Machine Learning-Enhanced Security for Multi-Cloud Oracle Database Deployments - Murthy Shankeshi, Raghu (Journal of Science & Technology, 2024)
Multi-cloud Oracle Database deployments based on machine learning-enhanced security represents a sophisticated approach to reduce the emerging cyber threats at the same time assuring data integrity, confidentiality, and availability. The rapid adaptation of multi cloud strategies in organisation to optimise performance and scalability, complexity of securing Oracle Database instances across heterogeneous cloud environments increases. Traditional security mechanisms are not able to adapt to the dynamic nature of cloud infrastructure. This problem makes it necessary to integrate machine learning-driven threat detection, anomaly identification, and adaptive access control. This research paper aims to explore the application of advanced machine learning models which includes supervised, unsupervised, and reinforcement learning techniques, which is used to detect malicious activities, optimize database security configurations, and enhance compliance with regulatory frameworks.