• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.267-272
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• 2021

The end of the ROK-U.S. missile guidelines opened up the possibility of developing space launch vehicles for various platforms based on air and sea. In particular, the air-based space launch vehicle is an essential space power projection capability compared to the ground-based space launch vehicle in consideration of the geographical location of the Korean Peninsula, such as the deployment of various satellite orbits and the timely launch of satellite. In addition, compared to the ground-based launch vehicle, the cost reduction effect is large, and it has the merit of energy gain because it can be launched with the advantage of the aircraft's altitude and speed. Therefore, in this paper, the necessity of air-based space launch vehicle in the strategic environment of the Korean Peninsula is clearly presented, and through technology trend analysis of various air launch vehicle, the three methods are proposed to have the most efficient air-based space launch vehicle capability in the Korean situation.

• Analyses & Alternatives
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• v.5 no.1
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• pp.25-57
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• 2021

As the strategic competition between the United States and China for global hegemony intensifies, China is using economic sanctions against other countries more and more frequently. Republic of Korea, which has China as its largest trading partner but is an ally of the United States, is more likely to be a target of economic sanctions, as seen in China's retaliation toward its deployment of a THAAD missile-defense system. Against the background, this paper analyzes China's economic sanctions, especially focusing on its informality. China does not publicly declare economic sanctions in most cases, such as Korean one, in which the trade structure is in its favor and can take advantage of its position as a big buyer with huge markets. However, China responds in a more open and formal manner when it is related to its core interests, when it is impossible to exert substantial sanctions effect and when mutual disputes intensify and cannot maintain informality. Korea, which is vulnerable to China's informal economic sanctions, should prepare for them by analyzing the characteristics of China's economic sanctions in depth and thinking about various strategies and measures in advance.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.249-255
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• 2022

This study proposed an analysis framework for real-time prediction of CNC processing defects using machine learning-based models that are recently attracting attention as processing defect prediction methods, and applied it to CNC machines. Analysis shows that the XGBoost, CatBoost, and LightGBM models have the same best accuracy, precision, recall, F1 score, and AUC, of which the LightGBM model took the shortest execution time. This short run time has practical advantages such as reducing actual system deployment costs, reducing the probability of CNC machine damage due to rapid prediction of defects, and increasing overall CNC machine utilization, confirming that the LightGBM model is the most effective machine learning model for CNC machines with only basic sensors installed. In addition, it was confirmed that classification performance was maximized when an ensemble model consisting of LightGBM, ExtraTrees, k-Nearest Neighbors, and logistic regression models was applied in situations where there are no restrictions on execution time and computing power.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.637-645
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• 2022

Cloud computing has been gaining popularity over decades, and container, a technology that is primarily used in cloud native applications, is also drawing attention. Although container technologies are lighter and more capable than conventional VMs, there are several security threats, such as sharing kernels with host systems or uploading/downloading images from the image registry. one of which can refer to the integrity of container images. In addition, runtime security while the container application is running is very important, and monitoring the behavior of the container application at runtime can help detect abnormal behavior occurring in the container. Therefore, in this paper, first, we implement a signing checker that automatically checks the signature of an image based on the existing Docker Content Trust (DCT) technology to ensure the integrity of the container image. Next, based on falco, an open source project of Cloud Native Computing Foundation(CNCF), we introduce newly created image for the convenience of existing falco image, and propose implementation of docker-compose and package configuration that easily builds a monitoring system.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.699-707
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• 2023

DevSecOps is a concept that adds security procedures to the operational procedures of DevOps to respond to the short development and operation cycle. Multi-step vulnerability scanning process should be considered to provide reliable security while supporting rapid development and deployment cycle in DevSecOps. Many open-source vulnerability scanning tools available can be used for each stage of scanning, but there are difficulties in evaluating the security level and identifying the importance of information in integrated operation due to the various functions supported by the tools and different security results. This paper proposes an integrated security metric design plan for scurity results and the combination of open-source scanning tools that can be used in security stage when building the open-source based DevSecOps system.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3409-3416
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• 2023

Licensing the next-generation of nuclear reactor designs requires extensive use of Modeling and Simulation (M&S) to investigate system response to many operational conditions, identify possible accidental scenarios and predict their evolution to undesirable consequences that are to be prevented or mitigated via the deployment of adequate safety barriers. Deep Learning (DL) and Artificial Intelligence (AI) can support M&S computationally by providing surrogates of the complex multi-physics high-fidelity models used for design. However, DL and AI are, generally, low-fidelity 'black-box' models that do not assure any structure based on physical laws and constraints, and may, thus, lack interpretability and accuracy of the results. This poses limitations on their credibility and doubts about their adoption for the safety assessment and licensing of novel reactor designs. In this regard, Physics Informed Neural Networks (PINNs) are receiving growing attention for their ability to integrate fundamental physics laws and domain knowledge in the neural networks, thus assuring credible generalization capabilities and credible predictions. This paper presents the use of PINNs as surrogate models for accidental scenarios simulation in Nuclear Power Plants (NPPs). A case study of a Loss of Heat Sink (LOHS) accidental scenario in a Nuclear Battery (NB), a unique class of transportable, plug-and-play microreactors, is considered. A PINN is developed and compared with a Deep Neural Network (DNN). The results show the advantages of PINNs in providing accurate solutions, avoiding overfitting, underfitting and intrinsically ensuring physics-consistent results.

• Journal of Internet of Things and Convergence
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• v.9 no.3
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• pp.21-26
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• 2023

The development and distribution approach of applications is transitioning from a monolithic architecture to microservices and containerization, a lightweight virtualization technology, is becoming a core IT technology. However, unlike traditional virtual machines based on hypervisors, container technology does not provide concrete security boundaries as it shares the same kernel. According to various preceding studies, there are many security vulnerabilities in most container images that are currently shared. Accordingly, attackers may attempt exploitation by using security vulnerabilities, which may seriously affect the system environment. Therefore, in this study, we propose an efficient automated deployment pipeline design to prevent the distribution of container images with security vulnerabilities, aiming to provide a secure container environment. Through this approach, we can ensure a safe container environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.447-448
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• 2021

The boundary-based security architecture has the advantage of easy deployment of security solutions and high operational efficiency. The boundary-based security architecture is easy to detect and block externally occurring security threats, but is inappropriate to block internally occurring security threats. Unfortunately, internal security threats are increasing in frequency. In order to solve this problem, a zero trust model has been proposed. The zero trust model requires a real-time monitoring function to analyze the behavior of a subject accessing various information resources. However, there is a limit to real-time monitoring of file access of a subject confirmed to be trusted in the system. Accordingly, this study proposes a method to monitor user's file access in real time. To verify the effectiveness of the proposed monitoring method, the target function was verified after the demonstration implementation. As a result, it was confirmed that the method proposed in this study can monitor access to files in real time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.1-6
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• 2023

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.