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An IoT Authentication Framework for Urban Infrastructure Security Using Blockchain and Deep Learning

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Engineering Applications of Neural Networks (EANN 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1600))

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Abstract

Internet of Things (IoT) enables increased connectivity between devices. However, this benefit also intrinsically increases cybersecurity risks as cyber attackers are provided with expanded network access and additional digital targets. To address this issue, this paper presents a holistic digital and physical cybersecurity user authentication framework, based on Blockchain and Deep Learning (BLDE) algorithms. The introduced user authentication framework, provides an additional layer of resilience against Cybersecurity attacks that may arise through the IoT. Moreover, it controls digital access through the seven OSI layers and via the physical user’s identity, such as finger prints or self-images, before the user is accepted in the IoT network. Finally, it offers many layers of security through its decentralized and distributed nature, in order to reduce the system’s vulnerability from cyber threats.

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Authors and Affiliations

  1. Department of Civil Engineering-Lab of Mathematics and Informatics (ISCE), Democritus University of Thrace, 67100, Xanthi, Greece

    Anastasios Panagiotis Psathas, Lazaros Iliadis, Antonios Papaleonidas & Dimitris Bountas

Authors
  1. Anastasios Panagiotis Psathas
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  2. Lazaros Iliadis
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  3. Antonios Papaleonidas
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  4. Dimitris Bountas
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Correspondence to Anastasios Panagiotis Psathas .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Teesside University, Middlesbrough, UK

    Chrisina Jayne

  3. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Anastasios Tefas

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Psathas, A.P., Iliadis, L., Papaleonidas, A., Bountas, D. (2022). An IoT Authentication Framework for Urban Infrastructure Security Using Blockchain and Deep Learning. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_24

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  • DOI: https://doi.org/10.1007/978-3-031-08223-8_24

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