HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication

Luke Puppo, Weng Keen Wong, Bechir Hamdaoui, Abdurrahman Elmaghbub

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

Abstract

New capabilities in wireless network security have been enabled by deep learning, which leverages patterns in radio frequency (RF) data to identify and authenticate devices. Open-set detection is an area of deep learning that identifies samples captured from new devices during deployment that were not part of the training set. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. We introduce a novel open-set detection approach based on the patterns of the hidden state values within a Convolutional Neural Network Long Short-Term Memory model. Our approach greatly improves the Area Under the Precision-Recall Curve on LoRa, Wireless-WiFi, and Wired-WiFi datasets, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices.

Original languageEnglish
Title of host publicationISCC 2023 - 28th IEEE Symposium on Computers and Communications
Subtitle of host publicationComputers and Communications for the Benefits of Humanity
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1122-1128
Number of pages7
ISBN (Electronic)9798350300482
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event28th IEEE Symposium on Computers and Communications, ISCC 2023 - Hybrid, Gammarth, Tunisia
Duration: 9 Jul 202312 Jul 2023

Publication series

NameProceedings - IEEE Symposium on Computers and Communications
Volume2023-July
ISSN (Print)1530-1346

Conference

Conference28th IEEE Symposium on Computers and Communications, ISCC 2023
Country/TerritoryTunisia
CityHybrid, Gammarth
Period9/07/2312/07/23

Keywords

  • Device authentication
  • RF device fingerprinting
  • deep learning
  • open-set detection

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