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An Efficient Fog-Based Attack Detection Using Ensemble of MOA-WMA for Internet of Medical Things

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Innovative Systems for Intelligent Health Informatics (IRICT 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 72))

Abstract

Smart healthcare applications such as smart fitness, smart watches, and elderly remote monitoring devices have reduced the load on traditional healthcare organizations and led to healthier lifestyles. Nevertheless, these devices are under high risk of zero-day attacks because of their fragile nature and network characteristics. Furthermore, few companies developing these devices take care of security requirements. Attack detection using machine learning techniques such as ensemble learning has been adopted to IoT as a second line of defense. However, most of the proposed approaches are either heavy to implement at Internet of Medical Things (IoMT) devices or are cloud based which lead to delay in the detection of attacks. Also, these detections are centralized which are less compatible with IoT. In this study, an ensemble attack detection method is proposed for the detection of stream data attack at fog layer. The base classifiers are stream and incremental based algorithms, which are compatible with IoMT nature and fog devices. A weighted majority algorithm is followed to obtain best accuracy with reduced latency. The results demonstrated that the proposed model is effective for attack detection at fog layer, while it gives better accuracy, higher detection rate and lower false positive rate with average detection time.

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Author information

Authors and Affiliations

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

    Shilan S. Hameed & Wan Haslina Hassan

  2. Directorate of Information Technology, Koya University, Kurdistan Region-F.R., 44023, Koya, Iraq

    Shilan S. Hameed

  3. Fakulti Teknologi and Informatik Razak, University Teknologi Malaysia, 54100, Kuala Lumpur, Malaysia

    Liza Abdul Latiff

Authors
  1. Shilan S. Hameed
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  2. Wan Haslina Hassan
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  3. Liza Abdul Latiff
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Correspondence to Shilan S. Hameed .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. School of Computing, Information Systems Department, Universiti Utara Malaysia, Sintok, Malaysia

    Fathey Mohammed

  3. Sanaa’a Community College, Sana'a, Yemen

    Abdulaziz Al-Nahari

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Hameed, S.S., Hassan, W.H., Latiff, L.A. (2021). An Efficient Fog-Based Attack Detection Using Ensemble of MOA-WMA for Internet of Medical Things. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_70

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