A Machine Learning Approach to Detecting Sensor Data Modification Intrusions in WBANs

Verner, Alexander; Butvinik, Dany

doi:10.1109/icmla.2017.0-163

Cited by 15 publications

(8 citation statements)

References 24 publications

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“…Additionally, measured data of blood glucose sensor was examined in order to detect adversarial and accidental data modification intrusions ( Verner & Butvinik, 2017 ). Here, Otsu’s thresholding algorithm was used with extra statistical analysis to create different informative feature vectors.…”

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

“…The results showed that the decision tree algorithm achieved the highest accuracy with fast training and prediction compared to the other algorithms. The SVM was the algorithm of choice in the study of Verner and Butvinik [20], where data of a blood glucose sensor was inspected in an attempt to detect accidental data modification intrusions. Other researchers [13] implemented an ML model to separate valid from anomalous data using a combination of a neural network (NN) with Ensemble Linear Regression as detection method.…”

Section: Related Work 31 Anomaly Detection In Ehealth Networkmentioning

confidence: 99%

Differentially Private Federated Learningfor Anomaly Detection in eHealth Networks

Cholakoska¹,

Pfitzner

Gjoreski

et al. 2021

Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Increasing number of ubiquitous devices are being used in the medical field to collect patient information. Those connected sensors can potentially be exploited by third parties who want to misuse personal information and compromise the security, which could ultimately result even in patient death. This paper addresses the security concerns in eHealth networks and suggests a new approach to dealing with anomalies. In particular we propose a concept for safe in-hospital learning from internet of health things (IoHT) device data while securing the network traffic with a collaboratively trained anomaly detection system using federated learning. That way, real time traffic anomaly detection is achieved, while maintaining collaboration between hospitals and keeping local data secure and private. Since not only the network metadata, but also the actual medical data is relevant to anomaly detection, we propose to use differential privacy (DP) for providing formal guarantees of the privacy spending accumulated during the federated learning. CCS CONCEPTS• Security and privacy → Intrusion detection systems; • Applied computing → Health care information systems; • Computing methodologies → Anomaly detection; Distributed artificial intelligence.

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“…It was concluded that the system was able to effectively detect the anomaly. Additionally, measured data of blood glucose sensor was examined in order to detect adversarial and accidental data modification intrusions (Verner & Butvinik 2017). Here, Otsu's thresholding algorithm was used with extra statistical analysis to create different informative feature vectors.…”

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

A Machine Learning Approach to Detecting Sensor Data Modification Intrusions in WBANs

Cited by 15 publications

References 24 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

Differentially Private Federated Learningfor Anomaly Detection in eHealth Networks

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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