DESIGN AND DEVELOPMENT OF SMART AND SECURE HEALTHCARE SYSTEM

Abstract

The digital transformation of healthcare through the Internet of Medical Things (IoMT) has enabled real-time monitoring, remote diagnosis, and intelligent health management. However, the proliferation of interconnected medical devices and the continuous exchange of sensitive health data exposes IoMT systems to significant cyber threats. Ensuring data confidentiality, integrity, and availability in resource-constrained environments remains a critical challenge. This thesis presents a novel security framework that integrates deep learning-based intrusion detection with blockchain technology to provide comprehensive protection for healthcare data. First, an intelligent intrusion detection system (IDS) is developed using advanced deep learning architectures that combine convolutional and recurrent neural networks with attention mechanisms. These models effectively capture both spatial and temporal features of network traffic, enabling the detection of complex attack patterns in heterogeneous IoMT environments. To address challenges of data tampering, centralized trust, and unauthorized access, a blockchain-based security architecture is introduced. This framework incorporates dynamic encryption schemes, robust access control policies, zero- knowledge proofs for privacy preservation, and a Practical Byzantine Fault Tolerant (PBFT) consensus mechanism. Decentralized storage using the InterPlanetary File System (IPFS) ensures immutability, scalability, and high availability of medical records. Furthermore, a Dynamic Adaptive Deep Reinforcement Learning (DA-DRL) framework is proposed to enhance AES (Advanced Encryption Standard) encryption by dynamically adjusting key generation in response to real-time threats. The multi-layered security design integrates AES, SHA-512, Non-Interactive Zero Knowledge Proofs (NIZKPs), PBFT, and Attribute-Based Access Control (ABAC), providing robust defense against diverse attack vectors. The Comprehensive experimental evaluation demonstrates the effectiveness and scalability of the proposed approach. The DA-DRL-AES-SHA-512 methodology achieves an encryption time of 0.0975 s, decryption time of 0.0846 s, throughput of 75.63 transactions/s, and network overhead of only 0.1289%. Energy consumption and computational overhead are reduced to 0.3664 J and 0.48%, respectively. The Secure and Dependable Bi-LSTM-GRU Intrusion Detection Framework (S- BiLSTMGRU-IDF) achieves 99.94% accuracy in binary classification and 99.89% accuracy in multiclass classification, outperforming state-of-the-art models by 0.6%-3.5%. The results establish that combining the predictive power of deep learning with the immutable and trustless nature of blockchain provides a resilient, scalable, and efficient IoMT security solution. This integrated framework significantly enhances real-time threat mitigation, ensures data integrity and confidentiality, and lays a practical foundation for secure healthcare applications in real-world deployments.