Blockchain and Deep Learning for Secure Communication in Digital Twin Empowered Industrial IoT Network
Kumar, Prabhat; Kumar, Randhir; Kumar, Abhinav; Franklin, A. Antony; Garg, Sahil; Singh, Satinder (2022-07-18)
Post-print / Final draft
Kumar, Prabhat
Kumar, Randhir
Kumar, Abhinav
Franklin, A. Antony
Garg, Sahil
Singh, Satinder
18.07.2022
IEEE transactions on network science and engineering
IEEE
School of Engineering Science
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© IEEE 2022
© IEEE 2022
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2022080552904
https://urn.fi/URN:NBN:fi-fe2022080552904
Tiivistelmä
The rapid expansion of the Industrial Internet of Things (IIoT) necessitates the digitization of industrial processes in order to increase network efficiency. The integration of Digital Twin (DT) with IIoT digitizes physical objects into virtual representations to improve data analytics performance. Nevertheless, DT empowered IIoT generates a massive amount of data that is mostly sent to the cloud or edge servers for real-time analysis. However, unreliable public communication channels and lack of trust among participating entities causes various types of threats and attacks on the ongoing communication. Motivated from the aforementioned discussion, we present a blockchain and Deep Learning (DL) integrated framework for delivering decentralized data processing and learning in IIoT network. The framework first present a new DT model that facilitates construction of a virtual environment to simulate and replicate security-critical processes of IIoT. Second, we propose a blockchain-based data transmission scheme that uses smart contracts to ensure integrity and authenticity of data. Finally, the DL scheme is designed to apply the Intrusion Detection System (IDS) against valid data retrieved from blockchain. In DL scheme, a Long Short Term Memory-Sparse AutoEncoder (LSTMSAE) technique is proposed to learn the spatial-temporal representation. The extracted characteristics are further used by the proposed Multi-Head Self-Attention (MHSA)-based Bidirectional Gated Recurrent Unit (BiGRU) algorithm to learn long-distance features and accurately detect attacks. The practical implementation of our proposed framework proves considerable enhancement of communication security and data privacy in DT empowered IIoT network.
Lähdeviite
Kumar, P., Kumar, R., Kumar, A., Franklin, A. A., Garg, S., Singh, S. (2022). Blockchain and Deep Learning for Secure Communication in Digital Twin Empowered Industrial IoT Network. IEEE transactions on network science and engineering. DOI: 10.1109/TNSE.2022.3191601
Kokoelmat
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