DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding

• URL: http://arxiv.org/abs/2010.10334v1
• Date: Sun, 18 Oct 2020 03:47:36 GMT
• Title: DLWIoT: Deep Learning-based Watermarking for Authorized IoT Onboarding
• Authors: Spyridon Mastorakis, Xin Zhong, Pei-Chi Huang, Reza Tourani
• Abstract summary: We present a framework, called Deep Learning-based Watermarking for authorized IoT onboarding (DLWIoT) DLWIoT features a robust and fully automated image watermarking scheme based on deep neural networks. Our experimental results demonstrate the feasibility of DLWIoT, indicating that authorized users can onboard IoT devices with DLWIoT within 2.5-3sec.
• Score: 8.430502131775722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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