Intelligence-based Network Security System to Predict the Possible Threats in Healthcare Data

Abstract

The world is filled with exciting technologies and ideas; scientists build machines to avoid human intervention in completing work. It is highly challenging to complete the task without the artificial intelligence (AI) technology intervention. With technological development, specific processes or consultations are performed with doctors available worldwide. In this scenario, it could be noticed that health care is one of the world’s expected domains that require the most incredible attention in data security while performing data transfer. Nodes in the network are considered based on the weakest link to overcome the cyber attacker’s issues. Besides building the software for data storage, a better mechanism has to be incorporated to provide security to the stored data. This process is a delicate task for every network engineer. This paper will explain such concepts related to health prediction and health care by building the most robust network security systems. The proposed optimized neural network representation is differentiated with the available data conclusive process. From the outcomes, it is observed that the suggested representation achieves an accuracy of 98.89%, which is 4.76% higher than the existing model.

Cybersecurity in health sector: a systematic review of the literature

“…On the other hand, the authors recommend the ontology tool that is equipped with rules, classifying security threats and automatically recommending controls that can be applied in the face of a threat [66], [67]. Other authors mention a neural network with artificial intelligence to perform queries and transfers of medical data in a secure way [68]- [71]. On the other hand, the author mentions that the defender software is a health industry computer network that goes against attacks, these network topologies are attractive that protect the information in real-time [72]- [74].…”