Staff View: iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems

iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems

To meet the demand of the world's largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However...

Full description

Saved in:

Bibliographic Details
Main Authors:	Azad, Saiful, Mahmud, Mufti, Kamal Zuhairi, Zamli, Kaiser, M. Shamim, Jahan, Sobhana, Razzaque, Md Abdur
Format:	Article
Language:	English
Published:	Elsevier Ltd 2024
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/38967/1/iBUST_An%20intelligent%20behavioural%20trust%20model%20for%20securing%20industrial%20cyber-physical%20systems.pdf http://umpir.ump.edu.my/id/eprint/38967/ https://doi.org/10.1016/j.eswa.2023.121676 https://doi.org/10.1016/j.eswa.2023.121676
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.ump.umpir.38967
record_format	eprints
spelling	my.ump.umpir.389672023-10-31T04:25:30Z http://umpir.ump.edu.my/id/eprint/38967/ iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems Azad, Saiful Mahmud, Mufti Kamal Zuhairi, Zamli Kaiser, M. Shamim Jahan, Sobhana Razzaque, Md Abdur QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) To meet the demand of the world's largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these networks are comprised of various heterogeneous devices with varying computational power and memory capabilities. As a result, many secure communication protocols – that demand considerably high computational power and memory – can not be verbatim employed on these networks, and thereby, leaving them more vulnerable to security threats and attacks over conventional networks. These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. However, among the existing ML-based solutions in detecting attack patterns, many of them are computationally expensive, require a long training time, and a considerably large amount of training data—which are seldom available. An aid to this is the association rule learning (ARL) paradigm, whose models are computationally inexpensive and do not require a long training time. Therefore, this paper proposes an ARL-based intelligent Behavioural Trust Model (iBUST) for securing the CPPS. For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. Afterwards, the trust class of an object is identified employing the Naïve Bayes classifier. This proposed model is evaluated on a trust evolution-supported experimental environment along with other compared models taking a benchmark dataset into consideration, where it outperforms its counterparts. Elsevier Ltd 2024-03-15 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/38967/1/iBUST_An%20intelligent%20behavioural%20trust%20model%20for%20securing%20industrial%20cyber-physical%20systems.pdf Azad, Saiful and Mahmud, Mufti and Kamal Zuhairi, Zamli and Kaiser, M. Shamim and Jahan, Sobhana and Razzaque, Md Abdur (2024) iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems. Expert Systems with Applications, 238 (121676). pp. 1-16. ISSN 0957-4174. (Published) https://doi.org/10.1016/j.eswa.2023.121676 https://doi.org/10.1016/j.eswa.2023.121676
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
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 Mahmud, Mufti Kamal Zuhairi, Zamli Kaiser, M. Shamim Jahan, Sobhana Razzaque, Md Abdur iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems
description	To meet the demand of the world's largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these networks are comprised of various heterogeneous devices with varying computational power and memory capabilities. As a result, many secure communication protocols – that demand considerably high computational power and memory – can not be verbatim employed on these networks, and thereby, leaving them more vulnerable to security threats and attacks over conventional networks. These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. However, among the existing ML-based solutions in detecting attack patterns, many of them are computationally expensive, require a long training time, and a considerably large amount of training data—which are seldom available. An aid to this is the association rule learning (ARL) paradigm, whose models are computationally inexpensive and do not require a long training time. Therefore, this paper proposes an ARL-based intelligent Behavioural Trust Model (iBUST) for securing the CPPS. For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. Afterwards, the trust class of an object is identified employing the Naïve Bayes classifier. This proposed model is evaluated on a trust evolution-supported experimental environment along with other compared models taking a benchmark dataset into consideration, where it outperforms its counterparts.
format	Article
author	Azad, Saiful Mahmud, Mufti Kamal Zuhairi, Zamli Kaiser, M. Shamim Jahan, Sobhana Razzaque, Md Abdur
author_facet	Azad, Saiful Mahmud, Mufti Kamal Zuhairi, Zamli Kaiser, M. Shamim Jahan, Sobhana Razzaque, Md Abdur
author_sort	Azad, Saiful
title	iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems
title_short	iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems
title_full	iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems
title_fullStr	iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems
title_full_unstemmed	iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems
title_sort	ibust: an intelligent behavioural trust model for securing industrial cyber-physical systems
publisher	Elsevier Ltd
publishDate	2024
url	http://umpir.ump.edu.my/id/eprint/38967/1/iBUST_An%20intelligent%20behavioural%20trust%20model%20for%20securing%20industrial%20cyber-physical%20systems.pdf http://umpir.ump.edu.my/id/eprint/38967/ https://doi.org/10.1016/j.eswa.2023.121676 https://doi.org/10.1016/j.eswa.2023.121676
_version_	1822923798711631872
score	13.235362

iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems

Similar Items