Enhancing 5G Security via Analysis of RF Hardware Characteristics and Spectral Behavior
Abstract
The research objective of this proposed effort is to secure 5G networks using their electromagnetic (EM) properties such as radio frequency front-end (RFFE) characteristics and wireless spectrum utilization. To achieve this objective, this effort will employ several technical approaches, including the use of radio frequency (RF) fingerprinting to classify different 5G devices forming the network, machine learning-based countermeasures to intelligent jamming techniques by classifying the type of interference present in the wireless spectrum, intelligent spectral overlay of private DoD 5G networks in the presence of indigenous non-DoD 5G networks within grey and contested environments, and hardware-accelerated machine learning implementations of machine learning classifiers for RF fingerprinting and intelligent jamming detection.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 07, 2024
- Source ID
- FA95502310651
Entities
People
- Alexander M. Wyglinski
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- Worcester Polytechnic Institute