Blockchained Adaptive Federated Auto MetaLearning BigData and DevOps CyberSecurity Architecture in Industry 4.0
Ημερομηνία
2021Βιβλιογραφικά στοιχεία
Demertzis, K., Iliadis, L., Pimenidis, E., Tziritas, N., Koziri, M., Kikiras, P. (2021). Blockchained Adaptive Federated Auto MetaLearning BigData and DevOps CyberSecurity Architecture in Industry 4.0. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_29
Επιτομή
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.