Performance Evaluation of ML-Based AWS Security Evaluation Model for Cloud Computing

Abstract

As there is a great increase in demand for cloud services, the number of companies that provide cloud services have been increased in number. Cloud-based web services wholly depends on the service provides by the companies. Hence, it is very essential to choose the cloud service provider for obtaining best services. API consistently gives access to different cloud services. In this paper a quantifiable security evaluation system for various clouds is analyzed. A set of evaluation elements are present in the security evaluation model which compresses of various sectors via storage, computing, maintenance, application security, etc. The major aim of this paper is to deal with the issues related and exhibits the comparison of Amazon Work Space (AWS) and International Business Machines (IBM) cloud. Either of the cloud service platforms are compared with each other under some situations for respective instances. A machine learning-based CNN method is used for developing the proposed cloud computing. This mechanism calculates the score for each field and gives the total score as result in order to create one secured pattern. Dynamic security scanning is done for single or multiple clouds by using visual graphs and cloud users are guided for improvement of cloud operations, changing the configuration and to provide protection against vulnerabilities. This ultimately results in the enhancement of security in cloud resources.