%P 517-523
%T Adaptation of Machine Learning and Blockchain Technology in Cyber-Physical System Applications: A Concept Paper
%A M. Abdullahi
%A H. Alhussian
%A N. Aziz
%I Springer Science and Business Media Deutschland GmbH
%V 758
%D 2022
%R 10.1007/978-981-16-2183-3₄₈
%O cited By 1; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
%J Lecture Notes in Electrical Engineering
%L scholars17380
%X 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.
%K Blockchain; Crime; Cybersecurity; Decision trees; Embedded systems; Engineering education; Learning systems; Support vector machines, Block-chain; Blockchain technology; Cybe-physical system; Cybe-physical systems; Cyber-physical systems; Machine learning; Machine-learning; Privacy; Security; System applications, Cyber Physical System