DLWIoT: Deep learning-based watermarking for authorized IoT onboarding

Spyridon Mastorakis, Xin Zhong, Pei Chi Huang, Reza Tourani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The onboarding of IoT devices by authorized users constitutes both a challenge and a necessity in a world, where the number of IoT devices and the tampering attacks against them continuously increase. Commonly used onboarding techniques today include the use of QR codes, pin codes, or serial numbers. These techniques typically do not protect against unauthorized device access-a QR code is physically printed on the device, while a pin code may be included in the device packaging. As a result, any entity that has physical access to a device can onboard it onto their network and, potentially, tamper it (e.g., install malware on the device). To address this problem, in this paper, we present a framework, called Deep Learning-based Watermarking for authorized IoT onboarding (DLWIoT), featuring a robust and fully automated image watermarking scheme based on deep neural networks. DLWIoT embeds user credentials into carrier images (e.g., QR codes printed on IoT devices), thus enables IoT onboarding only by authorized users. Our experimental results demonstrate the feasibility of DLWIoT, indicating that authorized users can onboard IoT devices with DLWIoT within 2.5-3sec.

Original languageEnglish (US)
Title of host publication2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728197944
DOIs
StatePublished - Jan 9 2021
Event18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021 - Virtual, Las Vegas, United States
Duration: Jan 9 2021Jan 13 2021

Publication series

Name2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021

Conference

Conference18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021
Country/TerritoryUnited States
CityVirtual, Las Vegas
Period1/9/211/13/21

Keywords

  • Deep learning
  • Internet of Things (IoT)
  • IoT onboarding
  • Watermarking

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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