Tweak: Towards Portable Deep Learning Models for Domain-Agnostic LoRa Device Authentication

Jared Gaskin, Bechir Hamdaoui, Weng Keen Wong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2022 IEEE Conference on Communications and Network Security, CNS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665462556
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 IEEE Conference on Communications and Network Security, CNS 2022 - Austin, United States
Duration: 3 Oct 20225 Oct 2022

Publication series

Name2022 IEEE Conference on Communications and Network Security, CNS 2022
Volume2022-January

Conference

Conference2022 IEEE Conference on Communications and Network Security, CNS 2022
Country/TerritoryUnited States
CityAustin
Period3/10/225/10/22

Keywords

  • Device authentication
  • domain-agnostic portable device fingerprints
  • learning model calibration

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