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Anomaly detection via blockchained deep learning smart contracts in industry 4.0

  • S.I. : Emerging applications of Deep Learning and Spiking ANN
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Abstract

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.

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Authors and Affiliations

  1. Department of Civil Engineering, School of Engineering, Faculty of Mathematics Programming and General Courses, Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Konstantinos Demertzis & Lazaros Iliadis

  2. Research Center for Cloud Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Nikos Tziritas

  3. Department of Computer Science, School of Science, University of Thessaly, Lamia, Greece

    Panagiotis Kikiras

Authors
  1. Konstantinos Demertzis
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  2. Lazaros Iliadis
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  3. Nikos Tziritas
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  4. Panagiotis Kikiras
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Correspondence to Lazaros Iliadis.

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Demertzis, K., Iliadis, L., Tziritas, N. et al. Anomaly detection via blockchained deep learning smart contracts in industry 4.0. Neural Comput & Applic 32, 17361–17378 (2020). https://doi.org/10.1007/s00521-020-05189-8

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