• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.73-78
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• 2024

COVID-19 pandemic outbreak increased the use of Internet of Medical Things (IoMT), but the existing IoMT solutions are not free from attacks. This paper proposes a secure and resilient framework for IoMT, it computes the risk using Risk Impact Parameters (RIP) and Risk is also calculated based upon the Threat Events in the Internet of Medical Things (IoMT). UICC (Universal Integrated Circuit Card) and TPM (Trusted Platform Module) are used to ensure security in IoMT. PILAR Risk Management Tool is used to perform qualitative and quantitative risk analysis. It is designed to support the risk management process along long periods, providing incremental analysis as the safeguards improve.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.641-643
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• 2021

The Internet of Things (IoT) connects all markets and industries, enabling new business models for a variety of services and service providers. The Internet of Medical Things (IoMT) not only accelerates medical advances, but also enables treatment with a more human approach. In addition, it improves treatment methods and quality of precision medical care through data, enables timely treatment, and improves operational productivity of medical institutions through a simplified workflow. However, since the medical field directly affects human health and life, securing security has become an issue above all else, and is a target for hackers trying to exploit it. Therefore, in this study, IoMT technology and security threats and countermeasures in the medical field are analyzed.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.254-266
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• 2021

The Internet of Medical Things (IoMTs) are to be considered an investment and an improvement to respond effectively and efficiently to patient needs, as it reduces healthcare costs, provides the timely attendance of medical responses, and increases the quality of medical treatment. However, IoMT devices face exposure from several security threats that defer in function and thus can pose a significant risk to how private and safe a patient's data is. This document works as a comprehensive review of modern approaches to achieving security within the Internet of Things. Most of the papers cited here are used been carefully selected based on how recently it has been published. The paper highlights some common attacks on IoMTs. Also, highlighting the process by which secure authentication mechanisms can be achieved on IoMTs, we present several means to detect different attacks in IoMTs

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.285-287
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• 2019

MPEG - IoMT(Internet of Media Things) 는 사물 인터넷 및 웨어러블 환경에서의 효율적인 미디어 서비스 제공을 위한 데이터 포맷 및 API(Application Programming Interface) 표준을 제공하고 있다. 본 논문에서는 MPEG - IoMT 에 채택된 헬스케어(healthcare) 정보 서술 툴에 대한 IoMT 참조 SW 에서의 검증 실험내용을 기술한다. IoMT 는 의료영상 저장/관리 및 통신을 위한 표준인 DICOM (Digital Imaging a nd Communication in Medical)을 기반으로 의료 미디어 정보를 기술하기 위한 Healthcare Information 스키마(schema)와 이를 기반으로 서술된 정보를 IoT 및 웨어러블 환경에서 활용하기 위한 API 표준을 포함하고 있다. 본 논문에서는 IoMT 참조 SW 를 이용하여 헬스케어 스키마에 따른 헬스케어 정보의 생성 및 파싱(parsing) 을 검증하고, 서술정보를 MThing (Media Thing) 들 간의 교환을 위한 API 에 대한 검증 내용을 보인다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.718-723
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• 2018

In a positive Internet of Medical Things (IoMT) environment, by combining the latest computer network technology with IoT technology, remote health care such as health care and monitoring is improved through the provision of quality medical information services. In this paper, we identified and compared the platforms applied with blockchain and presented the results of developing the product distribution de-centralized DApp. In the process, we developed a distribution platform that can use blockchain technology to identify product fraud, manage data, manage customers' information, prevent forgery, track transaction history, and facilitate product transactions.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.