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WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT

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Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence (IEA/AIE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13343))

Abstract

The fog-based attack detection systems can surpass cloud-based detection models due to their fast response and closeness to IoT devices. However, current fog-based detection systems are not lightweight to be compatible with ever-increasing IoMT big data and fog devices. To this end, a lightweight fog-based attack detection system is proposed in this study. Initially, a fog-based architecture is proposed for an IoMT system. Then the detection system is proposed which uses incremental ensemble learning, namely Weighted Hoeffding Tree Ensemble (WHTE), to detect multiple attacks in the network traffic of industrial IoMT system. The proposed model is compared to six incremental learning classifiers. Results of binary and multi-class classifications showed that the proposed system is lightweight enough to be used for the edge and fog devices in the IoMT system. The ensemble WHTE took trade-off between high accuracy and low complexity while maintained a high accuracy, low CPU time, and low memory usage.

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Acknowledgments

The authors sincerely thank Universiti Teknologi Malaysia (UTM) under Malaysia Research University Network (MRUN) Vot 4L876, for the completion of the research. This work was also partially supported/funded by the Ministry of Higher Education under the Fundamental Research Grant Scheme (FRGS/1/2018/ICT04/UTM/01/1) and Universiti Tenaga Nasional (UNITEN). The work and the contribution were also supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-SPEV-2022–2102). We are also grateful for the support of student Michal Dobrovolny in consultations regarding application aspects.

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Authors and Affiliations

  1. Malaysia-Japan International Institute of Technology (MJIIT), University Teknologi Malaysia, 54100, Kuala Lumpur, Malaysia

    Shilan S. Hameed, Ali Selamat & Ondrej Krejcar

  2. Directorate of Information Technology, Koya University, 44023, Koya, Iraq

    Shilan S. Hameed

  3. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru, 81310, Johor, Malaysia

    Ali Selamat & Shukor A. Razak

  4. Media and Games Center of Excellence (MagicX), Universiti Teknologi Malaysia, Skudai, 81310, Johor Bahru, Malaysia

    Ali Selamat

  5. Center for Basic and Applied Research, Faculty of Informatics and Management, University of Hradec Kralove, Rokitanskeho 62, Hradec Kralove, 50003, Czech Republic

    Ali Selamat & Ondrej Krejcar

  6. Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, 54100, Kuala Lumpur, Malaysia

    Liza Abdul Latiff

Authors
  1. Shilan S. Hameed
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  2. Ali Selamat
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  3. Liza Abdul Latiff
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  4. Shukor A. Razak
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  5. Ondrej Krejcar
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Corresponding author

Correspondence to Ali Selamat .

Editor information

Editors and Affiliations

  1. i-SOMET, Inc., Morioka-shi, Iwate, Japan

    Hamido Fujita

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Philippe Fournier-Viger

  3. Texas State University, San Marcos, TX, USA

    Moonis Ali

  4. Shanghai University of Finance and Economics, Shanghai, China

    Yinglin Wang

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Hameed, S.S., Selamat, A., Latiff, L.A., Razak, S.A., Krejcar, O. (2022). WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_41

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  • DOI: https://doi.org/10.1007/978-3-031-08530-7_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08529-1

  • Online ISBN: 978-3-031-08530-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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