[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123749-en":3,"doc-seo-123749-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123749,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning for Healthcare-IoT Security - A Review and Risk Mitigation","Healthcare Internet-of-Things (H-IoT) relies on smart sensing devices to enable faster responses, treatment, and diagnosis, but it also expands the cyber risk surface as threats evolve. The paper reviews H-IoT fundamentals and the privacy and data security challenges that arise when machine learning and H-IoT are combined. It highlights monitoring across perception, network, cloud, and application layers and discusses anomaly detection methods tied to multiple attack types and protocols. It further argues for machine-learning and deep-learning based authentication to mitigate growing cybersecurity vulnerabilities, exploring strategies that build resilience.","Received 17 November 2023, accepted 11 December 2023, date of publication 22 December 2023, date of current version 29 December 2023.  \nDigital Object Identifier 10.1109/ACCESS.2023.3346320  \nMachine Learning for Healthcare-IoT Security: A Review and Risk Mitigation  \nMIRZA AKHI KHATUN1,2,(Member, IEEE), SANOBER FARHEEN MEMON1,(Member, IEEE), CIARÁN EISING1,2,(Senior Member, IEEE),  \nAND LUBNA LUXMI DHIRANI1,2,(Senior Member, IEEE)  \n1Department Electronic and Computer Engineering, University of Limerick, Limerick, V94 T9PX Ireland  \n2 SFI CRT Foundations in Data Science, University of Limerick, Limerick, V94 T9PX Ireland Corresponding author: Mirza Akhi Khatun ([Mirza.Akhi@ul.ie](Mirza.Akhi@ul.ie))  \nThis work has emanated from research conducted with the financial support of Science Foundation Ireland (SFI)  \nunder Grant Number 18/CRT/6049 .  \nABSTRACT The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a datadriven 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.  \nINDEX TERMS Healthcare-IoT, generative AI, 5G-IoT, security and privacy challenges, cybersecurity, attacks, anomaly detection, machine learning, deep learning, mitigation techniques, 5G NR.  \nI. INTRODUCTION  \nThe Internet of Things (IoT) consists of interconnected physical devices exchanging data through sensors, software, and connectivity [1], [2] . The healthcare industry has undergone a significant transformation in recent years with advances in IoT, cloud, artificial intelligence (AI), and machine learning (ML) . According to several experts, the expanding horizon of IoT is expected to improve healthcare. IoT can revolutionize healthcare globally by providing affordable healthcare [3], remote health monitoring [4], wellness management [5], and virtual rehabilitation [6] . Healthcare analytics can provide insight into disease and drug discovery while adding a new dimension [7] .  \nThe associate editor coordinating the review of this manuscript and  \napproving it for publication was Claudio Agostino Ardagna  .  \nThe modern world requires more efficient and timely interventions to combat escalating health issues. While traditional healthcare systems are effective, these systems are often slow and inflexible [8] . The COVID-19 pandemic has fueled the need for remote and precision healthcare, and such objectives could only be achieved using emerging technologies. Embedding an IoT-enabled architecture ina healthcare ecosystem may facilitate the collecting and processing real-time data from sensors (i.e., body sensors strategically placed on or within a patient’s body, aiding real-time data collection) [9] . Different sensors are used for different applications, such as motion, flow, and biomedical. However, the ones used fo","cbCaikfIFg3GhxBB","https://ap.wps.com/l/cbCaikfIFg3GhxBB","pdf",6268972,1,28,"English","en",105,"# Abstract\n# Introduction\n## Motivation and background\n## IoT and H-IoT architecture overview\n## Remote and real-time healthcare needs","[{\"question\":\"What is the main focus of this review on Healthcare-IoT security?\",\"answer\":\"It reviews healthcare IoT fundamentals and privacy/data security challenges, emphasizing security requirements and risk mitigation strategies for building resilience in H-IoT.\"},{\"question\":\"Which H-IoT layers are highlighted for monitoring and protection?\",\"answer\":\"The paper emphasizes monitoring healthcare IoT layers including perception, network, cloud, and application.\"},{\"question\":\"Why are machine learning and deep learning used for protecting H-IoT devices?\",\"answer\":\"A robust authentication mechanism based on machine learning and deep learning is presented as necessary to mitigate increasing cybersecurity vulnerabilities and support anomaly detection and response.\"}]","Machine Learning for Healthcare-IoT Security - 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