A blockchain-based deep learning approach for cyber security in next generation industrial cyber-physical systems

Shailendra Rathore, Jong Hyuk Park

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

With the recent development of Internet of Things (IoT) in the next generation cyber-physical system (CPS) such as autonomous driving, there is a significant requirement of big data analysis with high accuracy and low latency. For efficient big data analysis, deep learning (DL) supports strong analytic capability; it has been applied at the cloud and edge layers by extensive research to provide accurate data analysis at low latency. However, existing researches failed to address certain challenges, such as centralized control, adversarial attacks, security, and privacy. To this end, we propose DeepBlockIoTNet, a secure DL approach with blockchain for the IoT network wherein the DL operation is carried out among the edge nodes at the edge layer in a decentralized, secure manner. The blockchain provides a secure DL operation and removes the control from a centralized authority. The experimental evaluation demonstrates that the proposed approach supports higher accuracy.
Original languageEnglish
Pages (from-to)5522-5532
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number8
Early online date26 Nov 2020
DOIs
Publication statusPublished - 1 Aug 2021
Externally publishedYes

Keywords

  • Blockchain
  • Cyber-physical systems (CPS)
  • Deep learning (DL)
  • Internet of Things (IoT)
  • Security and privacy

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