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Survey of Distributed and Decentralized IoT Securities: Approaches Using Deep Learning and Blockchain Technology

Author

Listed:
  • Ayodeji Falayi

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

  • Qianlong Wang

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

  • Weixian Liao

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

  • Wei Yu

    (Department of Computer and Information Sciences, Towson University, Towson, MD 21252, USA)

Abstract

The Internet of Things (IoT) continues to attract attention in the context of computational resource growth. Various disciplines and fields have begun to employ IoT integration technologies in order to enable smart applications. The main difficulty in supporting industrial development in this scenario involves potential risk or malicious activities occurring in the network. However, there are tensions that are difficult to overcome at this stage in the development of IoT technology. In this situation, the future of security architecture development will involve enabling automatic and smart protection systems. Due to the vulnerability of current IoT devices, it is insufficient to ensure system security by implementing only traditional security tools such as encryption and access control. Deep learning and blockchain technology has now become crucial, as it provides distinct and secure approaches to IoT network security. The aim of this survey paper is to elaborate on the application of deep learning and blockchain technology in the IoT to ensure secure utility. We first provide an introduction to the IoT, deep learning, and blockchain technology, as well as a discussion of their respective security features. We then outline the main obstacles and problems of trusted IoT and how blockchain and deep learning may be able to help. Next, we present the future challenges in integrating deep learning and blockchain technology into the IoT. Finally, as a demonstration of the value of blockchain in establishing trust, we provide a comparison between conventional trust management methods and those based on blockchain.

Suggested Citation

  • Ayodeji Falayi & Qianlong Wang & Weixian Liao & Wei Yu, 2023. "Survey of Distributed and Decentralized IoT Securities: Approaches Using Deep Learning and Blockchain Technology," Future Internet, MDPI, vol. 15(5), pages 1-28, May.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:5:p:178-:d:1144480
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    References listed on IDEAS

    as
    1. Abdurrahman Pektaş & Tankut Acarman, 2018. "Botnet detection based on network flow summary and deep learning," International Journal of Network Management, John Wiley & Sons, vol. 28(6), November.
    2. Razi Iqbal & Talal Ashraf Butt & Muhammad Afzaal & Khaled Salah, 2019. "Trust management in social Internet of vehicles: Factors, challenges, blockchain, and fog solutions," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477198, January.
    3. Angelis, Jannis & Ribeiro da Silva, Elias, 2019. "Blockchain adoption: A value driver perspective," Business Horizons, Elsevier, vol. 62(3), pages 307-314.
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    Cited by:

    1. Shrouk A. Ali & Shaimaa Ahmed Elsaid & Abdelhamied A. Ateya & Mohammed ElAffendi & Ahmed A. Abd El-Latif, 2023. "Enabling Technologies for Next-Generation Smart Cities: A Comprehensive Review and Research Directions," Future Internet, MDPI, vol. 15(12), pages 1-43, December.

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