Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a data-driven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cybe...

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Vydané v:IEEE access Ročník 11; s. 145869 - 145896
Hlavní autori: Khatun, Mirza Akhi, Memon, Sanober Farheen, Eising, Ciaran, Dhirani, Lubna Luxmi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
5G mobile communication
5G NR
5G-IoT
Anomalies
anomaly detection
attacks
Blood pressure
Command and control
Cybersecurity
Data integrity
Deep learning
generative AI
Generative artificial intelligence
Health care
Healthcare-IoT
Internet of medical things
Internet of Things
Machine learning
Medical services
mitigation techniques
Monitoring
Narrowband
Privacy
Risk
Security
security and privacy challenges
Sensors
Temperature sensors
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí:The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a data-driven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3346320