• Journal of the Korea Convergence Society
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• v.12 no.3
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• pp.53-59
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• 2021

When using Medical Information Retrieval services, a typical retrieval method is to use the Anatomic Therapyutic Chemical Classification (ATC) code. Traditional ATC code-based medical information retrieval is very useful for single ingredient product retrieval with single ingredient. However, in the case of complex, retrieval errors often occur. The cause of this problem is that ATC code-based retrieval proceeds by pattern matching ATC code.In this work, we design the mapping scheme based on ATC code by analyzing the requirement scenarios for retrieval based on main ingredient in ATC code-based retrieval. the mapping scheme based on ATC is a schema that stores the ATC code of the complex and all the ATC code of the single agent included in the complex. ATC code-based retrieval using this schema retrieves a complex as ingredient of a single ingredient product, thus having higher accuracy than existing methods. the mapping scheme based on ATC is expected to increase the efficiency of doctors' prescription of patients and increase the accuracy of drug safety use services.

• Review of KIISC
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• v.33 no.1
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• pp.13-21
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• 2023

NIST는 2022년 양자내성암호 표준화 진행 대상 알고리즘으로 KEM 1종(CRYSTALS-Kyber), 전자서명3종(CRYSTALS-Dilithium, FALCON, SPHINCS+)을 발표하였고, 추가로 KEM 4종(Classic McEliece, HQC, BIKE, SIKE)에 대한 Round 4 진행을 공표하였다. Round 3와 마찬가지로 Round 4에서도 부채널 분석 및 오류 주입에 대한 안전성은 알고리즘 선정에 있어 중요 평가 사항 중 하나이다. 따라서 해당 암호 알고리즘에 대한 새로운 부채널 분석기술에 대한 연구가 활발히 진행되고 있다. 본 논문은 Round 4의 암호 알고리즘 중 코드 기반 알고리즘 3종(Classic McEliece, HQC, BIKE)에 대한 부채널 분석 방법론의 동향을 파악하고 향후 연구 방향을 제시한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.283-286
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• 2017

소프트웨어 내제되어 있는 보안약점과 보안취약점으로 인해 사회적으로 많이 비용이 발생함에 따라 안전한 소프트웨어를 개발하고자하는 요구가 증가하고 있다. 최근 바이너리 코드에 내제된 보안약점을 분석하기 위해서 중간코드를 이용하여 정적분석을 수행하는 다양한 연구가 진행되고 있다. 중간 언어를 사용함으로 실행환경에 따라 달라지는 바이너리 코드가 중간언어로만 변환이 된다면 동일한 형태의 보안약점 분석기술을 통해 효과적인 수행이 가능하다. 이 기술의 핵심은 바이너리 코드로부터 얼마나 코드내의 자료형 및 변수를 재구성하여 중간언어로 변환하는 것이다. 본 논문에서는 이와 같은 바이너리 코드로부터 보안약점 분석을 위한 중간언어 변환시 효과적으로 자료형 및 변수 등에 관한 정보를 재구성하는 기법들에 대해서 조사 분석하였다.

• Journal of the Korean Society of Propulsion Engineers
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• v.23 no.6
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• pp.59-71
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• 2019

The numerical analysis of ablative thermal protection systems (TPS) for solid rockets has been carried out with various in-house codes since the 1960s. However, the application scope of commercial codes has been expanded by adding subroutines and user-defined functions (UDF) to codes such as Fluent, Marc, and ABAQUS. In the past, the flow, thermal response and structural analysis of TPS have been performed using separate approaches. Recently, research has been conducted to interrelate them. In this paper, the thermal response characteristics of thermal protection materials, the in-house codes for thermal response analysis, and the research trends of flow-thermal-structure analysis of TPS using commercial codes were reviewed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.381-390
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• 2022

Many attempts have been made to apply image recognition based on machine learning which has recently advanced dramatically to malware detection. They convert executable files to images and train deep learning networks like CNN to recognize or categorize dangerous executable files, which shows promising results. In this study, we are looking for an effective image generation method that may be used to identify malware using machine learning. To that end, we experiment and assess the effectiveness of various image generation methods in relation to malware detection. Then, we suggest a linear image creation method which represents control flow more clearly and our experiment shows our method can result in better precision in malware detection.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.98-98
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• 2023

