Analyzing Enterprise Data Protection and Safety Risks in Cloud Computing Using Ensemble Learning

Abstract

Nowadays, cloud computing is a significant advancement in the information technology sector. Cloud computing manages and distributes large amounts of data and resources on the internet. In the IT sector, it is used significantly for accessing IT infrastructure via a computer network without necessitating local installations on individual devices. Protecting data security and privacy in cloud computing has become major issue. In this study we used ensemble machine learning algorithms for analysis of cloud computing data, our focus lies analysis of features that effect the data security and privacy threats in cloud computing. Data was gathered through survey online and physical survey method. The data gathering method involved various industries professional’s interactions. The survey dataset features consist of security challenges faced by organizations, such as organization size, industry sector, types of data managed, existing security measures, and prevalent security challenges. The primary focus was on evaluating the effectiveness of three machine learning classifiers: Decision Tree, Random Forest, and Support Vector Machine (SVM), which achieved 85.4%, 89.6%, and 88.2%, accuracies of respectively. To enhance predictive accuracy and robustness, an ensemble learning approach using a voting classifier was implemented, resulting in a significantly improved accuracy of 91.5%. The results show that ensemble learning outperforms individual classifiers in predicting cloud data security threats concerns. This paper highlights significant insights for academics and practitioners by implementing ensemble learning approaches that used for significantly strengthen cloud computing security measures, making them more robust to possible attackers.