Intelligent identity and access management using neural networks

Abstract

Cloud computing ensures easy access to data on multiple form factor devices. It provides the flexibility to connect with business anytime, anywhere. Numerous cloud benefits are accounted for, including reduced information technology costs, infrastructure costs, scalability, business continuity, collaboration efficiency, the flexibility of work practices, and automated updates. These benefits can be utilized only with a strong identity and access management of cloud applications and services. Various challenges of IAM systems include user password fatigue, failure-prone manual provision and de-provisioning, visibility of compliance, isolated user directories for each application, access management across an explosion of browsers and devices, updated application integrations, the variation of administrative models for different applications and lack of knowledge of best practices for optimal utilization of cloud services. So to overcome the challenges, this paper proposes an intelligent identity and access management model trained using neural networks. This model extracts user’s file access logs, learn from them, and provides a question based intelligent identification system for user identification, authentication, and authorization of files, and ensures efficient and faster file access.