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Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things

Karanjeet Choudhary, Gurjot Singh Gaba, Rajan Miglani, Lavish Kansal, Pardeep Kumar Orcid Logo

Intelligent Wireless Communications, Pages: 329 - 361

Swansea University Author: Pardeep Kumar Orcid Logo

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DOI (Published version): 10.1049/pbte094e_ch13

Abstract

Advent of digital sensors and machines led to a significant acceleration in industrial evolution. The desire to automate industrial processes with minimum human intervention paved the way for the onset of a new era of technological nomenclature called the industrial Internet of things (IIoT). A rema...

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Published in: Intelligent Wireless Communications
ISBN: 9781839530951 9781839530968
Published: Institution of Engineering and Technology 2021
URI: https://cronfa.swan.ac.uk/Record/cronfa58126
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spelling 2021-10-27T15:40:09.2813415 v2 58126 2021-09-28 Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things 90a5efa66b9ae87756f5b059eb06ef1e 0000-0001-8124-5509 Pardeep Kumar Pardeep Kumar true false 2021-09-28 SCS Advent of digital sensors and machines led to a significant acceleration in industrial evolution. The desire to automate industrial processes with minimum human intervention paved the way for the onset of a new era of technological nomenclature called the industrial Internet of things (IIoT). A remarkable feature of IIoT is its underlying architecture which allows the managers/engineers/supervisors to remotely operate and access the performance of their machines. Industries ranging from healthcare, finance, logistics, and power have witnessed a major performance increment and quality stabilization by transforming themselves into an IIoT empowered smart environment. However, this transformation has brought with itself a whole new set of challenges with cybersecurity being the paramount. The vulnerabilities like bugs and broken processes can lead to a serious compromise or even collapse of security mechanisms of IIoT networks. Such a situation will have a devastating impact on the financial health, reputation, and credibility of companies. After an extensive review of existing technologies, we believe that blockchain, artificial intelligence (AI), and machine learning (ML) can complement each other in building a revolutionary deterrent to negate malicious activities that in any form intend to harm the system. While, blockchain offers public/private/consortium relationships, ML and AI, on the other hand, follow the principle of supervised/ unsupervised/reinforcement learning and reactive/memory approaches, respectively. Based on the distributed ledger system, blockchain mechanisms can be aided with self-learning algorithms which will update and strengthen the database by learning each time the system suffers new forms of network attacks and intrusions. This process of learning will help build a robust system which can learn to optimize its deterrence procedures against different forms of attacks. It is due to these overwhelming benefits, blockchain, AI, and ML find applications in smart logistics, predictive maintenance, autonomous vehicles, intelligent manufacturing, and smart grid maintenance. Book chapter Intelligent Wireless Communications 329 361 Institution of Engineering and Technology 9781839530951 9781839530968 14 4 2021 2021-04-14 10.1049/pbte094e_ch13 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-10-27T15:40:09.2813415 2021-09-28T11:27:39.0536498 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Karanjeet Choudhary 1 Gurjot Singh Gaba 2 Rajan Miglani 3 Lavish Kansal 4 Pardeep Kumar 0000-0001-8124-5509 5
title Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things
spellingShingle Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things
Pardeep Kumar
title_short Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things
title_full Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things
title_fullStr Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things
title_full_unstemmed Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things
title_sort Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things
author_id_str_mv 90a5efa66b9ae87756f5b059eb06ef1e
author_id_fullname_str_mv 90a5efa66b9ae87756f5b059eb06ef1e_***_Pardeep Kumar
author Pardeep Kumar
author2 Karanjeet Choudhary
Gurjot Singh Gaba
Rajan Miglani
Lavish Kansal
Pardeep Kumar
format Book chapter
container_title Intelligent Wireless Communications
container_start_page 329
publishDate 2021
institution Swansea University
isbn 9781839530951
9781839530968
doi_str_mv 10.1049/pbte094e_ch13
publisher Institution of Engineering and Technology
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
active_str 0
description Advent of digital sensors and machines led to a significant acceleration in industrial evolution. The desire to automate industrial processes with minimum human intervention paved the way for the onset of a new era of technological nomenclature called the industrial Internet of things (IIoT). A remarkable feature of IIoT is its underlying architecture which allows the managers/engineers/supervisors to remotely operate and access the performance of their machines. Industries ranging from healthcare, finance, logistics, and power have witnessed a major performance increment and quality stabilization by transforming themselves into an IIoT empowered smart environment. However, this transformation has brought with itself a whole new set of challenges with cybersecurity being the paramount. The vulnerabilities like bugs and broken processes can lead to a serious compromise or even collapse of security mechanisms of IIoT networks. Such a situation will have a devastating impact on the financial health, reputation, and credibility of companies. After an extensive review of existing technologies, we believe that blockchain, artificial intelligence (AI), and machine learning (ML) can complement each other in building a revolutionary deterrent to negate malicious activities that in any form intend to harm the system. While, blockchain offers public/private/consortium relationships, ML and AI, on the other hand, follow the principle of supervised/ unsupervised/reinforcement learning and reactive/memory approaches, respectively. Based on the distributed ledger system, blockchain mechanisms can be aided with self-learning algorithms which will update and strengthen the database by learning each time the system suffers new forms of network attacks and intrusions. This process of learning will help build a robust system which can learn to optimize its deterrence procedures against different forms of attacks. It is due to these overwhelming benefits, blockchain, AI, and ML find applications in smart logistics, predictive maintenance, autonomous vehicles, intelligent manufacturing, and smart grid maintenance.
published_date 2021-04-14T04:14:24Z
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score 11.037603

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