Secure Cloud Infrastructures: A Machine Learning Perspective

Abstract

A computing paradigm known as "cloud computing" offers end users scalable, measurable, and on-demand services. Nowadays, practically all businesses rely heavily on computing technology for infrastructure, development platforms, cost savings, data processing, data analytics, and other purposes. This work focuses on using machine learning (ML) approaches to advance industry standards for cloud computing security. The goals of the ML study are met by the identification of these ten crucial features. Additionally, the goal of this research is to create a workable plan for anticipating machine learning use in an Industrial Cloud context with respect to privacy and trust concerns. By using F1 scores, R.O.C. curves, confusion matrices, and validation matrices of precision, accuracy, and recall values, the effectiveness of the used models is evaluated. The findings showed that the X.G.B. model performed better than all other models for each matrix, with 98.60% accuracy, 98.70% precision, 98.70% recall, and 98.60% F1 score. This study demonstrates how machine learning techniques may improve cloud computing security for many sectors. It highlights how important it is to carry out ongoing research and development in order to produce more sophisticated and effective cloud computing security solutions. By investigating diverse machine learning models, algorithms, and tactics, the study advances the creation of sophisticated security frameworks customised for cloud-based systems. The goal of this interdisciplinary approach is to offer solutions and insights for maintaining strong security measures in the quickly changing cloud computing environment.