Machine learning based cybersecurity defense at the age of Industry 4.0

Vishavnath, Sourabh Kumar, Anwar, Adnan and Ahmed, Mohiuddin 2021, Machine learning based cybersecurity defense at the age of Industry 4.0. In Ghosh, U, Maleh, Y, Alazab, M and Khan Pathan, A-S (ed), Studies in computational intelligence, Springer International Publishing, Cham, Switzerland, pp.355-368, doi: 10.1007/978-3-030-72065-0_19.

Attached Files
Name Description MIMEType Size Downloads

Title Machine learning based cybersecurity defense at the age of Industry 4.0
Author(s) Vishavnath, Sourabh Kumar
Anwar, AdnanORCID iD for Anwar, Adnan orcid.org/0000-0003-3916-1381
Ahmed, Mohiuddin
Title of book Studies in computational intelligence
Editor(s) Ghosh, U
Maleh, Y
Alazab, M
Khan Pathan, A-S
Publication date 2021
Series Studies in computational intelligence
Start page 355
End page 368
Total pages 14
Publisher Springer International Publishing
Place of Publication Cham, Switzerland
Keyword(s) Industry 4.0
machine learning
artificial intelligence
Summary Industry 4.0 is mainly recognized as the digital transformation of the industrial sector which is driven through machine learning and artificial intelligence. It includes the historical collection of information, the capture of live data via sensors, data aggregation and connectivity between routing, gateways and other protocols, PLC integration, the dashboard for analysis and monitoring. The convergence of Machine learning (ML) and Artificial Intelligence (AI) has overcome data integration and decision-making challenges with the adoption of Industry 4.0. This article justifies the context and relevance of data sharing in the industrial sectors and the cyber threats in industry 4.0 and also provides the preventive techniques used via AI and ML. In addition, this book chapter illustrates real use cases and potential prospects for both technologies.
ISBN 9783030720643
ISSN 1860-949X
1860-9503
Language eng
DOI 10.1007/978-3-030-72065-0_19
Indigenous content off
HERDC Research category B1 Book chapter
Persistent URL http://hdl.handle.net/10536/DRO/DU:30152845

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 14 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Mon, 28 Jun 2021, 14:56:20 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.