Abstract

The process of safely granting people or organizations access to networks, systems, or resources via authentication and authorization is known as an ID management system. Ensuring safe access control involves a range of procedures, including identity verification, authorization, and authentication. ID Management System may have several disadvantages, such as high setup costs, possible incompatibilities with current infrastructure, and the demand for maintenance and upgrades to handle changing security risks and legal obligations. Research proposed a novel technique discriminative fragile support vector machine (DFSVM) to secure the user ID in cloud security. The ID management data is gathered. Here, research utilize the tokenization for data pre-processing, and Term frequency Inverse document frequency (TF-IDF) is employed for feature extraction to extract the pre-processed data. To reduce the risk of identity theft, illegal access, and data breaches, the system incorporates extensive authentication, authorization, and access control measures. The system reduces the potential of illegal access and risk from insiders by integrating role-based authorizations and selective access controls, which provide users with fine-grained control over highly confidential financial information. Performance evaluates thorough security assessments of the financial sector by using our proposed method. The parameter metrics used in this study are F1-score (96%), precision (97%), RMAE (22.24), MAE (5.8), and recall (96.51%). The financial industry greatly benefits by using DFSVM for an ID Management System, which improves user authentication and data integrity to efficiently minimize cyber risks and maintain regulatory compliance.