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An efficient fog-based attack detection using ensemble of MOA-WMA for internet of medical things

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...

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Main Authors: Hameed, Shilan S., Hassan, Wan Haslina, Abdul Latiff, Liza
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2021
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/100285/
http://dx.doi.org/10.1007/978-3-030-70713-2_70
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spelling my.utm.1002852023-03-29T07:22:46Z http://eprints.utm.my/id/eprint/100285/ An efficient fog-based attack detection using ensemble of MOA-WMA for internet of medical things Hameed, Shilan S. Hassan, Wan Haslina Abdul Latiff, Liza T Technology (General) 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. Springer Science and Business Media Deutschland GmbH 2021 Article PeerReviewed Hameed, Shilan S. and Hassan, Wan Haslina and Abdul Latiff, Liza (2021) An efficient fog-based attack detection using ensemble of MOA-WMA for internet of medical things. Lecture Notes on Data Engineering and Communications Technologies, 72 (NA). pp. 774-785. ISSN 2367-4512 http://dx.doi.org/10.1007/978-3-030-70713-2_70 DOI : 10.1007/978-3-030-70713-2_70
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.
Hassan, Wan Haslina
Abdul Latiff, Liza
An efficient fog-based attack detection using ensemble of MOA-WMA for internet of medical things
description 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.
format Article
author Hameed, Shilan S.
Hassan, Wan Haslina
Abdul Latiff, Liza
author_facet Hameed, Shilan S.
Hassan, Wan Haslina
Abdul Latiff, Liza
author_sort Hameed, Shilan S.
title An efficient fog-based attack detection using ensemble of MOA-WMA for internet of medical things
title_short An efficient fog-based attack detection using ensemble of MOA-WMA for internet of medical things
title_full An efficient fog-based attack detection using ensemble of MOA-WMA for internet of medical things
title_fullStr An efficient fog-based attack detection using ensemble of MOA-WMA for internet of medical things
title_full_unstemmed An efficient fog-based attack detection using ensemble of MOA-WMA for internet of medical things
title_sort efficient fog-based attack detection using ensemble of moa-wma for internet of medical things
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2021
url http://eprints.utm.my/id/eprint/100285/
http://dx.doi.org/10.1007/978-3-030-70713-2_70
_version_ 1762392184870404096
score 13.211869

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