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A Deep Learning-Based Intrusion Detection Technique for a Secured IoMT System

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Informatics and Intelligent Applications (ICIIA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1547))

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Abstract

The emergence of medical sensors in smart healthcare has brought about an intelligent Internet of Medical Things (IoMT) system for detecting life-threatening ill-ness globally. The IoMT-based system has been used to generate a huge amount of data that experts can use for various purposes like diagnosis, prediction, and real-time monitoring of patients. However, patients’ health data must be transferred to cloud database storage and external computing devices for processing due to the limited storage capability and calculation capability of IoMT-based devices. This can result in security and privacy issues due to a lack of control over the patient’s health information and the network’s vulnerability to numerous forms of assaults. Therefore, this paper proposes a swarm-neural net-work-based model to detect intruders in the data-centric IoMT-based system. The proposed model can be used to detect intruders during data transfer, allowing for efficient and accurate analysis of healthcare data at the network’s edge. The performance of the system was tested using a real-time NF-ToN-IoT dataset for IoT applications that collected telemetry, operating systems, and network data. The results of the proposed model are compared over the standard intrusion detection classification models that use the same dataset using various performance metrics. The experimental results reveal that the proposed model attains 89.0% accuracy over the ToN-IoT dataset.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde & Rasheed Gbenga Jimoh

  2. Department of Computer Science, Landmark University, Omu Aran, Nigeria

    Kazeem Moses Abiodun & Emmanuel Abidemi Adeniyi

  3. Department of Mathematical Science, Olabisi Onabanjo University, Ago-Iwoye, Nigeria

    Sakinat Oluwabukonla Folorunso

  4. Life on Land Research Group, Landmark University SDG 15, Omu Aran, Nigeria

    Kazeem Moses Abiodun

  5. Quality Education Group, Landmark University SDG 4, Omu Aran, Nigeria

    Kazeem Moses Abiodun & Emmanuel Abidemi Adeniyi

  6. Industry, Innovation and Infrastructure Research Group, Landmark University SDG 9, Omu Aran, Nigeria

    Emmanuel Abidemi Adeniyi

Authors
  1. Joseph Bamidele Awotunde
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  2. Kazeem Moses Abiodun
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  3. Emmanuel Abidemi Adeniyi
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  4. Sakinat Oluwabukonla Folorunso
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  5. Rasheed Gbenga Jimoh
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Correspondence to Joseph Bamidele Awotunde .

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Editors and Affiliations

  1. Østfold University College, Halden, Norway

    Sanjay Misra

  2. Covenant University, Ota, Nigeria

    Jonathan Oluranti

  3. Silesian University of Technology, Gliwice, Poland

    Robertas Damaševičius

  4. Kaunas University of Technology, Kaunas, Lithuania

    Rytis Maskeliunas

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Awotunde, J.B., Abiodun, K.M., Adeniyi, E.A., Folorunso, S.O., Jimoh, R.G. (2022). A Deep Learning-Based Intrusion Detection Technique for a Secured IoMT System. In: Misra, S., Oluranti, J., Damaševičius, R., Maskeliunas, R. (eds) Informatics and Intelligent Applications. ICIIA 2021. Communications in Computer and Information Science, vol 1547. Springer, Cham. https://doi.org/10.1007/978-3-030-95630-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-95630-1_4

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