An Improved Mechanism to Maintain Data Integrity and Anomaly Detection in Cloud Storage

There are different services which can be offered by cloud platforms such as Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS). These services and resources are provided by the third party and available on-demand through the Internet. It enables the organizations to focus on the infrastructure and services rather than maintain it. It includes several challenges such as vendor lock-in, security, data integrity, and compliance issues. To address these issues is very challenging for organizations and relies on cloud services. However, data integrity is one of the critical challenges due to data inconsistency, user trust, completeness, and compliance. Therefore, to ensure the data integrity distinct mechanisms can be applied such as encryption, access control, regular audits, backups, and data validations. The integration of these strategies results in a comprehensive framework for maintaining data integrity in cloud storage, ensuring trustworthiness, security, and compliance with regulatory standards. The result set indicates the higher accuracy approximate 1.0 with good number of instances of benign and suspicious data. The major aim of the current research is to enhance the security of cloud data by using AES algorithms and provide double encryption. The extraction of the top features by using recursive feature elimination. Finally, AI-powered anomaly detection is proposed for data integrity and to detect anomalous access patterns, unauthorized changes, and suspicious transactions in real-time, helping prevent integrity violations before they escalate.