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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Vydáno v:	IEEE access Ročník 11; s. 145869 - 145896
Hlavní autoři:	Khatun, Mirza Akhi, Memon, Sanober Farheen, Eising, Ciaran, Dhirani, Lubna Luxmi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Abstract	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.
AbstractList	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.
Author	Dhirani, Lubna Luxmi Khatun, Mirza Akhi Memon, Sanober Farheen Eising, Ciaran
Author_xml	– sequence: 1 givenname: Mirza Akhi orcidid: 0009-0008-4718-187X surname: Khatun fullname: Khatun, Mirza Akhi email: Mirza.Akhi@ul.ie organization: Department Electronic and Computer Engineering, University of Limerick, Limerick, Ireland – sequence: 2 givenname: Sanober Farheen orcidid: 0000-0001-9382-7354 surname: Memon fullname: Memon, Sanober Farheen organization: Department Electronic and Computer Engineering, University of Limerick, Limerick, Ireland – sequence: 3 givenname: Ciaran orcidid: 0000-0001-8383-2635 surname: Eising fullname: Eising, Ciaran organization: Department Electronic and Computer Engineering, University of Limerick, Limerick, Ireland – sequence: 4 givenname: Lubna Luxmi orcidid: 0000-0001-7654-4117 surname: Dhirani fullname: Dhirani, Lubna Luxmi organization: Department Electronic and Computer Engineering, University of Limerick, Limerick, Ireland
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Snippet	The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a data-driven infrastructure that highly relies on smart sensing devices...
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SubjectTerms	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
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Title	Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation
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