• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.675-684
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• 2019

Under the wave of the Fourth Industrial Revolution, developed countries around the world recognize Smart Factory as a core base and strive to enhance the nation's industrial competitiveness through related policies and industry development. Domestic ministries have also set up a strategy for manufacturing innovation 3.0 and are pushing for the expansion of smart factories with 30,000 targets by 2025. In this study, we analyze the practical cases of smart factory security related companies and present the application methods for the same industry. we also intend to contribute to the protectetion of important information in Smart Factory and stable operation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.224-226
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• 2021

Recently, in order to increase the efficiency of the product production process, the automation of facilities and devices in the factory is in progress, and a smart factory is being built using ICT and IoT technologies. In order to organically solve many problems occurring in the smart factory, a system for monitoring the wireless communication function between facilities and devices and the manufacturing process environment of the smart factory is required. In this paper, we propose a monitoring system using a Bluetooth module, a temperature/humidity sensor and a fine dust sensor to remotely monitor the process environment of a smart factory. The proposed monitoring system collect Arduino sensor values wirelessly through Bluetooth communication.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.73-76
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• 2020

본 논문은 생산 현장에서 필요로 하는 다양한 스마트팩토리 서비스를 현장 근로자가 직접 기획, 설계, 구현 및 적용 가능한 서비스 플랫폼을 제안한다. 이를 위하여 오픈 하드웨어 개발 도구 등을 활용한 IoT 기반 제조데이터 수집과 이를 활용하여 서비스 화면을 구성할 수 있는 개발도구를 설계하고 구현하였으며, 구현된 프로그램으로부터 제조데이터 기반의 다양한 현장 서비스를 근로자가 직접 만들고 배포할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.442-443
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• 2022

As the recent trend of factories has changed from analog to smart factory, there are various functions that conveniently use smart factory. This paper introduces various techniques for predicting demand within smart factories among the functions of smart factories.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.187-189
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• 2023

IoT(Internet of Things), 빅데이터, AI(Artificial Intelligence), 클라우드와 같은 ICT(Information and Communications Technology) 기술이 발전함에 따라 ICT와 제조기술이 융합된 스마트팩토리가 발전하고 있다. 이는 2개의 영역과 5개의 계층으로 구성되어 기타 환경들과 상이한 구조를 가지고 있으며, 각 영역·계층별 발생 가능한 보안위협도 상이하다. 또한, 각 영역과 계층이 연결됨에 따라 발생 가능한 보안위협이 증가하고 있으며, 이에 대한 효율적인 대응을 위하여 스마트팩토리 영역·계층별 환경을 고려한 대응체계 마련이 필요한 실정이다. 따라서, 본 논문에서는 스마트팩토리 영역·계층별 발생 가능한 보안위협을 분석하고, 이에 대응하기 위한 대응체계 도출 기법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.158-161
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• 2023

스마트팩토리는 기존 제조산업과 ICT(Information & Communication Technology)가 융합된 지능형 공장이다. 이는 직접적인 제조공정 과정이 수행되는 OT(Operational Technology) 영역(0~3계층)과 전사업무 관리를 수행하는 IT(Information Technology) 영역(4~5계층)으로 구분되며, 각 영역과 계층이 연결되어 제조·물류·유통 과정의 자동화 및 지능화를 제공한다. 그러나 각 영역과 계층이 연결됨에 따라 보안위협 벡터가 증가하고 있으며, 다영역·다계층 환경인 스마트팩토리에 적합한 대응체계 연구를 위해 영역별 보안위협 관련 데이터를 통합하여 처리 및 관리하는 아키텍처 연구가 필요한 실정이다. 이에 따라 본 논문에서는 스마트팩토리 환경 내 IT 및 OT 영역 장치를 식별하고 보안위협 관련 데이터 통합 처리 및 관리를 위한 아키텍처를 제안한다.

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.179-189
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• 2018

Currently, research on smart factories is steadily being carried out in terms of implementation strategies and considerations in construction. Various studies have not been conducted on companies that introduced smart factories. This study conducted a questionnaire survey for SMEs applying the basic stage of smart factory. And the cluster analysis was conducted to examine the characteristics of the company. In addition, we conducted Decision Tree and Naive Bay to examine how the characteristics of a company are derived and compare the results. As a result of the cluster analysis, it was confirmed that the group was divided into the high satisfaction group and the low satisfaction group. The decision tree and the Naive Bay analysis showed that the higher satisfaction group has high productivity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.73-79
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• 2021

In the wave of the 4th industrial revolution, smart factory is required in many factories. However, small and mid-sized companies (SMEs) still have aging machines and are having difficulties in the data collection stage, which is the basis of smart factories. This study proposes a low cost monitoring method by using an open source based technology that extracts data from the image of the facility control panel without the need for modification of existing facilities. The proposed method was tested and evaluated for forging facilities in automobile parts manufacturing plants through prototyping. As a result of the evaluation, it was confirmed that low-cost facility monitoring is possible, and it will help SMEs build smart factories.

• Journal of Convergence for Information Technology
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• v.8 no.2
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• pp.233-238
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• 2018

SMEs and small enterprises are making various attempts to manage SMEs in terms of equipment, safety and energy management as well as production management. However, SMEs do not have the investment capacity and it is not easy to build a smart factory to improve management and productivity of SMEs. In this paper, we propose a smart factory construction algorithm that partially integrates the factory equipment currently operated by SMEs. The proposed algorithm supports collection, storage, management and processing of product information and release information through IoT device during the whole manufacturing process so that SMEs' smart factory environment can be constructed and operated in stages. In addition, the proposed algorithm is characterized in that central server manages authentication information between devices to automate the linkage between IoT devices regardless of the number of IoT devices. As a result of the performance evaluation, the proposed algorithm obtained 13.7% improvement in the factory process and efficiency before building the Smart Factory environment, and 19.8% improvement in the processing time in the factory. Also, the cost of input of manpower into process process was reduced by 37.1%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1467-1482
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• 2017

Recently, Interests on The Fourth Industrial Revolution has been increased. In the manufacturing sector, the introduction of Smart Factory, which automates and intelligent all stages of manufacturing based on Cyber Physical System (CPS) technology, is spreading. The complexity and uncertainty of smart factories are likely to cause unexpected problems, which can lead to manufacturing process interruptions, malfunctions, and leakage of important information to the enterprise. It is emphasized that there is a need to perform systematic management by analyzing the threats to the Smart Factory. Therefore, this paper systematically identifies the threats using the STRIDE threat modeling technique using the data flow diagram of the overall production process procedure of Smart Factory. Then, using the Attack Tree, we analyze the risks and ultimately derive a checklist. The checklist provides quantitative data that can be used for future safety verification and security guideline production of Smart Factory.