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A Hybrid Deep Learning Scheme for Intrusion Detection in the Internet of Things

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Intelligent Systems and Pattern Recognition (ISPR 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1941))

  • 168 Accesses

Abstract

The Internet of Things (IoT) is the connection of smart devices and objects to the internet, allowing them to share and analyze data, communicate with each other, and be controlled remotely. Several IoT devices are designed to collect, process, and store confidential data in order to perform their intended function. This information can be sensitive such as location, health, military, financial information, and biometric data. The efficient implementation of IoT networks has become increasingly reliant on security. In IoT networks, several researchers used intrusion detection systems (IDS) for the identification of cyberattacks where machine learning (ML) and deep learning (DL) are significant components. The existing IDS still needs improvements for the detection of multiclass detection to identify each category of attack separately. To improve the detection performance of IDS, this study proposes a hybrid scheme of convolutional neural networks (CNN) and gated recurrent units (GRU). The proposed hybrid scheme integrates two CNN layers and three GRU layers. The proposed scheme was assessed using the IoTID20 dataset.

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Author information

Authors and Affiliations

  1. School of computing Engineering and Physical Sciences, University of the West of Scotland, Paisley, PA1 2BE, UK

    Asadullah Momand & Naeem Ramzan

  2. School of Computing Edinburgh Napier University, Edinburgh, EH10 5DT, UK

    Sana Ullah Jan

Authors
  1. Asadullah Momand
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  2. Sana Ullah Jan
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  3. Naeem Ramzan
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Corresponding author

Correspondence to Sana Ullah Jan .

Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Akram Bennour

  2. Sharjah University, Sharjah, United Arab Emirates

    Ahmed Bouridane

  3. University of Toulouse, Toulouse, France

    Lotfi Chaari

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Momand, A., Jan, S.U., Ramzan, N. (2024). A Hybrid Deep Learning Scheme for Intrusion Detection in the Internet of Things. In: Bennour, A., Bouridane, A., Chaari, L. (eds) Intelligent Systems and Pattern Recognition. ISPR 2023. Communications in Computer and Information Science, vol 1941. Springer, Cham. https://doi.org/10.1007/978-3-031-46338-9_21

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  • DOI: https://doi.org/10.1007/978-3-031-46338-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46337-2

  • Online ISBN: 978-3-031-46338-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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