Syndrome: Spectral analysis for anomaly detection on medical IoT and embedded devices

Sehatbakhsh, Nader; Alam, Monjur; Nazari, Alireza; Zajić, Alenka; Prvulovic, Milos

doi:10.1109/hst.2018.8383884

Cited by 49 publications

(19 citation statements)

References 17 publications

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“…In another study made by Sehatbakhsh et al (2018) , SYNDROME was proposed. This method can detect code injection attack in a known program which runs on the system in a real time manner.…”

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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“…In another study made by Sehatbakhsh et al (2018) , SYNDROME was proposed. This method can detect code injection attack in a known program which runs on the system in a real time manner.…”

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

“…Statistical and analytical methods have already been specified to characterize IoT data and infer potential attacks. For instance, the authors of [22] consider the statistical distribution of the electromagnetic signals generated by IoT devices. In addition, the authors of [23] exploit several machine learning methods applied to datasets generated by smart cities.…”

Section: Related Workmentioning

confidence: 99%

Comparative Assessment of Process Mining for Supporting IoT Predictive Security

Hemmer

Abderrahim

Badonnel

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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The growth of the Internet-of-Things (IoT) has been characterized by the large-scale deployment of sensors and connected objects. These ones are integrated with other Internet resources in order to elaborate more complex systems and applications. Security management is a major challenge for these systems due to their complexity, their heterogeneity and the limited resources of their devices. In this paper we evaluate the exploitability and performance of a process mining approach for detecting misbehaviors in such systems. We describe the considered architecture and detail its operation, from the generation of behavioral models to the detection of potential attacks. We formalize several alternative commonly-used detection methods, including elliptic envelope, support-vector machine, local outlier factor, and isolation forest techniques. After presenting a proofof-concept prototype, we quantify comparatively the benefits and limits of our process mining solution combined with data preprocessing, through extensive experiments based on different industrial datasets.

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“…Experimental results demonstrated that decision tree-based algorithms achieved the highest detection accuracy, low false positive rate, fast training and prediction speed compared to those of other algorithms. In another study made by Sehatbakhsh et al (Sehatbakhsh et al 2018), SYNDROME was proposed. This method can detect code injection attack in a known program which runs on the system in a real time manner.…”

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

Syndrome: Spectral analysis for anomaly detection on medical IoT and embedded devices

Cited by 49 publications

References 17 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

Comparative Assessment of Process Mining for Supporting IoT Predictive Security

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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