Modelling of smart risk assessment approach for cloud computing environment using AI & supervised machine learning algorithms

Abstract

Major backbone of today's competitive and upcoming market is definitely becoming Cloud computing & hence corporate utilize capabilities of cloud computing services. To improve security initiatives by cloud computing service or CRPs, novel types of tools and protocols finds themselves always in demand. In order to build comprehensive risk assessment methodology, extensive literature review was conducted to identify risk factors that may affect cloud computing adoption. In this context various risk factors were identified. After feature selection and identification of risk factors, utilized to select most effective features using linear regression algorithms. Then AI-ML techniques like Decision Tree (DTC), Randomizable Filter Classifier, k-star with RMSE method is used to analyse threats within CC environment. Experimental outcomes depicted that division of dataset to (95%-5%) provided best result out of every remaining partitioning and moreover put forth that DTC algorithm provided best outcomes out of entire data set used in experimental setups.