Assessing medical device vulnerabilities on the Internet of Things

McMahon, Emma; Williams, Ryan; El, Malaka; Samtani, Sagar; Patton, Mark; Chen, Hsinchun

doi:10.1109/isi.2017.8004903

Cited by 45 publications

(42 citation statements)

References 6 publications

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“…D. Decoded info transmission: Attackers screen prepare thus on listen in and take passwords. The transmission of decoded info accesses the appliance thus on separate knowledge for transmission malevolent directions [13]. Encryption Security is most widely discussed in this paper.…”

Section: Security Issues In Existing Iot Health Care Systemmentioning

confidence: 99%

Integrated Secure Health Domain System using IoT

Valeti*,

Sharmila

2019

IJRTE

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Fog computing plays major role in health care system. Fog performs well compare with cloud computing. Health care system needs more enhancements because there sensitivity. It is very important to secure the data in the health care system. Though there are many systems for health care security still there are issues to secure the data transfer from client to the fog by cloud computing. In the previous papers, we have discussed QOS parameters and various preventions to transmission of data from sensors to fog. IOT is most widely used in health systems to increase the performance which adopted with fog computing. In this paper, the integrated secure health domain system (ISHDS) used to overcome the system failures and providing the security for the health care data in various situations.

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Section: Security Issues In Existing Iot Health Care Systemmentioning

confidence: 99%

Integrated Secure Health Domain System using IoT

Valeti*,

Sharmila

2019

IJRTE

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“…The governments and research communities have realized the importance to protect their IMDS [3][4][5][6]. However, there is limited research on how the compromise has happened and how this can be prevented.…”

Section: Introductionmentioning

confidence: 99%

“…However, there is limited research on how the compromise has happened and how this can be prevented. Current cyber security research in healthcare focuses on protecting medical devices from cyber attacks [3][4][5][6][7][8][9][10], however, those strategies cannot be applied to protect IMDS. Accordingly, this study was undertaken to show how the IMDS diagnosis results can be compromised and present a set of cyber defense strategies to prevent such compromise.…”

Section: Introductionmentioning

confidence: 99%

Security Defense Strategy for Intelligent Medical Diagnosis Systems (IMDS)

Luo

Soygazi

Janicke

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Aims: The Intelligence Medical Diagnosis System (IMDS) has been targeted by the cyber terrorists, who aim to destroy the Critical National Infrastructure (CNI). This paper is motivated by the most recent incidents happened worldwide and have resulted in the compromise of diagnosis results. This study was undertaken to show how the IMDS could be attacked and diagnosis results compromised and present a set of cyber defense strategies to prevent against such attacks. Methods and Results: This study used the ECGs data from the PhysioNet/Computing in Cardiology (CinC) Challenge 2017. We fed the data into our IMDS and launched a series of ethical hacking, which is specifically tailored to target IMDS. We proposed a set of cyber security strategies to prevent such compromise. We tested the effectiveness of our cyber defense strategies using an experiment. The results showed that the strategies were effective in protecting the IMDS diagnosis results from being compromised. Conclusions: This study provides novel insights into the protection of IMDS and concludes that our cyber defense strategies can protect IMDS from being compromised by Brute Force and SQL Injection attacks.

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“…Even though these numbers present a promising future for wearable-based health monitoring solutions, our review indicates that there still exist some crucial hurdles before implementing health monitoring devices and applications in real-time [ 6 ]. These include: Focusing on developing physiological signal analysis algorithms which promote edge computing approaches [ 4 , 5 , 6 , 9 ]. That is, the data acquisition, compression and analysis must be done at the device level without having the need to transmit long, streaming data to cloud services.…”

Section: Introductionmentioning

confidence: 99%

“…This would lead to optimization of cloud resources by minimizing usage for data storage and analysis. The idea of edge computing is to help in optimizing on-device memory and power usage, thereby increasing operating efficiency and throughput [ 5 , 9 ]. In addition to this, there is also a need for data acquisition standardization with respect to data formats and communication protocols [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health

Athavale

Krishnan

2018

Sensors

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Actigraphs for personalized health and fitness monitoring is a trending niche market and fit aptly in the Internet of Medical Things (IoMT) paradigm. Conventionally, actigraphy is acquired and digitized using standard low pass filtering and quantization techniques. High sampling frequencies and quantization resolution of various actigraphs can lead to memory leakage and unwanted battery usage. Our systematic investigation on different types of actigraphy signals yields that lower levels of quantization are sufficient for acquiring and storing vital movement information while ensuring an increase in SNR, higher space savings, and in faster time. The objective of this study is to propose a low-level signal encoding method which could improve data acquisition and storage in actigraphs, as well as enhance signal clarity for pattern classification. To further verify this study, we have used a machine learning approach which suggests that signal encoding also improves pattern recognition accuracy. Our experiments indicate that signal encoding at the source results in an increase in SNR (signal-to-noise ratio) by at least 50–90%, coupled with a bit rate reduction by 50–80%, and an overall space savings in the range of 68–92%, depending on the type of actigraph and application used in our study. Consistent improvements by lowering the quantization factor also indicates that a 3-bit encoding of actigraphy data retains most prominent movement information, and also results in an increase of the pattern recognition accuracy by at least 10%.

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Assessing medical device vulnerabilities on the Internet of Things

Cited by 45 publications

References 6 publications

Integrated Secure Health Domain System using IoT

Integrated Secure Health Domain System using IoT

Security Defense Strategy for Intelligent Medical Diagnosis Systems (IMDS)

A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health

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