• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.1001-1010
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• 2021

Smart factories are complex systems which combine latest information technology (IT) with operation technology (OT). A smart factory aims to provide manufacturing capacity improvement, customized production, and resource reduction with these complex technologies. Although the smart factory is able to increase the efficiency through the technologies, the security level of the whole factory is low due to the vulnerability transfer from IT. In addition, the response and restoration of the business continuity plan are insufficient in case of damage due to the absence of factory security experts. The cope with the such problems, we propose an information security practice content for analyzing the damage by generating ransomware attacks in a digital twin-based smart factory similar to the real world. In our information security content, we introduce our conversion technique of physical devices into virtual machines or simulation models to build a practical environment for the digital twin. This content generates two types of the ransomware attacks according to a defined scenario in the digital twin. When the two generated attacks are successfully completed, at least 8 and 5 of the 23 virtual elements are take damage, respectively. Thus, our proposed content directly identifies the damage caused by the generation of two types of ransomware in the virtual world' smart factory.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.330-336
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• 2021

In bistatic sonar operation, the scattering strength of a sonar target is characterized by the probe signal frequency, the aspect angle and the bistatic angle. Therefore, the target detection and identification performance of the bistatic sonar may vary depending on how the positions of the target, sound source, and receiver are changed during sonar operation. In this study, it was evaluated which variable is advantageous to change by comparing the target identification performance between the case of changing the aspect angle and the case of changing the bistatic angle during the operation. A scenario of identifying a hollow sphere and a cylinder was assumed, and performance was compared by classifying two targets with a support vector machine and comparing their accuracy using a finite element method-based acoustic scattering simulation. As a result of comparison, using the scattering strength defined by the frequency and the bistatic angle with the aspect angle fixed showed superior average classification accuracy. It means that moving the receiver to change the bistatic angle is more effective than moving the sound source to change the aspect angle for target identification.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.339-346
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• 2023

The uncertainties of microstructural features affect the properties of materials. Numerous pores that are randomly distributed in materials make it difficult to predict the properties of the materials. The distribution of pores in cementitious materials has a great influence on their mechanical properties. Existing studies focus on analyzing the statistical relationship between pore distribution and material responses, and the correlation between them is not yet fully determined. In this study, the mechanical response of cementitious materials is predicted through an image-based data approach using a convolutional neural network (CNN), and the correlation between pore distribution and material response is analyzed. The dataset for machine learning consists of high-resolution micro-CT images and the properties (tensile strength) of cementitious materials. The microstructures are characterized, and the mechanical properties are evaluated through 2D direct tension simulations using the phase-field fracture model. The attributes of input images are analyzed to identify the spot with the greatest influence on the prediction of material response through CNN. The correlation between pore distribution characteristics and material response is analyzed by comparing the active regions during the CNN process and the pore distribution.