22nd EANN 2021, 25 - 27 June 2021, Greece

Blockchained Αdaptive Federated Auto MetaLearning BigData and DevOps CyberSecurity Architecture in Industry 4.0

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


  Maximizing the production process in modern industry, as proposed by Indus-try 4.0, re-quires 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 infor-mation that optimizes the effectiveness of deci-sions, 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 fur-ther complicated by the need to protect information, to ensure business confidentiali-ty and privacy, according to the recent General Data Protection Regulation - GDPR. This paper in-troduces an innovative holistic Blockchained Adaptive Federated Auto Meta Learning Big Data and DevOps Cyber Security Architec-ture 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 success-fully achieves the prediction and assessment of threat-related conditions in an in-dustrial ecosystem, while ensuring privacy and secrecy.  

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