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

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 fo...

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Main Authors: Hameed, Shilan S., Selamat, Ali, Abdul Latiff, Liza, A. Razak, Shukor, Krejcar, Ondrej
Format: Conference or Workshop Item
Published: 2022
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/99638/
http://dx.doi.org/10.1007/978-3-031-08530-7_41
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id my.utm.99638
record_format eprints
spelling my.utm.996382023-03-08T03:59:08Z http://eprints.utm.my/id/eprint/99638/ WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT Hameed, Shilan S. Selamat, Ali Abdul Latiff, Liza A. Razak, Shukor Krejcar, Ondrej T Technology (General) 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. 2022 Conference or Workshop Item PeerReviewed Hameed, Shilan S. and Selamat, Ali and Abdul Latiff, Liza and A. Razak, Shukor and Krejcar, Ondrej (2022) WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT. In: 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, 19 - 22 July 2022, Kitakyushu, Japan. http://dx.doi.org/10.1007/978-3-031-08530-7_41
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology (General)
spellingShingle T Technology (General)
Hameed, Shilan S.
Selamat, Ali
Abdul Latiff, Liza
A. Razak, Shukor
Krejcar, Ondrej
WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT
description 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.
format Conference or Workshop Item
author Hameed, Shilan S.
Selamat, Ali
Abdul Latiff, Liza
A. Razak, Shukor
Krejcar, Ondrej
author_facet Hameed, Shilan S.
Selamat, Ali
Abdul Latiff, Liza
A. Razak, Shukor
Krejcar, Ondrej
author_sort Hameed, Shilan S.
title WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT
title_short WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT
title_full WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT
title_fullStr WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT
title_full_unstemmed WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT
title_sort whte: weighted hoeffding tree ensemble for network attack detection at fog-iomt
publishDate 2022
url http://eprints.utm.my/id/eprint/99638/
http://dx.doi.org/10.1007/978-3-031-08530-7_41
_version_ 1761616360064942080
score 13.211869

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