Enhancing Blockchain-Based Access Control in Cloud Computing with Machine Learning Techniques

As cloud computing continues to expand, the importance of robust access control mechanisms to ensure security and privacy is paramount. Traditional access control systems often struggle with scalability, adaptability, and security vulnerabilities. This paper proposes a novel integration of blockchain technology with machine learning techniques to address these challenges. By harnessing the decentralized and immutable nature of blockchain and the predictive power of machine learning, our framework enhances the security and efficiency of access control systems in cloud environments. We developed a model that uses neural networks to automate and optimize access decisions and deployed it on a simulated cloud platform. Our results demonstrate significant improvements in threat detection and system adaptability compared to conventional methods. This research not only strengthens access control in cloud computing but also sets a foundational approach for future security enhancements in the field.