Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods
With the Present days increasing demand for the higher performance with the application developers have started considering cloud computing and cloud-based data centres as one of the prime options for hosting the application. Number of parallel research outcomes have for making a data centre secure,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Regional Association for Security and crisis management, Belgrade, Serbia
2021-09-01
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| Series: | Operational Research in Engineering Sciences: Theory and Applications |
| Subjects: | |
| Online Access: | https://oresta.rabek.org/index.php/oresta/article/view/146 |
| _version_ | 1811191078851706880 |
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| author | Shesagiri Taminana Lalitha Bhaskari Arwa Mashat Dragan Pamučar Haritha Akkineni |
| author_facet | Shesagiri Taminana Lalitha Bhaskari Arwa Mashat Dragan Pamučar Haritha Akkineni |
| author_sort | Shesagiri Taminana |
| collection | DOAJ |
| description | With the Present days increasing demand for the higher performance with the application developers have started considering cloud computing and cloud-based data centres as one of the prime options for hosting the application. Number of parallel research outcomes have for making a data centre secure, the data centre infrastructure must go through the auditing process. During the auditing process, auditors can access VMs, applications and data deployed on the virtual machines. The downside of the data in the VMs can be highly sensitive and during the process of audits, it is highly complex to permits based on the requests and can increase the total time taken to complete the tasks. Henceforth, the demand for the selective and adaptive auditing is the need of the current research. However, these outcomes are criticised for higher time complexity and less accuracy. Thus, this work proposes a predictive method for analysing the characteristics of the VM applications and the characteristics from the auditors and finally granting the access to the virtual machine by building a predictive regression model. The proposed algorithm demonstrates 50% of less time complexity to the other parallel research for making the cloud-based application development industry a safer and faster place. |
| first_indexed | 2024-04-11T15:00:51Z |
| format | Article |
| id | doaj.art-9f4f563320de4dc49fa9b04db47185d1 |
| institution | Directory Open Access Journal |
| issn | 2620-1607 2620-1747 |
| language | English |
| last_indexed | 2024-04-11T15:00:51Z |
| publishDate | 2021-09-01 |
| publisher | Regional Association for Security and crisis management, Belgrade, Serbia |
| record_format | Article |
| series | Operational Research in Engineering Sciences: Theory and Applications |
| spelling | doaj.art-9f4f563320de4dc49fa9b04db47185d12022-12-22T04:17:00ZengRegional Association for Security and crisis management, Belgrade, SerbiaOperational Research in Engineering Sciences: Theory and Applications2620-16072620-17472021-09-014310.31181/oresta20402059tSecure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning MethodsShesagiri Taminana0Lalitha Bhaskari1Arwa Mashat2Dragan Pamučar3Haritha Akkineni4Department of Computer Science & Systems Engineering, Andhra University College of Engineering (A), Andhra University, Visakhapatnam, IndiaDepartment of Computer Science & Systems Engineering, Andhra University College of Engineering (A), Andhra University, Visakhapatnam, IndiaDepartment of Information Systems, Faculty of Computing & Information Technology, King Abdulaziz University, Rabigh, Saudi ArabiaDepartment of Logistics, Milatary Academy, University of Defence in Belgrade, SerbiaDepartment of Information Technology, PVP Siddhartha Institute of Technology, Vijayawada, IndiaWith the Present days increasing demand for the higher performance with the application developers have started considering cloud computing and cloud-based data centres as one of the prime options for hosting the application. Number of parallel research outcomes have for making a data centre secure, the data centre infrastructure must go through the auditing process. During the auditing process, auditors can access VMs, applications and data deployed on the virtual machines. The downside of the data in the VMs can be highly sensitive and during the process of audits, it is highly complex to permits based on the requests and can increase the total time taken to complete the tasks. Henceforth, the demand for the selective and adaptive auditing is the need of the current research. However, these outcomes are criticised for higher time complexity and less accuracy. Thus, this work proposes a predictive method for analysing the characteristics of the VM applications and the characteristics from the auditors and finally granting the access to the virtual machine by building a predictive regression model. The proposed algorithm demonstrates 50% of less time complexity to the other parallel research for making the cloud-based application development industry a safer and faster place.https://oresta.rabek.org/index.php/oresta/article/view/146Adaptive, Coefficient Based Regression, Selective Auditing, Adaptive Auditing |
| spellingShingle | Shesagiri Taminana Lalitha Bhaskari Arwa Mashat Dragan Pamučar Haritha Akkineni Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods Operational Research in Engineering Sciences: Theory and Applications Adaptive, Coefficient Based Regression, Selective Auditing, Adaptive Auditing |
| title | Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods |
| title_full | Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods |
| title_fullStr | Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods |
| title_full_unstemmed | Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods |
| title_short | Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods |
| title_sort | secure cloud auditability for virtual machines by adaptive characterization using machine learning methods |
| topic | Adaptive, Coefficient Based Regression, Selective Auditing, Adaptive Auditing |
| url | https://oresta.rabek.org/index.php/oresta/article/view/146 |
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