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Title	Blockchain And Machine Learning Security For The Internet Of Things: A Bibliometric Analysis
ID_Doc	12379
Authors	Mudhar J.K.; Malhotra J.; Singh A.K.; Anand M.
Year	2023
Published	Proceedings - 2023 3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023
DOI	http://dx.doi.org/10.1109/ICPCSN58827.2023.00202
Abstract	The Internet of Things has developed as a catalyst for innovation across numerous industries, from home automation to smart cities to 4IR primarily Industry 4.0. Through the internet, IoT devices interact and exchange information. Beyond its potential for development, IoT devices are divergent, ubiquitous, memory-and computational-power-constrained, making them vulnerable to a range of security and privacy risks. Blockchain, a decentralised and tamper-proof technology, has recently been introduced in the BIoT domain for securing data handling activities and inculcating trusted transactions. In anticipation of IoT System vulnerabilities, Machine Learning (ML) algorithms yield accurate findings for large and complicated databases. This study conducts a bibliometric study from the Web of Science (WoS) database from 2017 to 2022 in the area of ML-based BIoT security using Prisma guidelines. Rankings of the most productive and influential writers, articles, and sources are based on total publications and citations. Text data content analysis, Author keywords, index keywords are utilized to highlight research hotspots and future trends in the ML-Based BIoT security domain. © 2023 IEEE.
Author Keywords	Bibliometric analysis; Blockchain; IoT; Machine Learning; Security; Web of Science

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