DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding

TL;DR

This paper introduces DLWIoT, a deep learning-based watermarking framework that embeds user credentials into device images to ensure secure, authorized onboarding of IoT devices, addressing vulnerabilities of traditional methods.

Contribution

The paper presents a novel deep neural network-based watermarking scheme for IoT onboarding that is robust, automated, and embeds credentials into images for secure access.

Findings

Authorized onboarding within 2.5-3 seconds

Effective embedding of user credentials into images

Potential to prevent unauthorized device access

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.