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

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.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 22nd International Conference on Engineering Applications of Neural Networks
• 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