Machine Learning–Based Identity and Access Management for Cloud Security

Cloud computing has the potential to offer an abundance of computing resources on demand due to its high scalability, which eliminates the need for providers to plan far in advance for hardware provisioning. Security remains a significant challenge in promoting cloud computing, but artificial intelligence (AI) can improve cloud services by enhancing security features. The privacy issue arises from the multiple data storage locations and available cloud services. Identity management services are crucial in establishing secure and efficient relationships in the cloud and cross-cloud environments by authenticating users based on their identity properties and past interactions. To incorporate AI, cloud security must be improved to provide an effective solution for data storage. We suggest AI-enabled cloud services security in this proposed architecture to protect cloud service users. Our model uses AI to identify users’ identities and restricts malicious access to cloud services, and it was trained based on CloudSim-generated datasets. Quality of services (QoS) was used to measure the architecture’s efficiency, and the results showed that the proposed architecture was effective and fruitful for users. The results effectively show the improvements in network throughput under congestion control with the proposed approach compared with existing state-of-the-art techniques.