A Novel Whale Based Optoelectronics System for Securing Healthcare Data in Internet of Things (IoT)-Based Cloud Environment

Abstract

Healthcare data, encompassing vital signs such as temperature, heart rate, respiratory rate, and blood pressure along with results from the lab and needs to be kept secure and private as it is used to monitor the health of patients in real-time and make decisions on their healthcare. Unfortunately, healthcare data in IoT-based cloud systems are at risk of malicious attacks, including access control bypass, malware injection, and data privacy violations; this is due to the distributed nature of cloud computing, which requires the processing and storage of sensitive information among multiple servers, heightening the risk of data breaches. The current security algorithms are unable to understand the complexity of healthcare data for effective security; thus, robust security measures based on the inherent data features must be employed to strengthen encryption, access control, and two-factor authentication for guaranteeing the security of healthcare data in a cloud-powered IoT environment. In this paper, we have developed a novel Whale-fitness based Recurrent Neural network Scheme (WbRNS), that can understand data patterns and employ attributes-based Encryption (ABE) to transform plain text into ciphertext. The continual updating of whale fitness enables the neural network model to monitor for attacks and unauthorized network access.