• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.65-72
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• 2020

The Internet of Things(IoT) is to build a hyper-connected society through interconnection, and on this basis, to improve the quality of life and productivity, including solving social problems, and to become the next growth engine for the nation. The open common eco-system pursued by the IoT can start with the under- standing of the word 'open'. The IoT can achieve the expected effect of lowering the barriers to entry of technology development, and in these changes, OSSW and OSHW play a very important role in accelerating IoT eco-system maturity and breaking the boundaries between industries to promote convergence. Recently, it has developed into an intelligent IoT that combines artificial intelligence (AI) with the connectivity of the IoT. Here, I will analyze the direction of development of the IoT through understanding and analysis of open source.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.73-79
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• 2020

Since the start of the 4th industrial revolution, technologies have been developed in the Internet of Things (IoT), artificial intelligence (AI), virtual reality (VR), and 5G. Compared to other technologies IoT is currently being commercialized more than other technologies where the numbers of connected things are increases every year. The IoT has a huge advantage to provide convenience and lots of information to users, but security cannot keep up with the speed of development. IoT services continue to provide services for related devices, but at present, more and more types of new services are being combined with other technologies by utilizing the services of devices. This paper reviews and analyzes research on security issues and services related to the Internet of Things to explore how security trends and service delivery will develop in the future.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.463-468
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• 2021

Although the IoT is showing explosive growth due to the development of technology and the spread of IoT devices and activation of services, serious security risks and financial damage are occurring due to the activities of various botnets. Therefore, it is important to accurately and quickly detect the activities of these botnets. As security in the IoT environment has characteristics that require operation with minimum processing performance and memory, in this paper, the minimum characteristics for detection are selected, and KNN (K-Nearest Neighbor), Naïve Bayes, Decision Tree, Random A comparative study was conducted on the performance of machine learning algorithms such as Forest to detect botnet activity. Experimental results using the Bot-IoT dataset showed that KNN can detect DDoS, DoS, and Reconnaissance attacks most effectively and efficiently among the applied machine learning algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.7
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• pp.829-836
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• 2019

IoT devices are widely used in network construction and are increasing. If necessary, heterogeneous IoT devices are used for data transmission. This paper proposes to prevent the method of data loss due to differences in throughput when the local network is constructed by Bluetooth 5 and long range network does by LoRa(Long Range). Data loss occur when the data transmits through LoRa, due to the throughputs of Bluetooth 5 faster than that of LoRa. The prevent method proposed by this paper can apply not only Bluetooth 5 and LoRa but heterogeneous IoT devices and expect to prevent data loss due to differences in throughput between heterogeneous IoT devices. Also, this paper shows the simulation result by applying the proposed avoid method. In this paper, two way to the preventive method shows the data transmission ratio and amount of memory that of necessity.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.300-306
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• 2021

Due to the development of IT technology, hardware technologies applied to IoT equipment have recently been developed, so smart systems using low-cost, high-performance RF and computing devices are being developed. However, in the infrastructure environment where a large amount of IoT devices are installed, big data collection causes a load on the collection server due to a bottleneck between the transmitted data. As a result, data transmitted to the data collection server causes packet loss and reduced data throughput. Therefore, there is a need for an efficient big data collection technique in an infrastructure environment where a large amount of IoT devices are installed. Therefore, in this paper, we propose an efficient big data collection technique in an infrastructure environment where a vast amount of IoT devices are installed. As a result of the performance evaluation, the packet loss and data throughput of the proposed technique are completed without loss of the transmitted file. In the future, the system needs to be implemented based on this design.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.37-45
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• 2022

With the development of the Internet of Things (IoT), related network infrastructures require new technologies to protect against threats such as external hacking. This study proposes an L-PBFT consensus algorithm that can protect IoT networks based on a blockchain consensus algorithm. We designed a blockchain (private) model suitable for small networks, tested processing performance for ultra-small/low-power IoT devices, and verified stability. As a result of performance analysis, L-PBFT proved that at least the number of nodes complies with the operation of the consensus algorithm(minimum 14%, maximum 29%) and establishes a trust network(separation of secure channels) different from existing security protocols. This study is a 4th industry convergence research and will be a foundation technology that will help develop IoT device security products in the future.

• Convergence Security Journal
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• v.22 no.4
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• pp.17-24
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• 2022

Recently, machine learning-based cyber attack detection and classification research has been actively conducted, achieving a high level of detection accuracy. However, low-spec IoT devices and large-scale network traffic make it difficult to apply machine learning-based detection models in IoT environment. Therefore, In this paper, we propose an efficient IoT attack detection and classification method through PCA(Principal Component Analysis) and LightGBM(Light Gradient Boosting Model) using datasets collected in a MQTT(Message Queuing Telementry Transport) IoT protocol environment that is also used in the defense field. As a result of the experiment, even though the original dataset was reduced to about 15%, the performance was almost similar to that of the original. It also showed the best performance in comparative evaluation with the four dimensional reduction techniques selected in this paper.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.201-210
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• 2018

High expectations are posed on the blockchain-based internet of things (IoT), in which IoT and blockchain technology is combined to obtain trust in the Internet, where trust appears impossible to obtain. However, applications of current blockchain-based IoT technology to real-world scenarios appears to be significantly more difficult owing to limitations regarding scalability and security. In this paper, the difficulties to implement blockchain networking technologies for IoT and digital businesses are investigated and practical solutions such as sharding, off-chain, de-idetification and P2P crypto-currency exchange are explored. In further work, a blockchain platform for IoTs which provides scalability and security will be implemented according to this research results, and compared with conventional blockchain platforms.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1071-1081
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• 2021

In addition to the current quality control procedures for the weather observation performed by the Korea Meteorological Administration (KMA), this study proposes quality inspection standards for Internet of Things (IoT) urban weather observed data based on machine learning that can be used in smart cities of the future. To this end, in order to confirm whether the standards currently set based on ASOS (Automated Synoptic Observing System) and AWS (Automatic Weather System) are suitable for urban weather, usability was verified based on SKT AWS data installed in Seoul, and a machine learning-based quality control algorithm was finally proposed in consideration of the IoT's own data's features. As for the quality control algorithm, missing value test, value pattern test, sufficient data test, statistical range abnormality test, time value abnormality test, spatial value abnormality test were performed first. After that, physical limit test, stage test, climate range test, and internal consistency test, which are QC for suggested by the KMA, were performed. To verify the proposed algorithm, it was applied to the actual IoT urban weather observed data to the weather station located in Songdo, Incheon. Through this, it is possible to identify defects that IoT devices can have that could not be identified by the existing KMA's QC and a quality control algorithm for IoT weather observation devices to be installed in smart cities of future is proposed.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.140-145
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• 2019

With the development of IT technology, many changes are taking place in the health service environment over the past. However, even if medical technology is converged with IT technology, the problem of medical costs and management of health services are still one of the things that needs to be addressed. In this paper, we propose a model for hospitals that have established the IoT system to efficiently analyze and manage the personal information of users who receive medical services. The proposed model aims to efficiently check and manage users' medical information through an in-house IoT system. The proposed model can be used in a variety of heterogeneous cloud environments, and users' medical information can be managed efficiently and quickly without additional human and physical resources. In particular, because users' medical information collected in the proposed model is stored on servers through the IoT gateway, medical staff can analyze users' medical information accurately regardless of time and place. As a result of performance evaluation, the proposed model achieved 19.6% improvement in the efficiency of health care services for occupational health care staff over traditional medical system models that did not use the IoT system, and 22.1% improvement in post-health care for users who received medical services. In addition, the burden on medical staff was 17.6 percent lower on average than the existing medical system models.