Enhancing Industrial Cyber Security, Focusing on Formulating a Practical Strategy for Making Predictions through Machine Learning Tools in Cloud Computing Environment

Abstract

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study’s objectives. This study identifies gaps in utilizing ML techniques in cloud cyber security. Moreover, this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of precision, accuracy, and recall values, as well as F1 scores, R.O.C. curves, and confusion matrices. The results demonstrated that the X.G.B. model outperformed, in terms of all the matrices, with an accuracy of 97.50%, a precision of 97.60%, a recall value of 97.60%, and an F1 score of 97.50%. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.