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Anomaly detection via blockchained deep learning smart contracts in industry 4.0

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Název: Anomaly detection via blockchained deep learning smart contracts in industry 4.0
Autoři: Demertzis K., Iliadis L., Tziritas N., Kikiras P.
Zdroj: Neural Computing and Applications ; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088317750&doi=10.1007%2fs00521-020-05189-8&partnerID=40&md5=617ad36ac03a642add4bec4d69f1c6f3
Rok vydání: 2020
Sbírka: University of Thessaly Institutional Repository / Ιδρυματικό Αποθετήριο Πανεπιστημίου Θεσσαλίας
Témata: Anomaly detection, Blockchain, Computer systems programming, Deep neural networks, Industrial internet of things (IIoT), Industrial research, Industry 4.0, Network architecture, Network security, Archiving systems, Changing environment, Distributed platforms, Intelligent solutions, Network communications, Operating parameters, Security Architecture, Structural elements, Deep learning, Springer Science and Business Media Deutschland GmbH
Popis: The complexity of threats in the ever-changing environment of modern industry is constantly increasing. At the same time, traditional security systems fail to detect serious threats of increasing depth and duration. Therefore, alternative, intelligent solutions should be used to detect anomalies in the operating parameters of the infrastructures concerned, while ensuring the anonymity and confidentiality of industrial information. Blockchain is an encrypted, distributed archiving system designed to allow for the creation of real-time log files that are unequivocally linked. This ensures the security and transparency of transactions. This research presents, for the first time in the literature, an innovative Blockchain Security Architecture that aims to ensure network communication between traded Industrial Internet of Things devices, following the Industry 4.0 standard and based on Deep Learning Smart Contracts. The proposed smart contracts are implementing (via computer programming) a bilateral traffic control agreement to detect anomalies based on a trained Deep Autoencoder Neural Network. This architecture enables the creation of a secure distributed platform that can control and complete associated transactions in critical infrastructure networks, without the intervention of a single central authority. It is a novel approach that fuses artificial intelligence in the Blockchain, not as a supportive framework that enhances the capabilities of the network, but as an active structural element, indispensable and necessary for its completion. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
Druh dokumentu: article in journal/newspaper
Jazyk: English
ISSN: 09410643
Relation: http://hdl.handle.net/11615/73205
DOI: 10.1007/s00521-020-05189-8
Dostupnost: http://hdl.handle.net/11615/73205
https://doi.org/10.1007/s00521-020-05189-8
Přístupové číslo: edsbas.52632D48
Databáze: BASE
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  Data: Anomaly detection via blockchained deep learning smart contracts in industry 4.0
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  Data: Neural Computing and Applications ; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088317750&doi=10.1007%2fs00521-020-05189-8&partnerID=40&md5=617ad36ac03a642add4bec4d69f1c6f3
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  Data: The complexity of threats in the ever-changing environment of modern industry is constantly increasing. At the same time, traditional security systems fail to detect serious threats of increasing depth and duration. Therefore, alternative, intelligent solutions should be used to detect anomalies in the operating parameters of the infrastructures concerned, while ensuring the anonymity and confidentiality of industrial information. Blockchain is an encrypted, distributed archiving system designed to allow for the creation of real-time log files that are unequivocally linked. This ensures the security and transparency of transactions. This research presents, for the first time in the literature, an innovative Blockchain Security Architecture that aims to ensure network communication between traded Industrial Internet of Things devices, following the Industry 4.0 standard and based on Deep Learning Smart Contracts. The proposed smart contracts are implementing (via computer programming) a bilateral traffic control agreement to detect anomalies based on a trained Deep Autoencoder Neural Network. This architecture enables the creation of a secure distributed platform that can control and complete associated transactions in critical infrastructure networks, without the intervention of a single central authority. It is a novel approach that fuses artificial intelligence in the Blockchain, not as a supportive framework that enhances the capabilities of the network, but as an active structural element, indispensable and necessary for its completion. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
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