Radio frequency (RF) fingerprinting is able present unique characteristics (e.g., carrier frequency of the oscillators) of a wireless device, which can be used to authenticate this device. This device authentication based on RF fingerprinting has been emerged as an alternative of the traditional authentical based on public key cryptography, especially for the low-cost and low-complexity wireless devices in the context of Internet of Things. This authentication first requires a valid dataset of the RF fingerprinting. Therefore, the first task of this project is to conduct a thorough literature review on the existing RF fingerprinting dataset and the authentication schemes (e.g., based on machine learning), identifying the used unique characteristics or other features, authentication performance, and the limiting factors. The second task of this project is to collect a RF fingerprinting dataset via using the available software and hardware platforms at ECU (e.g., Ettus X310, N210, Spectrum analyzer). Then, the final task is to apply existing authentication schemes by using the existing and self-collected dataset, while new authentication schemes should be developed as well. The performance of various authentication schemes will be also examined and analyzed by using the existing and self-collected dataset in this task. This project should lead to multiple high-quality journal publications.
Literature samples:
Available platforms at ECU:
Available software platforms:
Experience with software define radios, MATLAB, basic wireless communication knowledge.
Project Area: Wireless Cybersecurity
Research Centre: Security Research Institute (SRI)
Supervisor(s): Dr Shihao YAN
Project level: Masters / PhD
Funding: Applicant should apply for ECUHDR or RTP Scholarship
Start date: Any