Enhanced Grey Risk Assessment Model for Support of Cloud Service Provider
The cloud computing environment provides easy-to-access service for private and confidential data. However, there are many threats to the leakage of private data. This paper focuses on investigating the vulnerabilities of cloud service providers (CSPs) from three risk aspects: management risks, law...
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IEEE
2020-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/9064787/ |
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author | Abdul Razaque Fathi Amsaad Salim Hariri Marwah Almasri Syed S. Rizvi Mohamed Ben Haj Frej |
author_facet | Abdul Razaque Fathi Amsaad Salim Hariri Marwah Almasri Syed S. Rizvi Mohamed Ben Haj Frej |
author_sort | Abdul Razaque |
collection | DOAJ |
description | The cloud computing environment provides easy-to-access service for private and confidential data. However, there are many threats to the leakage of private data. This paper focuses on investigating the vulnerabilities of cloud service providers (CSPs) from three risk aspects: management risks, law risks, and technology risks. Additionally, this paper presents a risk assessment model that is based on grey system theory (GST), defines indicators for assessment, and fully utilizes the analytic hierarchy process (AHP). Furthermore, we use the GST to predict the risk values by using the MATLAB platform. The GST determines the bottom evaluation sequence, while the AHP calculates the index weights. Based on the GST and the AHP, layer-based assessment values are determined for the bottom evaluation sequence and the index weights. The combination of AHP and GST aims to obtain systematic and structured well-defined procedures that are based on step-by-step processes. The AHP and GST methods are applied successfully to handle any risk assessment problem of the CSP. Furthermore, substantial challenges are encountered in determining the CSP's response time and identifying the most suitable solution out of a specified series of solutions. This issue has been handled using two additive features: the response time and the grey incidence. The final risk values are calculated and can be used for prediction by utilizing the enhanced grey model (EGM) (1,1), which reduces the prediction error by providing direct forecast to avoid the iterative prediction shortcoming of standard GM (1,1). Thus, EGM (1,1) helps maintain the reliability on a larger scale despite utilizing more prediction periods. Based on the experimental results, we evaluate the validity, accuracy, and response time of the proposed approach. The simulation experiments were conducted to validate the suitability of the proposed model. The simulation results demonstrate that our risk assessment model contributes to reducing deviation to support CSPs with the three adopted models. |
first_indexed | 2024-12-14T19:15:25Z |
format | Article |
id | doaj.art-18765da83a324a56a50a0c4ced0f0653 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-12-14T19:15:25Z |
publishDate | 2020-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj.art-18765da83a324a56a50a0c4ced0f06532022-12-21T22:50:37ZengIEEEIEEE Access2169-35362020-01-018808128082610.1109/ACCESS.2020.29877359064787Enhanced Grey Risk Assessment Model for Support of Cloud Service ProviderAbdul Razaque0https://orcid.org/0000-0002-6890-0799Fathi Amsaad1Salim Hariri2Marwah Almasri3Syed S. Rizvi4Mohamed Ben Haj Frej5https://orcid.org/0000-0001-5315-1898Department of Computer Engineering and Telecommunication, International IT University, Almaty, KazakhstanDepartment of Information Security and Applied Computing (SISAC), Eastern Michigan University (EMU), Ypsilanti, MI, USADepartment of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ, USADepartment of Computer Science, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaDepartment of Information Sciences and Technology, The Pennsylvania State University, Altoona, PA, USADepartment of Computer Science, University of Bridgeport, Bridgeport, CT, USAThe cloud computing environment provides easy-to-access service for private and confidential data. However, there are many threats to the leakage of private data. This paper focuses on investigating the vulnerabilities of cloud service providers (CSPs) from three risk aspects: management risks, law risks, and technology risks. Additionally, this paper presents a risk assessment model that is based on grey system theory (GST), defines indicators for assessment, and fully utilizes the analytic hierarchy process (AHP). Furthermore, we use the GST to predict the risk values by using the MATLAB platform. The GST determines the bottom evaluation sequence, while the AHP calculates the index weights. Based on the GST and the AHP, layer-based assessment values are determined for the bottom evaluation sequence and the index weights. The combination of AHP and GST aims to obtain systematic and structured well-defined procedures that are based on step-by-step processes. The AHP and GST methods are applied successfully to handle any risk assessment problem of the CSP. Furthermore, substantial challenges are encountered in determining the CSP's response time and identifying the most suitable solution out of a specified series of solutions. This issue has been handled using two additive features: the response time and the grey incidence. The final risk values are calculated and can be used for prediction by utilizing the enhanced grey model (EGM) (1,1), which reduces the prediction error by providing direct forecast to avoid the iterative prediction shortcoming of standard GM (1,1). Thus, EGM (1,1) helps maintain the reliability on a larger scale despite utilizing more prediction periods. Based on the experimental results, we evaluate the validity, accuracy, and response time of the proposed approach. The simulation experiments were conducted to validate the suitability of the proposed model. The simulation results demonstrate that our risk assessment model contributes to reducing deviation to support CSPs with the three adopted models.https://ieeexplore.ieee.org/document/9064787/Analytic hierarchy processcloud service providerdeviation reductiongrey modelrisk assessment |
spellingShingle | Abdul Razaque Fathi Amsaad Salim Hariri Marwah Almasri Syed S. Rizvi Mohamed Ben Haj Frej Enhanced Grey Risk Assessment Model for Support of Cloud Service Provider IEEE Access Analytic hierarchy process cloud service provider deviation reduction grey model risk assessment |
title | Enhanced Grey Risk Assessment Model for Support of Cloud Service Provider |
title_full | Enhanced Grey Risk Assessment Model for Support of Cloud Service Provider |
title_fullStr | Enhanced Grey Risk Assessment Model for Support of Cloud Service Provider |
title_full_unstemmed | Enhanced Grey Risk Assessment Model for Support of Cloud Service Provider |
title_short | Enhanced Grey Risk Assessment Model for Support of Cloud Service Provider |
title_sort | enhanced grey risk assessment model for support of cloud service provider |
topic | Analytic hierarchy process cloud service provider deviation reduction grey model risk assessment |
url | https://ieeexplore.ieee.org/document/9064787/ |
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