TY - JOUR
T1 - Deep Learning Model Portability for Domain-Agnostic Device Fingerprinting
AU - Gaskin, Jared
AU - Elmaghbub, Abdurrahman
AU - Hamdaoui, Bechir
AU - Wong, Weng Keen
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2023
Y1 - 2023
N2 - RF (Radio Frequency) device fingerprinting approaches using deep learning have recently emerged as potential methods of identifying devices solely based on their RF transmissions. However, these recently proposed approaches suffer from the domain portability problem, in that when the deep learning models used for fingerprinting are trained on data collected on one (source) domain but tested on data collected on a different (target) domain, the models will not perform well. For example, a change in the receiver used for data collection, in the day on which data was captured, or in the configuration settings of transmitters amounts to a change in the domain. This work proposes a technique that uses metric learning and model calibration to enable a model trained with data from one source domain to perform well on data collected on another target domain. This is accomplished with only a small amount of training data from the target domain and without changing the weights of the model, which makes the technique computationally quick. The effectiveness of the proposed technique is assessed using RF data captured using a testbed of real devices and under various different setup scenarios. Our results show that the proposed technique is viable and useful for networks with limited computational resources and applications with time-critical missions.
AB - RF (Radio Frequency) device fingerprinting approaches using deep learning have recently emerged as potential methods of identifying devices solely based on their RF transmissions. However, these recently proposed approaches suffer from the domain portability problem, in that when the deep learning models used for fingerprinting are trained on data collected on one (source) domain but tested on data collected on a different (target) domain, the models will not perform well. For example, a change in the receiver used for data collection, in the day on which data was captured, or in the configuration settings of transmitters amounts to a change in the domain. This work proposes a technique that uses metric learning and model calibration to enable a model trained with data from one source domain to perform well on data collected on another target domain. This is accomplished with only a small amount of training data from the target domain and without changing the weights of the model, which makes the technique computationally quick. The effectiveness of the proposed technique is assessed using RF data captured using a testbed of real devices and under various different setup scenarios. Our results show that the proposed technique is viable and useful for networks with limited computational resources and applications with time-critical missions.
KW - Device/RF fingerprinting
KW - Hardware impairments
KW - Network device authentication
KW - Portable deep learning
UR - http://www.scopus.com/inward/record.url?scp=85168266150&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2023.3305257
DO - 10.1109/ACCESS.2023.3305257
M3 - Article
AN - SCOPUS:85168266150
SN - 2169-3536
VL - 11
SP - 86801
EP - 86823
JO - IEEE Access
JF - IEEE Access
ER -