Machine Learning-Enhanced Security for Multi-Cloud Oracle Database Deployments
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https://doi.org/10.55662/JST.2024.5301Downloads
Abstract
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.
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
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Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.