THE ASSESSMENT OF RISKS IN PUBLIC CLOUD ENVIRONMENT BY DEVELOPING MULTINOMINAL LOGISTIC REGRESSION MODEL

Abstract

The public cloud information infrastructure is getting increasing complex and well-connected which, in parallel, increases the risks to the cloud assets. Hence it becomes the need of the hour to identify, analyze and mitigate the risks towards the information security systems and the data associated with it. In the current research work, a quantitative information security risk analysis methodology is proposed for public clouds. In the existing methodology, enterprises follow two approaches such as consolidated and detailed approach towards information security in which the former computes risk as single value for every asset, whereas, the threat-vulnerability pair responsible for a risk is identified and a risk factor corresponding to each security property for every asset is computed in latter approach. In the proposed methodology, the assets in the public cloud are studied in which the consolidated approach is used to find the risk factor of each of these assets. The assets are classified into three different risk zones namely high, medium and low risk zone. In case of high-risk assets, it becomes mandatory for the management to install high cost infrastructure to overcome the risk. For medium-risk assets, proper auditing and ensuring all policies, guidelines and procedures in place may reduce risks. For low-risk assets, there is no such need to invest much from the management.