relation: https://khub.utp.edu.my/scholars/17380/
title: Adaptation of Machine Learning and Blockchain Technology in Cyber-Physical System Applications: A Concept Paper
creator: Abdullahi, M.
creator: Alhussian, H.
creator: Aziz, N.
description: In recent years, Cyber-Physical Systems (CPS) have been adopted in various sectors such as smart cities, smart industries etc. These types of systems continuously generate a huge amount of data which increasingly attract cyber-crimes. There are several existing approaches produced to overcome these issues by using Blockchain Technology (BT) such as Public, Private, Construme, Hybrid Blockchain-based on CPS applications and Machine Learning (ML) such as Support Vector Machine (SVM), Linear Regression, and Decision Tree etc. With the rapid increase in data size affix with cyber-crimes, such approaches become less effective and therefore necessitate the invention of a more robust and self-trainable approach. In this paper, we presented brief details on ML and BT and how they can be adopted in CPS applications to solve security issues concerning cyber-crimes. The architecture was also presented to depict the proposed method. Moreover, technologies/techniques which can be implemented in CPS applications are discovered such as industrial automation, smart buildings, medical systems, and vehicular systems. We also have some future scope and conclusion. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2022
type: Article
type: PeerReviewed
identifier:   Abdullahi, M. and Alhussian, H. and Aziz, N.  (2022) Adaptation of Machine Learning and Blockchain Technology in Cyber-Physical System Applications: A Concept Paper.  Lecture Notes in Electrical Engineering, 758.  pp. 517-523.  ISSN 18761100     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142741921&doi=10.1007%2f978-981-16-2183-3_48&partnerID=40&md5=d0a404556b629fd3e69a59e41762e187
relation: 10.1007/978-981-16-2183-3₄₈
identifier: 10.1007/978-981-16-2183-3₄₈