Secure Cloud Auditability for Virtual Machines by Adaptive Characterization Using Machine Learning Methods

• Main Authors: Shesagiri Taminana, Lalitha Bhaskari, Arwa Mashat, Dragan Pamučar, Haritha Akkineni
• Format: Article
• Language: English
• Published: Regional Association for Security and crisis management, Belgrade, Serbia 2021-09-01
• Series: Operational Research in Engineering Sciences: Theory and Applications
• Subjects: Adaptive, Coefficient Based Regression, Selective Auditing, Adaptive Auditing
• Online Access: https://oresta.rabek.org/index.php/oresta/article/view/146

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.