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Ensuring Intrusion Detection for IoT Services Through an Improved CNN

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Abstract

Internet of Things (IoT) devices are challenging to manage information security due to some factors such as processing capability, exponential growth in homes, and their low energy consumption which raises the risk of cyberattacks. One way to avoid cyberattacks is using an intrusion detection system that is able to recognize assaults while warning users so that appropriate countermeasures can be taken. Several deep learning and machine learning techniques have been used in the past to try to detect new assaults; however, these attempts have not been successful. In order to optimize IoT devices, in this study, we make a classification of network assaults using the convolutional neural network models mCNN and CNN. This study aims to assess the application of deep learning intrusion detection systems for IoT devices. The NF-UNSW-NB15-v2 dataset was used in this experiment to train the neural network. The network stream’s data were transformed into RGB images, which the neural network was trained on. The mCNN model outperformed the CNN model when compared to the proposed one for classifying network attacks. In addition, both networks perform better in most categories, with the exception of network attack detection, where the CNN performed worse than the suggested mCNN model.

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Notes

  1. https://rdm.uq.edu.au/files/8c6e2a00-ef9c-11ed-827d-e762de186848.

  2. https://research.unsw.edu.au/projects/unsw-nb15-dataset.

  3. https://www.vast.ai/.

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Funding

The authors declare that no fund was received for this study.

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Authors and Affiliations

  1. First Technical University Ibadan, Ibadan, Nigeria

    Sunday Adeola Ajagbe

  2. University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde

  3. Universidad Distrital Francisco Jose de Caldas, Bogotá, Colombia

    Hector Florez

Authors
  1. Sunday Adeola Ajagbe
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  2. Joseph Bamidele Awotunde
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  3. Hector Florez
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Contributions

SAA: conceptualization, writhing of original draft, validation, methodology, and coding. JBA: project administration, methodology, resource management, editing, and supervision. HF: resource management, review, editing, and supervision.

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Correspondence to Sunday Adeola Ajagbe.

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This article is part of the topical collection “Emerging Technologies in Applied Informatics” guest edited by Hector Florez and Marcelo Leon.

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Ajagbe, S.A., Awotunde, J.B. & Florez, H. Ensuring Intrusion Detection for IoT Services Through an Improved CNN. SN COMPUT. SCI. 5, 49 (2024). https://doi.org/10.1007/s42979-023-02448-y

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