Identification of Security Issues and Finding their Solution in Cloud Computing

Abstract

The advent of Cloud Computing has simplified on-demand access to IT services including data storage and administration. In addition, it seeks to secure systems and make them functional. With these benefits, there are significant security constraints for cloud providers. When it comes to cloud computing, one of the biggest obstacles is ensuring the safety of data and services. Considering this, several solutions have been put into place to boost cloud security by keeping an eye on everything from resources to services to networks to identify and stop intrusions as soon as they occur. The term "Intrusion Detection System" (IDS) refers to an improved technique used to regulate network traffic and identify abnormal activity. This paper presents the identification of Security Issues and Finding their Solution in Cloud Computing using machine learning techniques including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Multi-Layer Protocol (MLP). This model is trained and evaluated using NSL-KDD dataset. The experimental findings show the highest accuracy of 93.5% with the use of SVM model. As a result, the achieved results demonstrate strong performance concerning Accuracy, Precision, Recall, and F1-Score when compared to recent studies.