2020/06/12 by Sekhar Rajendran, Zhi Sun, Rajendran, Sekhar +5
Computer Science · Engineering · #Advanced Wireless Communication Technologies #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Electrical engineering #Signal Processing (eess.SP) #Systems and Control (eess.SY) #Wireless Communication Security Techniques #Wireless Signal Modulation Classification #cs.CR #cs.SY #eess.SP #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2006.06895
Keywords: Physical layer Security with Reconfigurable Intelligent Surface, Intelligent Reflective Surface, RF-Fingerprint, IoT security, Internet of things, Channel Robust; 13 pages, 11 figures, This paper is submitted to IEEE TIFS
openalex publication_date 2020/06/12 · openalex created_date 2020/06/19 · arxiv created 2020/12/05 · arxiv updated 2020/12/08 · openalex updated_date 2026/07/28
In Internet of Things, where billions of devices with limited resources are communicating with each other, security has become a major stumbling block affecting the progress of this technology. Existing authentication schemes-based on digital signatures have overhead costs associated with them in terms of computation time, battery power, bandwidth, memory, and related hardware costs. Radio frequency fingerprint (RFF), utilizing the unique device-based information, can be a promising solution for IoT. However, traditional RFFs have become obsolete because of low reliability and reduced user capability. Our proposed solution, Metasurface RF-Fingerprinting Injection (MeRFFI), is to inject a carefully-designed radio frequency fingerprint into the wireless physical layer that can increase the security of a stationary IoT device with minimal overhead. The injection of fingerprint is implemented using a low cost metasurface developed and fabricated in our lab, which is designed to make small but detectable perturbations in the specific frequency band in which the IoT devices are communicating. We have conducted comprehensive system evaluations including distance, orientation, multiple channels where the feasibility, effectiveness, and reliability of these fingerprints are validated. The proposed MeRFFI system can be easily integrated into the existing authentication schemes. The security vulnerabilities are analyzed for some of the most threatening wireless physical layer-based attacks.