (RF) signals at the physical layer based on hardware variations from manufacturing. This project adopts a cross-layer approach, which synergizes signal processing at the physical layer and deep learning at the data layer.

This research project includes three thrusts: (1) developing a new robust radio fingerprinting method by designing complex-valued triplet neural networks. This method can achieve high accuracy when a classifier is trained with RF signals from one day but is tested with RF signals from a different day; (2) building a new receiver-agnostic radio fingerprinting method by building Physical-Layer-Assisted Generative Adversarial Networks. This method can train classifiers that can be utilized by different receivers; (3) developing a new cross-domain radio fingerprinting method by building neural networks across the time domain, frequency domain, and time-frequency domain. This method will be resilient against adversarial RF signals perturbed by white-box attackers. This project will integrate the research activities into curriculum development, render research opportunities to female and underrepresented students, and advance research experience for students in STEM (Science, Technology, Engineering and Math).

Publications

1. Mabon Ninan, Evan Nimmo, Shane Reilly, Channing Smith, Wenhai Sun, Boyang Wang, John M. Emmert
   “A Second Look at the Portability of Deep Learning Side-Channel Attacks over EM Traces”
   The 27th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2024), Padua, Itay, Sep. 30 - Oct. 2, 2024
   

2. Kaustubh Gupta, Nirnimesh Ghose, Boyang Wang
   “RADTEC: Re-authentication of IoT Devices with Machine Learning”
   IEEE Consumer Communications and Networking Conference (IEEE CCNC), Las Vegas, NV, Jan. 08 - 11, 2023.
   

3. “RadioNet: Robust Deep-Learning Based Radio Fingerprinting”
   IEEE Conference on Communication and Network Security (CNS 2022), Austin, TX, October 3–5, 2022.
   Code & Datasets, Paper PDF

4. Haipeng Li, Chenggang Wang, Nirnimesh Ghose, Boyang Wang,
   “POSTER: Robust Deep-learning-based Radio Fingerprinting with Fine-Tuning,”
   ACM Conference on Security and Privacy in Wireless and Mobile Network (ACM WiSec 2021), Virtual, June, 2021.
   Code & Datasets