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Security Threats and Promising Solutions Arising from the Intersection of AI and IoT: A Study of IoMT and IoET Applications

Author

Listed:
  • Hadeel Alrubayyi

    (School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
    These authors contributed equally to this work.)

  • Moudy Sharaf Alshareef

    (School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
    These authors contributed equally to this work.)

  • Zunaira Nadeem

    (School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
    These authors contributed equally to this work.)

  • Ahmed M. Abdelmoniem

    (School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK)

  • Mona Jaber

    (School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK)

Abstract

The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, which are empowered by artificial intelligence, need to be investigated with cognisance of security threats and mitigation schemes that are tailored to their specific constraints and requirements. In this work, we present a comprehensive review of security threats in IoT and emerging countermeasures with a particular focus on malware and man-in-the-middle attacks. Next, we elaborate on two use cases: the Internet of Energy Things and the Internet of Medical Things. Innovative artificial intelligence methods for automating energy theft detection and stress levels are first detailed, followed by an examination of contextual security threats and privacy breach concerns. An artificial immune system is employed to mitigate the risk of malware attacks, differential privacy is proposed for data protection, and federated learning is harnessed to reduce data exposure.

Suggested Citation

  • Hadeel Alrubayyi & Moudy Sharaf Alshareef & Zunaira Nadeem & Ahmed M. Abdelmoniem & Mona Jaber, 2024. "Security Threats and Promising Solutions Arising from the Intersection of AI and IoT: A Study of IoMT and IoET Applications," Future Internet, MDPI, vol. 16(3), pages 1-19, February.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:3:p:85-:d:1349112
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    References listed on IDEAS

    as
    1. Jose A. Afonso & Vitor Monteiro & Joao L. Afonso, 2023. "Internet of Things Systems and Applications for Smart Buildings," Energies, MDPI, vol. 16(6), pages 1-3, March.
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