TY - GEN
T1 - Tweak
T2 - 2022 IEEE Conference on Communications and Network Security, CNS 2022
AU - Gaskin, Jared
AU - Hamdaoui, Bechir
AU - Wong, Weng Keen
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Deep learning based device fingerprinting has emerged as a key method of identifying and authenticating devices solely via their captured RF transmissions. Conventional approaches are not portable to different domains in that if a model is trained on data from one domain, it will not perform well on data from a different but related domain. Examples of such domains include the receiver hardware used for collecting the data, the day/time on which data was captured, and the protocol configuration of devices. This work proposes Tweak, a technique that, using metric learning and a calibration process, enables a model trained with data from one domain to perform well on data from another domain. This process 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 lightweight and thus suitable for resource-limited IoT networks. This work evaluates the effectiveness of Tweak vis-a-vis its ability to identify IoT devices using a testbed of real LoRa-enabled devices under various scenarios. The results of this evaluation show that Tweak is viable and especially useful for networks with limited computational resources and applications with time-sensitive missions.
AB - Deep learning based device fingerprinting has emerged as a key method of identifying and authenticating devices solely via their captured RF transmissions. Conventional approaches are not portable to different domains in that if a model is trained on data from one domain, it will not perform well on data from a different but related domain. Examples of such domains include the receiver hardware used for collecting the data, the day/time on which data was captured, and the protocol configuration of devices. This work proposes Tweak, a technique that, using metric learning and a calibration process, enables a model trained with data from one domain to perform well on data from another domain. This process 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 lightweight and thus suitable for resource-limited IoT networks. This work evaluates the effectiveness of Tweak vis-a-vis its ability to identify IoT devices using a testbed of real LoRa-enabled devices under various scenarios. The results of this evaluation show that Tweak is viable and especially useful for networks with limited computational resources and applications with time-sensitive missions.
KW - Device authentication
KW - domain-agnostic portable device fingerprints
KW - learning model calibration
UR - http://www.scopus.com/inward/record.url?scp=85142903629&partnerID=8YFLogxK
U2 - 10.1109/CNS56114.2022.10227829
DO - 10.1109/CNS56114.2022.10227829
M3 - Conference contribution
AN - SCOPUS:85142903629
T3 - 2022 IEEE Conference on Communications and Network Security, CNS 2022
BT - 2022 IEEE Conference on Communications and Network Security, CNS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 3 October 2022 through 5 October 2022
ER -