Brexit Impact on Cyber Security of United Kingdom

Saleem, Muntaha

doi:10.1109/cybersecpods.2019.8885271

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…Only a few wearable devices have been approved by the TGA to monitor vital signs and physiological features in a clinical setting and were manufactured by Masimo Corporation, The Waringa Group, and GE Healthcare [ 120 - 122 ]. Particularly in the United Kingdom, there is a severe lack of medical device and data security regulations or guidance, which has been exacerbated by the recent changes in the UK political landscape [ 94 , 123 ]. Currently, there is little regulation or guidance on whether wearable devices should be approved for clinical use in the United Kingdom.…”

Section: Discussionmentioning

confidence: 99%

“…Currently, the different data policies that countries must abide by can make accessing data difficult. However, in the United Kingdom, how General Data Protection Regulation continues to be implemented is uncertain and could potentially make wearable device implementation in UK health care challenging [ 123 ]. Moreover, security mechanisms for wearable devices are left to the manufacturers with no clear legal obligations, and IT security is often insufficiently assessed [ 46 , 47 , 127 , 128 ].…”

Section: Discussionmentioning

confidence: 99%

“…Because of the unique changes to the UK political and regulatory landscape, changes in UK regulations have occurred. Therefore, the scoping review focused on PD studies in the United Kingdom because of the current uncertainty and changes in UK clinical and technological regulations [ 48 , 119 , 123 ]. However, the limited scope may have increased the chance of publication bias, as the scope was more focused [ 58 ].…”

Section: Discussionmentioning

confidence: 99%

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An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

Tam,

Alajlani,

Abd-alrazaq

2023

J Med Internet Res

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Background The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom. Objective In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom. Methods A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors. Results Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis. Conclusions This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

Tam,

Alajlani,

Abd-alrazaq

2023

J Med Internet Res

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“…Only a few wearable devices have been approved by the TGA to monitor vital signs and physiological features in a clinical setting and were manufactured by Masimo Corporation, The Waringa Group, and GE Healthcare [120][121][122]. Particularly in the United Kingdom, there is a severe lack of medical device and data security regulations or guidance, which has been exacerbated by the recent changes in the UK political landscape [94,123]. Currently, there is little regulation or guidance on whether wearable devices should be approved for clinical use in the United Kingdom.…”

Section: Principal Findingsmentioning

confidence: 99%

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review (Preprint)

Tam,

Alajlani,

Abd-alrazaq

2022

Preprint

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BACKGROUND The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom. OBJECTIVE In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom. METHODS A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors. RESULTS Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis. CONCLUSIONS This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape. CLINICALTRIAL

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“…IoT applications are the weak links in the information technology (IT) network, making them a major threat to the system's security [6]. Over 600 organizations were affected by the WannaCry malware attack in 2017, including health, educational, financial, and governance institutes, thus creating a global risk factor [7,8]. The NHS in the UK was one of the targeted organizations, where affected hospitals' yellow and medical staff were locked out of their digital system across England and Scotland.…”

Section: Introductionmentioning

confidence: 99%

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

Alrubayyi

Goteng

Jaber

et al. 2021

JSAN

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The fast growth of the Internet of Things (IoT) and its diverse applications increase the risk of cyberattacks, one type of which is malware attacks. Due to the IoT devices’ different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion-detecting algorithms inspired by the human body’s adaptive immune system techniques. Most of these algorithms imitate the human’s body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions.

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Brexit Impact on Cyber Security of United Kingdom

Cited by 5 publications

References 6 publications

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review (Preprint)

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

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