• Review of KIISC
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• v.29 no.2
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• pp.29-35
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• 2019

CPS(Cyber Physical System)에 대한 사이버 공격이 다양해지고 고도화됨에 따라 시그니쳐에 기반한 악성행위 탐지는 한계가 있어 기계학습 기반의 정상행위 학습을 통한 비정상행위 탐지 기법이 많이 연구되고 있다. 그러나 CPS 보안 연구는 보안상의 이유로 CPS 데이터가 주로 외부에 공개되지 않으며 또한 실제 비정상행위를 가동 중인 CPS에 실험하는 것이 불가능하여 개발 기법의 검증이 어려운 문제가 있다. 이를 해결하기 위해 2015년 SUTD(Singapore University of Technology and Design)의 iTrust 연구소에서 SWaT(Secure Water Treatment) 테스트베드를 구성하고 36가지의 공격을 수행한 데이터셋을 공개하였다. 이후 국 내외에서 SWaT 테스트베드 데이터를 사용하여 다양한 보안 기법을 검증한 연구결과가 발표되고 있으며 CPS 보안에 기여하고 있다. 따라서 본 논문에서는 SWaT 테스트베드 데이터 및 SWaT 테스트베드 데이터에 기반한 비정상행위 탐지 연구를 분석한 내용을 설명하고, 이를 통해 CPS 비정상행위 탐지 설계의 주요 요소를 분석하여 제시하고자 한다.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.1
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• pp.50-60
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• 2021

For conventional AESA radars, DC-DC power modules using 300 Vdc have low efficiency, high volume, heavy weight, and high price, which have problems in modularity with T/R module groups. In this paper, to improve these problems, we propose a distributed DC-DC power module with high-voltage 800 Vdc and high-efficiency Step-down Converter. In particular, power requirements for modern and future marine weapons systems and sensors are rapidly evolving into high-energy and high-voltage power systems. The power distribution of the next generation Navy AESA radar antenna is under development with 1000 Vdc. In this paper, the proposed highvoltage, high-efficiency DC-DC power modules increase space(size), weight, power and cooling(SWaP-C) margins, reduce integration costs/risk, and reduce maintenance costs. Reduced system weight and higher reliability are achieved in navy and ground AESA systems. In addition, the proposed architecture will be easier to scale with larger shipboard radars and applicable to other platforms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.691-708
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• 2022

As the technology of machine learning and deep learning became common, it began to be applied to research on anomaly(abnormal) detection of industrial control systems. In Korea, the HAI dataset was developed and published to activate artificial intelligence research for abnormal detection of industrial control systems, and an AI contest for detecting industrial control system security threats is being conducted. Most of the anomaly detection studies have been to create a learning model with improved performance through the ensemble model method, which is applied either by modifying the existing deep learning algorithm or by applying it together with other algorithms. In this study, a study was conducted to improve the performance of anomaly detection with a post-processing method that detects abnormal data and corrects the labeling results, rather than the learning algorithm and data pre-processing process. Results It was confirmed that the results were improved by about 10% or more compared to the anomaly detection performance of the existing model.

• Journal of Korea Water Resources Association
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• v.40 no.5
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• pp.419-430
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• 2007

Integrated modelling of surface water and groundwater has become important to satisfy the growing demands for sustainable water resources and improved water quality. In this study, the integrated model of the semi-distributed watershed model, SWA T and the fully-distributed groundwater flow model, MODFLOW is applied to Musirn river basin for the purpose of investigating its applicability to reproduce watershed-scale hydrological processes. This objective is accomplished by first demonstrating good agreement between the simulated discharge hydrographs with the measured hydrographs for the period of 2001 -2004 while simultaneously calibrating the calculated groundwater level distribution to observation wells. Next, the integrated model is used to evaluate the effect of different temporal precipitation averages on hydrodynamic processes of streamflow, percolation, recharge and groundwater discharge. Moreover, comprehensive simulations are performed to present the relationships between monthly precipitation and each hydrological component, and to analyze the temporal-spatial variability of recharge. The results show that the components are highly interrelated, and that the heterogeneity of watershed characteristics such as subbasin slope, land use, soil type causes a significant spatial variation of recharge. Overall it is concluded that the model is capable of reproducing the temporally and spatially varied surface and subsurface hydrological processes at the watershed scale.