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Light-weight internet-of-things device authentication, encryption and key distribution using end-to-end neural cryptosystems

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Title: Light-weight internet-of-things device authentication, encryption and key distribution using end-to-end neural cryptosystems
Authors: Sun, Y
Lo, FP-W
Lo, B
Item Type: Journal Article
Abstract: Device authentication, encryption, and key distribution are of vital importance to any Internet-of-Things (IoT) systems, such as the new smart city infrastructures. This is due to the concern that attackers could easily exploit the lack of strong security in IoT devices to gain unauthorized access to the system or to hijack IoT devices to perform denial-of-service attacks on other networks. With the rise of fog and edge computing in IoT systems, increasing numbers of IoT devices have been equipped with computing capabilities to perform data analysis with deep learning technologies. Deep learning on edge devices can be deployed in numerous applications, such as local cardiac arrhythmia detection on a smart sensing patch, but it is rarely applied to device authentication and wireless communication encryption. In this paper, we propose a novel lightweight IoT device authentication, encryption, and key distribution approach using neural cryptosystems and binary latent space. The neural cryptosystems adopt three types of end-to-end encryption schemes: symmetric, public-key, and without keys. A series of experiments were conducted to test the performance and security strength of the proposed neural cryptosystems. The experimental results demonstrate the potential of this novel approach as a promising security and privacy solution for the next-generation of IoT systems.
Issue Date: 15-Aug-2022
Date of Acceptance: 1-Jan-2021
URI: http://hdl.handle.net/10044/1/88043
DOI: 10.1109/jiot.2021.3067036
ISSN: 2327-4662
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 14978
End Page: 14987
Journal / Book Title: IEEE Internet of Things Journal
Volume: 9
Issue: 16
Copyright Statement: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (E
Bill and Melinda Gates Foundation
Bill & Melinda Gates Foundation
British Council (UK)
Funder's Grant Number: 540213 SeNTH plus
Keywords: Science & Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Computer Science
Neural networks
Deep learning
Biological neural networks
binary latent space
deep learning
Internet of Things (IoT)
0805 Distributed Computing
1005 Communications Technologies
Publication Status: Published
Online Publication Date: 2021-03-18
Appears in Collections:Department of Surgery and Cancer
Faculty of Medicine
Institute of Global Health Innovation