Access management based on deep reinforcement learning for effective cloud storage security.

Securing sensitive data in cloud storage systems is crucial with the increasing sophistication of cyber-attacks in today's digital ecosystem. The rising need to protect cloud storage systems from illegal access, data breaches, and new cyber threats makes the research necessary. Because of the difficulty in keeping up with the ever-changing nature of security threats and user behaviour, innovative, flexible, and dynamic ways to access management have had to be developed. The ever-changing nature of modern threats and the requirement for constant monitoring and instantaneous responses provide difficulties for cloud storage security. The present research presents Deep Reinforcement Learning-Enhanced Adaptive Access Control (DRL-AAC), which makes access control decisions that are dynamic and aware of their context using modern reinforcement learning algorithms. It has been used to design and implement a Deep Reinforcement Learning (DRL-AAC) enhanced adaptive access control system for dynamically managing cloud storage. The system should be able to make access determinations in real-time, adapting to changing security threats and user habits. DRL-AAC uses user profiling to analyze and detect abnormalities and unauthorized access based on user behaviour patterns. DRL-AAC has wide-ranging potential uses, from consumer to enterprise to government cloud storage. Because of its intelligence and flexibility, it may be used to protect data in various settings. This research provides a comprehensive simulation analysis to prove the efficiency of DRL-AAC. The experimental findings demonstrate that the suggested DRL-AAC model increases the computational intensity analysis ratio of 96.2%, level of data privacy of 97.8%, adaptability ratio of 95.9%, resource allocation efficiency of 97.5% efficiency, scalability ratio of 98.8% compared to other existing models. The purpose of the research is to demonstrate the efficacy of DRL-AAC in doing its intended task of detecting and neutralizing potential security risks with as few false positives and negatives as possible. The paper aspires to verify DRL-AAC as a ground-breaking approach to bolstering cloud storage security. It can potentially improve cloud storage security by responding in real-time to new threats and user habits, protecting sensitive information on the cloud. The proposed work is evaluated by investigating its computational intensity, data privacy, adaptability, resource efficiency, and scalability. [ABSTRACT FROM AUTHOR]