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Chen, Yuling, Yang, Xiong, Li, Tao, Ren, Yi 
ORCID: https://orcid.org/0000-0001-7423-6719 and Long, Yangyang
(2024)
A blockchain-empowered authentication scheme for worm detection in wireless sensor network.
Digital Communications and Networks, 10 (2).
pp. 265-272.
ISSN 2352-8648
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Abstract
Wireless Sensor Network (WSN) is a distributed sensor network composed a large number of nodes with low cost, low performance and self-management. The special structure of WSN brings both convenience and vulnerability. For example, a malicious participant can launch attacks by capturing a physical device. Therefore, node authentication that can resist malicious attacks is very important to network security. Recently, blockchain technology has shown the potential to enhance the security of the Internet of Things (IoT). In this paper, we propose a Blockchain-empowered Authentication Scheme (BAS) for WSN. In our scheme, all nodes are managed by utilizing the identity information stored on the blockchain. Besides, the simulation experiment about worm detection is executed on BAS, and the security is evaluated from detection and infection rate. The experiment results indicate that the proposed scheme can effectively inhibit the spread and infection of worms in the network.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: This work was supported by the Natural Science Foundation under Grant No. 61962009, Major Scientific and Technological Special Project of Guizhou Province under Grant No. 20183001, and Foundation of Guizhou Provincial Key Laboratory of Public Big Data under Grant No. 2018BDKFJJ003, 2018BDKFJJ005 and 2019BDKFJJ009. |
| Uncontrolled Keywords: | blockchain,node authentication,tangle,wireless sensor network (wsn),worm detection,hardware and architecture,computer networks and communications ,/dk/atira/pure/subjectarea/asjc/1700/1708 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 21 Apr 2022 09:30 |
| Last Modified: | 21 Dec 2024 01:03 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/84710 |
| DOI: | 10.1016/j.dcan.2022.04.007 |
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