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Blockchained Αdaptive Federated Auto Meta Learning Big Data and DevOps CyberSecurity Architecture in Industry 4.0

Kikiras, Panagiotis; Koziri, Maria; Tziritas, Nikolaos; Pimenidis, Elias; Iliadis, Lazaros; Demertzis, Konstantinos

Authors

Panagiotis Kikiras

Maria Koziri

Nikolaos Tziritas

Lazaros Iliadis liliadis@civil.duth.gr

Konstantinos Demertzis kdemertz@fmenr.duth.gr1



Abstract

Maximizing the production process in modern industry, as proposed by Industry 4.0, requires extensive use of Cyber-Physical Systems (CbPS). Artificial intelligence technologies, through CbPS, allow monitoring of natural processes , making autonomous, decentralized and optimal decisions. Collection of information that optimizes the effectiveness of decisions , implies the need for big data management and analysis. This data is usually coming from heterogeneous sources and it might be non-interoperable. Big data management is further complicated by the need to protect information, to ensure business confidentiality and privacy, according to the recent General Data Protection Regulation-GDPR. This paper introduces an innovative holistic Blockchained Adaptive Federated Auto Meta Learning Big Data and DevOps Cyber Security Architecture in Industry 4.0. The aim is to fill the gap found in the ways of handling and securing industrial data. This architecture, combines the most modern software development technologies under an optimal and efficient framework. It successfully achieves the prediction and assessment of threat-related conditions in an industrial ecosystem, while ensuring privacy and secrecy.

Citation

Kikiras, P., Koziri, M., Tziritas, N., Pimenidis, E., Iliadis, L., & Demertzis, K. (2021). Blockchained Αdaptive Federated Auto Meta Learning Big Data and DevOps CyberSecurity Architecture in Industry 4.0. In Proceedings of the 22nd Engineering Applications of Neural Networks Conference (345-363). https://doi.org/10.1007/978-3-030-80568-5_29

Conference Name 22nd International Conference on Engineering Applications of Neural Networks
Conference Location Crete, Greece
Start Date Jun 25, 2021
End Date Jun 27, 2021
Acceptance Date Apr 18, 2021
Online Publication Date Jul 1, 2021
Publication Date Jul 1, 2021
Deposit Date May 17, 2021
Publicly Available Date Jul 2, 2022
Publisher Springer (part of Springer Nature)
Pages 345-363
Series Title Proceedings of the International Neural Networks Society
Series Number 3
Series ISSN 2661-8141
Book Title Proceedings of the 22nd Engineering Applications of Neural Networks Conference
ISBN 9783030805678
DOI https://doi.org/10.1007/978-3-030-80568-5_29
Keywords Blockchain; MetaLearning; Federated Learning; CyberSecurity; Privacy; Industry 40; GDPR
Public URL https://uwe-repository.worktribe.com/output/7336961
Publisher URL https://www.springer.com/series/16268?detailsPage=titles

Files

This file is under embargo until Jul 2, 2022 due to copyright reasons.

Contact Elias.Pimenidis@uwe.ac.uk to request a copy for personal use.






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