A Two-Level Machine Learning Based-Intrusion Detection System for IoT Healthcare Application Based on Blockchain

A Multi-Factor Authentication Framework for IoT Healthcare Systems Based on Blockchain and Smart Contract

Authentication is becoming essential due to the expansion of the Internet of Things (IoT) applications in smart cities, supply chain, and healthcare. In the healthcare sector, hospitals use centralized server-based systems to manage user information and patient medical records. However, this approach may lead to scalability, interoperability, security and privacy challenges. To address these issues, this paper presents a blockchain-based multi-factor authentication (MFA) framework for IoT healthcare systems. The framework uses the Ethereum blockchain and smart contracts to improve authentication security and minimize unauthorized access risk. It also uses the InterPlanetary File System (IPFS) to securely and efficiently store sensitive medical data. Performance and security are evaluated to show the effectiveness, reliability, and feasibility of the proposed system.

An Intelligent Intrusion Detection and Privacy-Preserving Architecture for the Internet of Medical Things (IoMT)

This work proposes an intelligent, lightweight Tiny LSTM–GRU hybrid IDS on the edge to monitor device-generated behavioral patterns in real time, with minimal computational and energy overhead, and proposes an adaptive FedProx-based weighted federated learning framework.

Enhancing Smart Home Security Using Adaptive Access Control with Blockchain and Machine Learning

Smart homes, equipped with interconnected IoT devices such as locks, cameras, and sensors, face critical security challenges due to the limitations of static access control mechanisms like Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC), which lack adaptability to dynamic, multi-user environments and evolving threats. To address this problem, this research introduces a hybrid Blockchain–Machine Learning (ML) framework that ensures secure, adaptive, and context-aware access control for smart home ecosystems. The proposed system integrates IoT devices with ML algorithms, including Support Vector Machines (SVM) and Neural Networks, to predict user behaviours and dynamically adjust access permissions in real time, while Blockchain ensures immutable, decentralized, and tamper-proof logging of access events. The methodology employed a mixed approach, beginning with an extensive literature review to identify shortcomings in existing static models, followed by system design using smart contracts, caching strategies to reduce latency, and a user perception survey involving 25 participants to validate acceptance and usability. Results demonstrated high user trust and readiness to adopt the proposed system, with 96% of respondents favouring Blockchain-ML-enabled dynamic access control over conventional methods despite concerns about privacy risks, costs, and implementation complexity. This work contributes to society by offering a scalable and intelligent smart home security solution that enhances trust, improves user experience, and strengthens resilience against cyber threats, ultimately supporting safer and smarter living environments.

A Secure IoT-Based Biomedical Monitoring System Using Machine Learning–Driven Intrusion Detection and Data Encryption

IoT enables continuous patient monitoring and immediate response to healthcare needs by creating a connected environment where healthcare services can interact seamlessly and continuously with one another. However, IoT-based healthcare systems represent an attractive target for cybercriminals, as they hold sensitive medical information on individuals. An IoT-based healthcare platform can be compromised through data manipulation, unauthorized access, and denial-of-service (DoS) attacks. To keep this issue from occurring, this paper describes a secure solution for the continuous monitoring of patient health through the use of IoT technology. The Secure IoT-based Biomedical Monitoring Framework (SIBMF) will utilize lightweight encryption techniques and the Machine Learning-based Intrusion Detection System (MLIDS) to enhance security for IoT users. Physiological signals from patients’ bodies (ECG, heart rate, and temperature) will be captured by IoT sensors and transmitted in an encrypted format to a cloud-based server. IoT sensors will utilize a cryptographic algorithm to encrypt their biomedical data prior to sending it to a cloud-based server, ensuring the confidentiality of patient data. MLIDS will detect unauthorized suspect access to the IoT network by analyzing the features of the traffic created by the IoT devices. A prototype of the SIBMF was built in MATLAB, and various performance metrics (i.e., accuracy, detection rate, false positive rate, data encryption time, and latency) were assessed. The experimental results demonstrate that this approach significantly improves data security and will provide superior accuracy for monitoring patients’ health and has minimal computational requirements.

A Secure Blockchain-Enabled SDN-Based Edge Computing Framework for IoT Healthcare Systems

: Healthcare systems based on the Internet of Things (IoT) are widely used in patient monitoring, telemedicine, emergency care, and hospital-at-home services. However, existing IoT healthcare networks still face major challenges related to security, trust management, network control, and real-time emergency data handling. Centralized trust mechanisms and repeated cloud-based verification may increase delay and reduce reliability in critical healthcare scenarios. Moreover, suspicious medical devices must be quickly isolated, while sensitive patient data and emergency traffic must be protected and prioritized. To address these issues, this work proposes a blockchain-enabled, software-defined networking (SDN)-based edge computing framework for IoT healthcare systems. The proposed architecture integrates blockchain for secure and distributed trust management, edge computing for low-latency processing, and an Open Network Operating System (ONOS)-based SDN control plane for emergency-aware flow management, dynamic traffic engineering, and policy-based medical traffic segmentation. The framework is evaluated against a recent blockchain-edge-cloud-SDN IoT baseline. The results show that the proposed framework reduces average jitter by 15.05% , energy consumption by 6.10% , and delay by 14.16% , while improving packet delivery ratio by 6.85% and throughput by 9.85% . These results indicate that the proposed approach can provide more reliable, efficient, and secure communication for connected hospitals, intensive care unit (ICU) monitoring

A secure IoT-based healthcare monitoring system using sand cat optimized ECC with blockchain and optimized Bi-GRU for disease prediction