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Artificial intelligence and machine learning aided blockchain systems to address security vulnerabilities and threats in the industrial Internet of things
Intelligent Wireless Communications, Pages: 329 - 361
Swansea University Author: Pardeep Kumar
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DOI (Published version): 10.1049/pbte094e_ch13
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...
|Published in:||Intelligent Wireless Communications|
Institution of Engineering and Technology
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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.
Faculty of Science and Engineering