ECCS-EPSRC: SecureID: Towards Secure Device Identification Using Radio Frequency Fingerprints

This award is funded through the NSF Directorate for Engineering - UKRI Engineering and Physical Sciences Research Council Lead Agency (ENG-EPSRC) Opportunity, a collaborative solicitation between NSF and the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Research and Innovation (UKRI). This project will design innovative device fingerprinting solutions for pervasive Internet of things (IoT) devices.

IoT has become the new digital infrastructure by connecting everyone and everything together via billions of wireless devices. The majority of IoT devices are usually low cost, small size with limited computational capacity and energy resources, hence, cannot afford computational expensive cryptography. There is a trend to solicit non-cryptographic and lightweight solutions for IoT, as evidenced by MIT Technology Review in 2022 reporting the end of the password as the top 10 breakthrough technologies.

Radio frequency fingerprint identification (RFFI) emerges as a non-cryptographic technique for secure device identification that exploits the unique and stable hardware impairments of radio devices as their identifiers. RFFI is promising for all wireless technologies, including WiFi, Bluetooth, and cellular. While RFFI has attracted active research interests in the last decade, there are still critical research challenges remaining for a more robust and reliable RFFI system, which will be the focus of this project.

This project will bring together experts from Rice University and the University of Liverpool, UK. It will carry out a systematic and comprehensive investigation of deep learning-enhanced RFFI involving RFFI algorithm design and enhancement, adversarial attacks and countermeasures, as well as FPGA implementation. A synergistic research methodology will be adopted consisting of modelling, algorithm design, simulation and experimental evaluation as well as real implementation.

A unique outcome of this project will be the creation of robust and secure RFFI systems, validated by both simulation and practical experiments & implementation. The immediate benefits of the project are: (i) well-designed channel elimination algorithms suitable to various channel conditions, (ii) hardware feature enhancement to further improve the classification performance, (iii) practical deep learning attacks against RFFI and countermeasures, (iv) real implementation based on FPGA platforms.

The project's broader impact will be to study RFFI algorithms and feasible systems implementation for technology transfer in emerging IoT devices. The project's broader impact on education and outreach will include (i) training students as part of a collaborative, multi-institutional research team, (ii) integrating outcomes into undergraduate and graduate courses at Rice University, and (iii) making research outcomes broadly available through OpenStax courseware and the Rice RENEW and Houdini wireless testbed forums.

This collaborative U.S.- U.K. project is supported by the U.S. National Science Foundation (NSF) and the Engineering and Physical Sciences Research Council (EPSRC) of United Kingdom Research and Innovation (UKRI) where NSF funds the U.S. investigator and EPSRC funds the U.K partners.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.