• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.95-102
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• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.

• 전기전자학회논문지
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• 제22권3호
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• pp.590-598
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• 2018

최근 사물들에 인터넷 서비스를 제공하는 IoT(Internet of Things) 기술에 관한 관심이 높아지고 있다. IoT는 홈 네트워크, 헬스 케어, 재난 알림 등에서 다양한 서비스를 제공하고 있다. LLN(Low Power & Lossy Networks) 특징을 갖는 IoT는 빈번한 센서 노드의 손실이 발생한다. IoT의 표준 라우팅 프로토콜인 RPL은 센서 노드의 손실이 발생하면 전체 경로 재설정(Global repair)을 수행하여 데이터를 전송한다. 하지만 낮은 사양의 센서 노드로 인한 빈번한 센서 노드 손실은 잦은 전체 경로 재설정으로 인해 네트워크 성능 저하를 일으킨다. 본 논문에선 Storing mode 센서 노드 선정 이후에도 잦은 경로 재설정 문제로 인한 네트워크 성능 저하 문제를 해결하기 위한 Storing mode 센서 노드의 추가 선정 방법을 제안하고 각 Instance의 전체 경로 재설정 횟수의 평준화 방법을 제안하고자 한다.

• 한국정보전자통신기술학회논문지
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• 제11권6호
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• pp.778-783
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• 2018

정보통신과 산업기술의 급속한 발전으로 인간과 모든 프로그램 그리고 사물들이 인터넷을 통해 연결되는 초연결사회(hyper-connected society)가 구현되고 있다. 초연결사회를 구현하기 위한 정보의 수집은 사물과 사물 그리고 사물과 인간을 연결하는 사물인터넷(IoT: Internet of Thing)이 그 역할을 담당하고 있다. (MQTT: Message Queuing Telemetry Transport)는 이런 사물인터넷의 제약된 통신환경에 최적화되어 개발된 푸시 기술(push technology) 기반의 경량 메시지 전송 프로토콜이다. 초연결사회를 지향하면서 IoT가 담고 있는 정보는 센서의 환경정보에서 이제는 사람의 질병과 건강관리까지 담당하는 광범위한 정보가 되었다. 이런 MQTT에 대한 보안 문제는 환경정보 유출뿐만 아니라 개인의 정보 침해에까지 이르게 되었다. 우리는 이런 MQTT의 보안의 문제점을 해결하기 위해 혼돈계의 초기치 민감성, 유사 난수성을 무결성과 기밀성에 적용하여 새로운 보안 MQTT통신방법을 설계하였다. 우리가 설계한 혼돈계를 이용한 암호화 방법은 구조가 간단하고 계산량이 적기 때문에 IoT와 같은 제한된 통신환경에 적합한 통신방법이라고 생각된다.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.29-34
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• 2019

In Recently, as the interest in health has increased and the spread of smart phones has become common, the development of smart health care related contents has been active. In this paper, we introduce the design, implementation and effects of game-based content that can make walking exercise fun in the Internet of Things environment. This content calculates the consumed calories by walking the stairs with the Beacon installed, and incorporates games to encourage continuous fun activities. It also provides event functions that enable on-off-line coordination. The goal of this content is to enable busy modern people to exercise lightly, funly, and constantly in the surrounding activity space. The effect of this content has been confirmed through the review of many users participating in offline events. This content is expected to be able to converge with various types of healthcare systems with the expansion of the application space.

• 센서학회지
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• 제32권1호
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• pp.1-9
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• 2023

Recently, the digital healthcare technologies including non-invasive diagnostics based on Internet of Things (IOT) are getting attention. Human exhaled breath contains a variety of volatile organic compounds (VOCs), which can provide information of malfunctions of the body and presence of a specific disease. Detection of VOCs in exhaled breath using gas sensors are easy to use, safe, and cost-effective. However, accurate diagnosis of diseases is challenging because changes in concentration of VOCs are extremely small and lots of body factors directly or indirectly influence to the conditions. To overcome the limitations, highly selective nanosensors and artificial intelligent electronic nose (E-nose) systems have been mainly researched in recent decades. This review provides brief reviews of the recent studies for diabetes and lung cancer diagnosis using nanosensors and E-nose systems.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 추계 학술논문 발표대회
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• pp.57-58
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• 2018

The number of deaths in the korean construction industry is more than three times the OECD average. Although safety management system should be improved to prevent the safety accidents, it is difficult to improve due to domestic safety conditions. Especially, in order to prevent accidents at construction sites, there is an increasing tendency to monitor the movement of workers and equipment in real time by introducing a location positioning system. Therefore, this study proposes a system that can monitor the position of workers and heavy equipments in real - time, detect danger and transmit alarms so that workers can pay attention to safety and keep safety. The system is expected to reduce safety accidents by transmitting alarms to workers so that they can pay attention.

