Blockchain-Enabled Secure Healthcare Data Management with Modified Gazelle Optimization and DLT-Trained RNN-BILSTM Approach - Saxena, Rolly; D, Srinivasa Rao (JOIN (Jurnal Online Informatika), 2025)
The growth of the healthcare system has posed challenges in safeguarding patient privacy amidst the storage, distribution and management of medical data. Blockchain (BC) offers a promising result by securely enabling the exchange of medical information. Utilizing block chain technology ensures the security of individuals' confidential health information.  The use of a decentralized, immutable ledger using blockchain technology provides a secure, impenetrable platform for storing and retrieving private medical information, protecting patient privacy.   The application of Modified Gazelle Optimization enables the determination of the shortest path for efficient data transfers within the block chain network. By adopting a specialized routing protocol called Modified Gazelle Optimized Routing, this approach minimizes latency and maximizes throughput, facilitating continuous and expedited transfer of health data across the network. To assure the data confidentiality and integrity of network nodes, a Distributed Ledger Technology (DLT) trained Recurrent Neural Network with Bidirectional Long Short Term Memory (RNN-BILSTM) approach is implemented. This advanced Deep Learning (DL) technique enhances the security and reliability of the network by detecting and preventing unauthorized access and tampering attempts. The proposed RNN-BILSTM based Intrusion Detection System (IDS) efficiently detects different types of attacks with high accuracy. By analyzing network traffic and patterns in real-time, the IDS have the ability to identify and mitigate harmful Internet of Things (IoT) requests and various stealthy attack types, including previously unknown threats. The outcomes of this research prove an efficacy and consistency of the proposed strategy in enhancing the security, efficiency and performance matrix with an accuracy of 97% and comparative analysis is done with traditional methods, thereby ensuring an availability and integrity of healthcare data.