Transforming Cloud Security with Machine Learning: An In-Depth Analysis of Modern Methodologies and Metrics
Saurabh Sharma, Pankaj Pali, Zohaib Hasan, Vishal Paranjape · International Journal of Innovative Research in Computer and Communication Engineering · 2023
The swift adoption of cloud computing has significantly altered the practices of data storage, management, and processing. The scalability, flexibility, and cost-effectiveness offered by cloud services have made them indispensable in modern digital infrastructure. However, this widespread integration has brought about numerous security challenges, necessitating advanced measures to protect sensitive data and resources. Traditional security approaches often prove inadequate for the dynamic and complex nature of cloud environments, requiring the implementation of more sophisticated solutions. In this context, machine learning (ML) has become a crucial tool in cybersecurity, enabling real-time detection, prediction, and response to threats. The ability of ML algorithms to analyze large datasets allows for the identification of patterns and anomalies that are beyond the scope of manual detection by human analysts. Consequently, a range of ML-based strategies has been developed to enhance cloud security. This paper provides a comprehensive survey of modern ML methodologies for strengthening cloud security. It examines the various techniques employed, assesses their effectiveness in different scenarios, and addresses the challenges associated with their deployment. The proposed method demonstrates high accuracy, achieving a performance accuracy of 97.5%. Additionally, it records a mean absolute error (MAE) of 0.476 and a root mean square error (RMSE) of 0.203, highlighting its robustness. Through a thorough analysis of current research and practices, this survey aims to elucidate the transformative potential of machine learning in cloud security and to identify areas for future exploration.