Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage

Sultan Almansoori; Mohamed Alzaabi; Mohammed Alrayssi; Deepak Puthal; Joy Dutta; Aamna Al Shehhi

doi:10.1109/COMPSAC57700.2023.00188

Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage

Sultan Almansoori, Mohamed Alzaabi, Mohammed Alrayssi, Deepak Puthal, Joy Dutta, Aamna Al Shehhi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The focus of our paper is to explore the concept of blockchain, which is a digitalized, shared, and decentralized network where every transaction can be viewed by all users with access to the blockchain. Our main objective is to develop an access control mechanism for our private blockchain, which is implemented using Ethereum. This mechanism will use a machine learning-based security layer to regulate user access, allowing or denying access based on predetermined rules. Our ultimate goal is to create a mechanism that is highly secure, maintains data confidentiality, and improves user authenticity. To achieve the objectives, we construct a client-side model with an appealing graphical user interface that enables users to take advantage of the unique functionalities offered by the private blockchain. This paper will help to determine the most effective strategies for building a secure and reliable access control mechanism for blockchain networks.

Original language	British English
Title of host publication	Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023
Editors	Hossain Shahriar, Yuuichi Teranishi, Alfredo Cuzzocrea, Moushumi Sharmin, Dave Towey, AKM Jahangir Alam Majumder, Hiroki Kashiwazaki, Ji-Jiang Yang, Michiharu Takemoto, Nazmus Sakib, Ryohei Banno, Sheikh Iqbal Ahamed
Publisher	IEEE Computer Society
Pages	1243-1248
Number of pages	6
ISBN (Electronic)	9798350326970
DOIs	https://doi.org/10.1109/COMPSAC57700.2023.00188
State	Published - 2023
Event	47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023 - Hybrid, Torino, Italy Duration: 26 Jun 2023 → 30 Jun 2023

Publication series

Name	Proceedings - International Computer Software and Applications Conference
Volume	2023-June
ISSN (Print)	0730-3157

Conference

Conference	47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023
Country/Territory	Italy
City	Hybrid, Torino
Period	26/06/23 → 30/06/23

Keywords

Access Control
Blockchain
dApp
Ethereum
Machine Learning
Smart contracts

Access to Document

10.1109/COMPSAC57700.2023.00188

Cite this

Almansoori, S., Alzaabi, M., Alrayssi, M., Puthal, D., Dutta, J., & Shehhi, A. A. (2023). Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage. In H. Shahriar, Y. Teranishi, A. Cuzzocrea, M. Sharmin, D. Towey, AKM. J. A. Majumder, H. Kashiwazaki, J.-J. Yang, M. Takemoto, N. Sakib, R. Banno, & S. I. Ahamed (Eds.), Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023 (pp. 1243-1248). (Proceedings - International Computer Software and Applications Conference; Vol. 2023-June). IEEE Computer Society. https://doi.org/10.1109/COMPSAC57700.2023.00188

Almansoori, Sultan ; Alzaabi, Mohamed ; Alrayssi, Mohammed et al. / Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage. Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023. editor / Hossain Shahriar ; Yuuichi Teranishi ; Alfredo Cuzzocrea ; Moushumi Sharmin ; Dave Towey ; AKM Jahangir Alam Majumder ; Hiroki Kashiwazaki ; Ji-Jiang Yang ; Michiharu Takemoto ; Nazmus Sakib ; Ryohei Banno ; Sheikh Iqbal Ahamed. IEEE Computer Society, 2023. pp. 1243-1248 (Proceedings - International Computer Software and Applications Conference).

@inproceedings{01df94cb18724bcc95fd9bea18af1c50,

title = "Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage",

abstract = "The focus of our paper is to explore the concept of blockchain, which is a digitalized, shared, and decentralized network where every transaction can be viewed by all users with access to the blockchain. Our main objective is to develop an access control mechanism for our private blockchain, which is implemented using Ethereum. This mechanism will use a machine learning-based security layer to regulate user access, allowing or denying access based on predetermined rules. Our ultimate goal is to create a mechanism that is highly secure, maintains data confidentiality, and improves user authenticity. To achieve the objectives, we construct a client-side model with an appealing graphical user interface that enables users to take advantage of the unique functionalities offered by the private blockchain. This paper will help to determine the most effective strategies for building a secure and reliable access control mechanism for blockchain networks.",

