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A Group-Based IoT Devices Classification Through Network Traffic Analysis Based on Machine Learning Approach

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Towards new e-Infrastructure and e-Services for Developing Countries (AFRICOMM 2020)

Abstract

With the rapid growth of the Internet of Things (IoT), the deployment, management, and identification of IoT devices that are connected to networks become a big concern. Consequently, they emerge as a prominent challenge either for mobile network operators who try to offer cost-effective services tailored to IoT market, or for network administrators who aim to identify as well reduce costs processing and optimize traffic management of connected environments. In order to achieve high accuracy in terms of reliability, loss and response time, new devices real time discovery techniques based on traffic characteristics are mandatory in favor of the identification of IoT connected devices.

Therefore, we design \(GBC_{-}IoT\), a group-based machine learning approach that enables to identify connected IoT devices through network traffic analysis. By leveraging well-known machine learning algorithms, \(GBC_{-}IoT\) framework identifies and categorizes IoT devices into three classes with an overall accuracy equals to roughly \(99.98\%\). Therefore, \(GBC_{-}IoT\) can efficiently identify IoT devices with less processing overhead compared to previous studies.

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Authors and Affiliations

  1. Université Cheikh Anta Diop, Dakar, Senegal

    Avewe Bassene & Bamba Gueye

Authors
  1. Avewe Bassene
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  2. Bamba Gueye
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Corresponding author

Correspondence to Avewe Bassene .

Editor information

Editors and Affiliations

  1. ECE Paris, INSEEC U. Research Center, Paris, France

    Rafik Zitouni

  2. African Network Information Center, Ebène, Mauritius

    Amreesh Phokeer

  3. University of Cape Town, Rondebosch, South Africa

    Josiah Chavula

  4. (org. 984648855), Simula Research Laboratory, Fornebu, Norway

    Ahmed Elmokashfi

  5. CMU-Africa, Kigali, Rwanda

    Assane Gueye

  6. Universite Moulay Ismail De Meknes, Meknes, Morocco

    Nabil Benamar

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Bassene, A., Gueye, B. (2021). A Group-Based IoT Devices Classification Through Network Traffic Analysis Based on Machine Learning Approach. In: Zitouni, R., Phokeer, A., Chavula, J., Elmokashfi, A., Gueye, A., Benamar, N. (eds) Towards new e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-030-70572-5_12

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  • DOI: https://doi.org/10.1007/978-3-030-70572-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70571-8

  • Online ISBN: 978-3-030-70572-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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