AUDIT: AnomaloUs data Detection and Isolation approach for mobile healThcare systems

Amor, Lamia Ben; Lahyani, Imene; Jmaïel, Mohamed

doi:10.1111/exsy.12390

Cited by 14 publications

(5 citation statements)

References 30 publications

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“… Ben Amor, Lahyani & Jmaiel (2020) suggested an anomaly data detection and separation for the mobile smart healthcare. Two steps were implemented in the study, namely a preprocessing step and a real-time processing step.…”

Section: Results and Findingsmentioning

confidence: 99%

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Hameed

Hassan

Latiff

et al. 2021

PeerJ Computer Science

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Background The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device’s lifespan. Methodology This article is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 and 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis was conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

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Section: Results and Findingsmentioning

confidence: 99%

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

Hameed

Hassan

Latiff

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…This article usually uses the adjacency matrix to represent the connectivity between nodes. This article defines that A ij represents the adjacency value for nodes v i and v j , the value range and value condition A ij are shown in formula (6).…”

Section: Rules For Calculating the Weight Coefficientmentioning

confidence: 99%

“…Spatio-temporal heterogeneity data (STD) [1][2][3] is the data basis for applying big data analysis technology to solve decision-making problems in urban operation and maintenance [4], oil and gas development [5], medical decision-making [6], life sciences [7] and other fields, and its accuracy detection is undoubtedly important [8,9]. Due to the stochasticity and complexity of STD in the temporal dimension and the global and local correlation in the spatial dimension, which makes detection extremely difficult, related research has also become a research hotspot for data analysts [10].…”

Section: Introductionmentioning

confidence: 99%

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Liu¹,

Zhang²,

Yin³

et al. 2023

Computer Systems Science and Engineering

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Spatio-temporal heterogeneous data is the database for decisionmaking in many fields, and checking its accuracy can provide data support for making decisions. Due to the randomness, complexity, global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions, traditional detection methods can not guarantee both detection speed and accuracy. Therefore, this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks. Firstly, the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology. Secondly, design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy. Finally, the proposed method is compared with three methods, ARIMA, T-GCN, and STGCN, in real scenarios to verify its effectiveness in terms of detection speed, detection accuracy and stability. The experimental results show that the RMSE, MAE, and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree, which are 13.82/12.08, 2.77/2.41, and 16.70/14.73, respectively. Also, it detects the shortest time of 672.31/887.36, respectively. In addition, the evaluation results are the same under different time periods of processing and complex topology environment, which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.

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“…Results showed an accuracy of 91% and F1 score of 90%. Ben Amor et al (Ben Amor et al 2020) suggested an anomaly data detection and separation for the mobile smart healthcare. Two steps were implemented in the study, namely a preprocessing step and a real-time processing step.…”

Section: Computer Sciencementioning

confidence: 99%

Peer Review #3 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.2)"

2021

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Background . The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device's lifespan.Methodology. This paper is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 to 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis were conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results.It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

show abstract

AUDIT: AnomaloUs data Detection and Isolation approach for mobile healThcare systems

Cited by 14 publications

References 30 publications

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Peer Review #3 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.2)"

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