Modeling Cloud Computing Risk Assessment Using Machine Learning

摘要: Cloud computing emerged in recent years as the most significant developments modern computing. However, there are several risks involved using a cloud environment. To make decision of migrating to services is great need assess various involved. The main target risk assessment define appropriate controls for reducing or eliminating those risks. We conducted survey and formulated different associated factors simulate data from experiments. applied feature selection algorithms such Best-First, random search methods reduce attributes 3, 4, 9 attributes, which enabled us achieve better accuracy. Further, seven function approximation algorithms, namely Isotonic Regression, Randomizable Filter Classifier, Kstar, Extra Tree, IBK, multilayered perceptron, SMOreg were selected after experimenting with more than thirty algorithms. experimental results reveal that reduction prediction very efficient can high