Identity Access Management for Network Devices: Enhancing Security of Modern IT Infrastructure in Healthcare

The current medical information technology (IT) structure, which depends on conventional administration, entails analyzing and storing a lot of clinical statistics. Access oversight and administration have been significantly enhanced by the incorporation of artificial intelligence (AI) technology into identity and access management (IAM) platforms, which provide smarter, more resilient, and adaptable options than conventional techniques. Internet of Things (IOTs)-based medical treatment has evolved with remarkable analytical capabilities and vast data preservation due to IoT integration and steady development. The cloud-based server where the data collected by the smart IoT component is kept is vulnerable to security breaches and assaults by hackers and unregistered individuals. The cloud analyzes medical data employing a convolutional neural network (CNN) without revealing client private data. The Industrial IoT (IIoT) has advanced to the point where the resultant network aims to create smart hospitals that can wirelessly track a patient's wellness through a smart gadget. The cloud-based server where the data collected by the smart IoT component is kept is vulnerable to security breaches and assaults by hackers and unregistered individuals. The cloud analyzes medical data employing a convolutional neural network (CNN) without revealing client private data. As a result, an IAM unit for the IoT-medical platform is implemented, which operates according to client characteristics. Employing the suggested study, a connection between the traits and the clients' confidence is found. The suggested CNN-LSTM algorithm achieves a 95% accuracy rate in terms of precision, recall, and F1 value. Better efficiency is achieved when the training collection's dimensions rise. The architecture functions more effectively without data augmentation than it does with it. Furthermore, a rise in the number of users results in an accuracy of about 98%. The suggested method is robust and successful in terms of little privacy breaches and elevated data credibility, according to empirical research.