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A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier

Healthcare 4.0 has revolutionized the delivery of healthcare services during the last years. Facilitated by it, many hospitals have migrated to the paradigm of being smart. Smartization of hospitals has reduced healthcare costs while providing improved and reliable healthcare services. Thanks to the...

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Main Authors: Azad, Saiful, Amin Salem, Saleh Bllagdham, Mahmud, Mufti, Kaiser, M. Shamim, Miah, Md Saef Ullah
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/42379/1/A%20behavioral%20trust%20model%20for%20internet%20of%20healthcare.pdf
http://umpir.ump.edu.my/id/eprint/42379/2/A%20behavioral%20trust%20model%20for%20internet%20of%20healthcare%20things%20using%20an%20improved%20fp-growth%20algorithm%20and%20na%C3%AFve%20bayes%20classifier_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42379/
https://doi.org/10.1109/STI53101.2021.9732588
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spelling my.ump.umpir.423792024-10-30T04:37:50Z http://umpir.ump.edu.my/id/eprint/42379/ A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier Azad, Saiful Amin Salem, Saleh Bllagdham Mahmud, Mufti Kaiser, M. Shamim Miah, Md Saef Ullah QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Healthcare 4.0 has revolutionized the delivery of healthcare services during the last years. Facilitated by it, many hospitals have migrated to the paradigm of being smart. Smartization of hospitals has reduced healthcare costs while providing improved and reliable healthcare services. Thanks to the Internet of Healthcare Things (IoHT) based healthcare delivery frameworks, integration of many heterogeneous devices with varying computational capabilities has been possible. However, this introduced a number of security concerns as many secure communication protocols for traditional networks can not be verbatim employed on these frameworks. To ensure security, the threats can largely be tackled by employing a Trust Management Model (TMM) which will critically evaluate the behavior or activity pattern of the nodes and block the untrusted ones. Towards securing these frameworks through an intelligent TMM, this work proposes a machine learning based Behavioral Trust Model (BTM), where an improved Frequent Pattern Growth (iFP-Growth) algorithm is proposed and applied to extract behavioral signatures of various trust classes. Later, these behavioral signatures are utilized in classifying incoming communication requests to either trustworthy and untrustworthy (trust) class using the Naïve Bayes classifier. The proposed model is tested on a benchmark dataset along with other similar existing models, where the proposed BMT outperforms the existing TMMs. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42379/1/A%20behavioral%20trust%20model%20for%20internet%20of%20healthcare.pdf pdf en http://umpir.ump.edu.my/id/eprint/42379/2/A%20behavioral%20trust%20model%20for%20internet%20of%20healthcare%20things%20using%20an%20improved%20fp-growth%20algorithm%20and%20na%C3%AFve%20bayes%20classifier_ABS.pdf Azad, Saiful and Amin Salem, Saleh Bllagdham and Mahmud, Mufti and Kaiser, M. Shamim and Miah, Md Saef Ullah (2021) A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier. In: 2021 3rd International Conference on Sustainable Technologies for Industry 4.0, STI 2021. 3rd International Conference on Sustainable Technologies for Industry 4.0, STI 2021 , 18 - 19 December 2021 , Dhaka. pp. 1-6.. ISBN 978-166540009-1 (Published) https://doi.org/10.1109/STI53101.2021.9732588
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Azad, Saiful
Amin Salem, Saleh Bllagdham
Mahmud, Mufti
Kaiser, M. Shamim
Miah, Md Saef Ullah
A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier
description Healthcare 4.0 has revolutionized the delivery of healthcare services during the last years. Facilitated by it, many hospitals have migrated to the paradigm of being smart. Smartization of hospitals has reduced healthcare costs while providing improved and reliable healthcare services. Thanks to the Internet of Healthcare Things (IoHT) based healthcare delivery frameworks, integration of many heterogeneous devices with varying computational capabilities has been possible. However, this introduced a number of security concerns as many secure communication protocols for traditional networks can not be verbatim employed on these frameworks. To ensure security, the threats can largely be tackled by employing a Trust Management Model (TMM) which will critically evaluate the behavior or activity pattern of the nodes and block the untrusted ones. Towards securing these frameworks through an intelligent TMM, this work proposes a machine learning based Behavioral Trust Model (BTM), where an improved Frequent Pattern Growth (iFP-Growth) algorithm is proposed and applied to extract behavioral signatures of various trust classes. Later, these behavioral signatures are utilized in classifying incoming communication requests to either trustworthy and untrustworthy (trust) class using the Naïve Bayes classifier. The proposed model is tested on a benchmark dataset along with other similar existing models, where the proposed BMT outperforms the existing TMMs.
format Conference or Workshop Item
author Azad, Saiful
Amin Salem, Saleh Bllagdham
Mahmud, Mufti
Kaiser, M. Shamim
Miah, Md Saef Ullah
author_facet Azad, Saiful
Amin Salem, Saleh Bllagdham
Mahmud, Mufti
Kaiser, M. Shamim
Miah, Md Saef Ullah
author_sort Azad, Saiful
title A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier
title_short A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier
title_full A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier
title_fullStr A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier
title_full_unstemmed A behavioral trust model for internet of healthcare things using an improved FP-growth algorithm and Naïve Bayes classifier
title_sort behavioral trust model for internet of healthcare things using an improved fp-growth algorithm and naïve bayes classifier
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url http://umpir.ump.edu.my/id/eprint/42379/1/A%20behavioral%20trust%20model%20for%20internet%20of%20healthcare.pdf
http://umpir.ump.edu.my/id/eprint/42379/2/A%20behavioral%20trust%20model%20for%20internet%20of%20healthcare%20things%20using%20an%20improved%20fp-growth%20algorithm%20and%20na%C3%AFve%20bayes%20classifier_ABS.pdf
http://umpir.ump.edu.my/id/eprint/42379/
https://doi.org/10.1109/STI53101.2021.9732588
_version_ 1822924726807298048
score 13.235362

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