An Enhanced Security Model for Protecting Data Transmission and Communication in Recent IoT Integrated Healthcare Industry Using Machine Learning Algorithm

Abstract

Different kinds of security need to be applied to various application-centric IoT networks. Safety is one of the most important aspects to be considered regarding user, device, and data. The healthcare industry is a special IoT network fully connected with medical/healthcare IoT devices. The data generated from the IoT devices are transmitted or shared from one hospital to another through the Internet. Healthcare data has more private, medical, and insurance information that intruders can use on the Internet. The intruders misbehave with the patient or the general public registered in the healthcare industry. Some intruders blackmail the patient based on their private/personal information. Healthcare industries and their research team are trying to create a security framework to safeguard the data to avoid these malicious activities. This paper aims to secure and analyze healthcare IoT data using the Support Vector Machine algorithm. It learns the entire dataset, classifies it, and calls the encryption-decryption algorithms (RSA) to secure private data. The proposed SVM and the RSA algorithm are implemented in Python, and the results are verified. The performance of the proposed SVM-RSA is evaluated by comparing its results with the other algorithms.