Distributed Ledger Technology Enables Deep Convolutional Neural Network (CNN) Based Intrusion Detection to Enhance the Secure Collection & Storage of Health Data

Abstract

This abstract presents a comprehensive concept that leverages the synergy of various cutting-edge technologies to assure confidentiality and integrity of health data. Internet of Things (IoT) sensors are utilized as the primary data source, enabling the continuous monitoring of patients vital signs and health parameters. To ensure the security of this sensitive health data, Blockchain infrastructure is employed. The Blockchain employs a specialized routing protocol called Improved Whale Optimized Routing to efficiently handle data transactions. This routing protocol minimizes latency and maximizes throughput, ensuring the seamless transfer of health data to the Blockchain. The security of the Blockchain is further fortified by Deep Convolutional Neural Network (DCNN) based intrusion detection system. This DCNN model is trained using Distributed Ledger Technology (DLT), which ensures data privacy and integrity by distributing the training process across a network of nodes. This collaborative approach enhances the CNN's ability to identify and respond to potential security breaches in real time. Once the health data is verified as intrusion-free, it is securely stored in the Blockchain using the shortest path routing algorithm. This guarantees that data is efficiently stored, and retrieval is expedited when needed for medical diagnosis or research. This integrated system represents a novel approach for collecting and securely storing health data, providing a robust foundation for the future of healthcare systems. It combines the power of IoT sensors, Blockchain, Deep CNN-based intrusion detection and Distributed Ledger Technology to ensure the highest standards of data security and accessibility in healthcare applications.