• 한국병원경영학회지
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• 제spc호
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• pp.74-84
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• 2016

This research is for an innovative health information service cases based on Information Communication Technology (ICT), conducted at a general hospital in Korea. This study introduces a personal use self-diagnosis & self management device for pulmonary chronic disease patients, a mobile communication application service for doctor rounds, a surgical education system providing natural-user-interface with virtual reality for surgeons, and an Internet of things(IOT) technology using personal electrocardiogram (ECG) measurement device cases. Due to every case is on developing, there are still many issues needed to be improved. For this reason, various opinions with constructive critiques from the readers of this paper will be welcomed for better practical implementation.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.184-191
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• 2020

With the emergence of the 4th Industrial Revolution, core technologies that will lead the 4th Industrial Revolution such as AI (artificial intelligence), big data, and Internet of Things (IOT) are also at the center of the topic of the general public. In particular, there is a growing trend of attempts to present future visions by discovering new models by using them for big data analysis based on data collected in a specific field, and inferring and predicting new values with the models. In order to obtain the reliability and sophistication of statistics as a result of big data analysis, it is necessary to analyze the meaning of each variable, the correlation between the variables, and multicollinearity. If the data is classified differently from the hypothesis test from the beginning, even if the analysis is performed well, unreliable results will be obtained. In other words, prior to big data analysis, it is necessary to ensure that data is well classified according to the purpose of analysis. Therefore, in this study, data is classified using a decision tree technique and a random forest technique among classification analysis, which is a machine learning technique that implements AI technology. And by evaluating the degree of classification of the data, we try to find a way to improve the classification and analysis rate of the data.

• International journal of advanced smart convergence
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• 제8권4호
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• pp.16-25
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• 2019

The Internet of things (IOT) is remodeling the agribusiness empowering the agriculturists through the extensive range of strategies, for example, accuracy as well as practical farming to deal with challenges in the field. The paper aims making use of evolving technology i.e. IoT and smart agriculture using automation. The objective of this research paper to present tools and best practices for understanding the role of information and communication technologies in agriculture sector, motivate and make the illiterate farmers to understand the best insights given by the big data analytics using machine learning. The methodology used in this system can monitor the humidity, moisture level and can even detect motions. According to the data received from all the sensors the water pump, cutter and sprayer get automatically activated or deactivated. we investigate a remote monitoring system using Wi-Fi. These nodes send data wirelessly to a central server, which collects the data, stores it and will allow it to be analyzed then displayed as needed and can also be sent to the client mobile.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.135-147
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• 2023

Internet of things has revolutionaries every field of life due to the use of artificial intelligence within Machine Learning. It is successfully being used for the study of Radiation monitoring, prediction of Ultraviolet and Electromagnetic rays. However, there is no particular system available that can monitor and detect waves. Therefore, the present study designed in which IOT enables intelligence system based on machine learning was developed for the prediction of the radiation and their effects of human beings. Moreover, a sensor based system was installed in order to detect harmful radiation present in the environment and this system has the ability to alert the humans within the range of danger zone with a buzz, so that humans can move to a safer place. Along with this automatic sensor system; a self-created dataset was also created in which sensor values were recorded. Furthermore, in order to study the outcomes of the effect of these rays researchers used Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Extra Trees, Bagging Classifier, Random Forests, Logistic Regression and Adaptive Boosting Classifier were used. To sum up the whole discussion it is stated the results give high accuracy and prove that the proposed system is reliable and accurate for the detection and monitoring of waves. Furthermore, for the prediction of outcome, Adaptive Boosting Classifier has shown the best accuracy of 81.77% as compared with other classifiers.