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author = "Sultan Almansoori and Mohamed Alzaabi and Mohammed Alrayssi and Deepak Puthal and Joy Dutta and Shehhi, {Aamna Al}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023 ; Conference date: 26-06-2023 Through 30-06-2023",

year = "2023",

doi = "10.1109/COMPSAC57700.2023.00188",

language = "British English",

series = "Proceedings - International Computer Software and Applications Conference",

publisher = "IEEE Computer Society",

pages = "1243--1248",

editor = "Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and Majumder, {AKM Jahangir Alam} and Hiroki Kashiwazaki and Ji-Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Ahamed, {Sheikh Iqbal}",

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Almansoori, S, Alzaabi, M, Alrayssi, M, Puthal, D, Dutta, J & Shehhi, AA 2023, Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage. in H Shahriar, Y Teranishi, A Cuzzocrea, M Sharmin, D Towey, AKMJA Majumder, H Kashiwazaki, J-J Yang, M Takemoto, N Sakib, R Banno & SI Ahamed (eds), Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023. Proceedings - International Computer Software and Applications Conference, vol. 2023-June, IEEE Computer Society, pp. 1243-1248, 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, Hybrid, Torino, Italy, 26/06/23. https://doi.org/10.1109/COMPSAC57700.2023.00188

Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage. / Almansoori, Sultan; Alzaabi, Mohamed; Alrayssi, Mohammed et al.
Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023. ed. / Hossain Shahriar; Yuuichi Teranishi; Alfredo Cuzzocrea; Moushumi Sharmin; Dave Towey; AKM Jahangir Alam Majumder; Hiroki Kashiwazaki; Ji-Jiang Yang; Michiharu Takemoto; Nazmus Sakib; Ryohei Banno; Sheikh Iqbal Ahamed. IEEE Computer Society, 2023. p. 1243-1248 (Proceedings - International Computer Software and Applications Conference; Vol. 2023-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage

AU - Almansoori, Sultan

AU - Alzaabi, Mohamed

AU - Alrayssi, Mohammed

AU - Puthal, Deepak

AU - Dutta, Joy

AU - Shehhi, Aamna Al

PY - 2023

Y1 - 2023

N2 - The focus of our paper is to explore the concept of blockchain, which is a digitalized, shared, and decentralized network where every transaction can be viewed by all users with access to the blockchain. Our main objective is to develop an access control mechanism for our private blockchain, which is implemented using Ethereum. This mechanism will use a machine learning-based security layer to regulate user access, allowing or denying access based on predetermined rules. Our ultimate goal is to create a mechanism that is highly secure, maintains data confidentiality, and improves user authenticity. To achieve the objectives, we construct a client-side model with an appealing graphical user interface that enables users to take advantage of the unique functionalities offered by the private blockchain. This paper will help to determine the most effective strategies for building a secure and reliable access control mechanism for blockchain networks.

AB - The focus of our paper is to explore the concept of blockchain, which is a digitalized, shared, and decentralized network where every transaction can be viewed by all users with access to the blockchain. Our main objective is to develop an access control mechanism for our private blockchain, which is implemented using Ethereum. This mechanism will use a machine learning-based security layer to regulate user access, allowing or denying access based on predetermined rules. Our ultimate goal is to create a mechanism that is highly secure, maintains data confidentiality, and improves user authenticity. To achieve the objectives, we construct a client-side model with an appealing graphical user interface that enables users to take advantage of the unique functionalities offered by the private blockchain. This paper will help to determine the most effective strategies for building a secure and reliable access control mechanism for blockchain networks.

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Almansoori S, Alzaabi M, Alrayssi M, Puthal D, Dutta J , Shehhi AA. Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage. In Shahriar H, Teranishi Y, Cuzzocrea A, Sharmin M, Towey D, Majumder AKMJA, Kashiwazaki H, Yang JJ, Takemoto M, Sakib N, Banno R, Ahamed SI, editors, Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023. IEEE Computer Society. 2023. p. 1243-1248. (Proceedings - International Computer Software and Applications Conference). doi: 10.1109/COMPSAC57700.2023.00188

Machine Learning-based Adaptive Access Control Mechanism for Private Blockchain Storage

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