Blockchain-Integrated Security for Real-Time Patient Monitoring in the Internet of Medical Things Using Federated Learning DOI Creative Commons
Mohammad Faisal Khan,

Mohammad Abaoud

IEEE Access, Год журнала: 2023, Номер 11, С. 117826 - 117850

Опубликована: Янв. 1, 2023

The Internet of Medical Things (IoMT) heralds a transformative era in healthcare, with the potential to revolutionize patient care, healthcare services, and medical research. As all technological progressions, IoMT introduces suite complex challenges, predominantly centered on security. In particular, ensuring integrity, confidentiality, availability health data real-time communication stands paramount, given sensitivity information ramifications breaches or misuse. light these existing security frameworks, while commendable, exhibit limitations. Specifically, they often grapple comprehensive anomaly detection, effective resistance replay attacks, robust protection against threats like man-in-the-middle eavesdropping, tampering, identity spoofing. proposed framework integrates state-of-the-art encryption techniques, cutting-edge pattern recognition modules, adaptive learning mechanisms. These components collaboratively ensure integrity during transmission, provide conventional novel attack vectors, adapt evolving through continuous learning. Moreover, incorporates sophisticated checksum techniques advanced behavioral analysis, further enhancing its protective capabilities. Our system demonstrated significant improvements detection metrics, consistently outperforming benchmark solutions MRMS BACKM-EHA.

Язык: Английский

A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems DOI Creative Commons

Malarvizhi Nandagopal,

Koteeswaran Seerangan,

Tamilmani Govindaraju

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Май 4, 2024

Abstract In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial has made it possible to effectively control disease using networks interconnected sensors worn by individuals. The purpose this work develop an AI-IoMT framework for identifying several chronic diseases form the patients’ medical record. For that, Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new framework, been developed rapid diagnosis like heart disease, diabetes, stroke. Then, Auto-Encoder Model (DAEM) used in proposed formulate imputed preprocessed data determining fields characteristics or information that are lacking. To speed up classification training testing, Golden Flower Search (GFS) approach then utilized choose best features from data. addition, cutting-edge Bias Integrated GAN (ColBGaN) model created precisely recognizing classifying types records patients. loss function optimally estimated during Water Drop Optimization (WDO) technique, reducing classifier’s error rate. Using some well-known benchmarking datasets performance measures, DACL’s effectiveness efficiency evaluated compared.

Язык: Английский

Процитировано

5
Internet of things challenges for medical solutions DOI
José Luis Ordóñez-Ávila, Manuel Cardona

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 185 - 194

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Blockchain-Integrated Security for Real-Time Patient Monitoring in the Internet of Medical Things Using Federated Learning DOI Creative Commons
Mohammad Faisal Khan,

Mohammad Abaoud

IEEE Access, Год журнала: 2023, Номер 11, С. 117826 - 117850

Опубликована: Янв. 1, 2023

The Internet of Medical Things (IoMT) heralds a transformative era in healthcare, with the potential to revolutionize patient care, healthcare services, and medical research. As all technological progressions, IoMT introduces suite complex challenges, predominantly centered on security. In particular, ensuring integrity, confidentiality, availability health data real-time communication stands paramount, given sensitivity information ramifications breaches or misuse. light these existing security frameworks, while commendable, exhibit limitations. Specifically, they often grapple comprehensive anomaly detection, effective resistance replay attacks, robust protection against threats like man-in-the-middle eavesdropping, tampering, identity spoofing. proposed framework integrates state-of-the-art encryption techniques, cutting-edge pattern recognition modules, adaptive learning mechanisms. These components collaboratively ensure integrity during transmission, provide conventional novel attack vectors, adapt evolving through continuous learning. Moreover, incorporates sophisticated checksum techniques advanced behavioral analysis, further enhancing its protective capabilities. Our system demonstrated significant improvements detection metrics, consistently outperforming benchmark solutions MRMS BACKM-EHA.

Язык: Английский

Процитировано

10