Enhancing Security in Cloud Computing through Machine Learning Techniques

Thesis Abstract

Abstract


Cloud computing has become an integral part of modern IT infrastructure, offering scalability, flexibility, and cost-efficiency. However, the security of data and applications in the cloud remains a significant concern. This research aims to enhance security in cloud computing through the application of machine learning techniques. The study begins with a comprehensive review of cloud computing security, including an analysis of current challenges and best practices. Subsequently, a detailed methodology for data collection, preprocessing, and the selection of machine learning algorithms is presented. The implementation phase involves integrating machine learning models into the cloud environment and conducting experiments to evaluate their effectiveness in enhancing security. The results are analyzed, compared with existing methods, and visualized to demonstrate the improvements achieved. The thesis concludes with a summary of research contributions, implications, and recommendations for future work in the field of cloud security. This research is expected to provide valuable insights and practical solutions for addressing security concerns in cloud computing using machine learning.