ChatGPT(Chat과 Generative Pre-trained Transformer의 합성어)는 사용자와 주고받는 대화의 과정을 통해 질문에 답하도록 설계된 대형언어모델로, 지도학습과 강화학습을 모두 사용하여 세밀하게 조정된 인공지능 챗봇이다. ChatGPT는 주고받은 대화와 대화의 문맥을 기억할 수 있으며, 보고서나 실제로 작동하는 파이썬 코드를 비롯한 인간과 유사하게 상세하고 논리적인 글을 만들어 낼 수 있다고 알려져있다. 본 연구에서는 수자원시스템분야의 문제해결에 있어 ChatGPT의 적용가능성을 사례기반으로 확인하고, ChatGPT의 올바른 활용을 위해 필요한 사항에 대해 고찰하였다. 수자원시스템분야의 대표적인 연구주제인 상수관망시스템의 누수인지와 수리해석을 통한 문제해결에 ChatGPT를 활용하였다. 즉, 딥러닝 기반의 데이터분석을 활용한 누수인지와 오픈소스기반의 수리해석 모델을 활용한 관망시스템 적정 분석을 목표로 ChatGPT와 대화를 진행하고, ChatGPT에 의해 제안된 코드를 구동하여 결과를 분석하였다. ChatGPT가 제시한 코드의 구동결과를 사전에 연구자가 직접 구현한 코드구동 결과와 비교분석하였다. 분석결과 ChatGPT가 제시한 코드가 보다 더 간결할 수 있으며, 상대적으로 경쟁력 있는 결과를 도출하는 것을 확인하였다. 다만, 상대적으로 간결한 코드와 우수한 구동결과를 획득하기 위해서는 해당 도메인의 전문적 지식을 바탕으로 적절한 다수의 질문을 해야 하며, ChatGPT에 의해 작성된 코드의 의미를 명확히 해석하거나 비판적 분석을 하기 위해서는 전문가지식이 반드시 필요함을 알 수 있었다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.4
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• pp.847-857
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• 2018

As the number of malicious code increases steeply, cyber attack victims targeting corporations, public institutions, financial institutions, hospitals are also increasing. Accordingly, academia and security industry are conducting various researches on malicious code detection. In recent years, there have been a lot of researches using machine learning techniques including deep learning. In the case of research using Convolutional Neural Network, ResNet, etc. for classification of malicious code, it can be confirmed that the performance improvement is higher than the existing classification method. However, one of the characteristics of the target attack is that it is custom malicious code that makes it operate only for a specific company, so it is not a form spreading widely to a large number of users. Since there are not many malicious codes of this kind, it is difficult to apply the previously studied machine learning or deep learning techniques. In this paper, we propose a method to classify malicious codes when the amount of samples is insufficient such as targeting type malicious code. As a result of the study, we confirmed that the accuracy of 97% can be achieved even with a small amount of data by applying the Memory Augmented Neural Networks model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.593-603
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• 2020

The advent of malicious code has increased exponentially due to the spread of malicious code generation tools in accordance with the development of the network, but there is a limit to the response through existing malicious code detection methods. According to this situation, a machine learning-based malicious code detection method is evolving, and in this paper, the feature of data is extracted from the PE header for machine-learning-based malicious code detection, and then it is used to automate the malware through autoencoder. Research on how to extract the indicated features and feature importance. In this paper, 549 features composed of information such as DLL/API that can be identified from PE files that are commonly used in malware analysis are extracted, and autoencoder is used through the extracted features to improve the performance of malware detection in machine learning. It was proved to be successful in providing excellent accuracy and reducing the processing time by 2 times by effectively extracting the features of the data by compressively storing the data. The test results have been shown to be useful for classifying malware groups, and in the future, a classifier such as SVM will be introduced to continue research for more accurate malware detection.

• Journal of KIISE:Information Networking
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• v.35 no.1
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• pp.1-10
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• 2008

Once the sensor node in wireless sensor networks is installed, it usually operates without human intervention for a long time. The remote code update scheme is required because it is difficult to recall the sensor node in many situations. Therefore, studies on the reliable and efficient transport protocol for code propagation in wireless sensor networks have been increasingly done. However, by considering only the stability aspect of transmission, most of previous works ignore the consideration on the fast code propagation. This results the energy inefficiency by consuming unnecessary energy due to the slow code propagation. In this paper, in order to overcome limitation of the previous code propagation protocols, we propose a new code propagation protocol called "FCPP(Fast Code Propagation Protocol)". The FCPP aims at improving the reliability at well as performance. For this purpose, the FCPP accomplishes the fast code propagation by using the RTT-based transmission rate control and NACK suppression scheme, which provides a better the network utilization and avoids a unnecessary transmission delay. Based on the ns-2 simulation result, we prove that the FCPP Improves significantly both reliability and performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.39-44
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• 2020

Malicious codes cause damage to equipments while avoiding detection programs(vaccines). The reason why it is difficult to detect such these new malwares using the existing vaccines is that they use "signature-based" detection techniques. these techniques effectively detect already known malicious codes, however, they have problems about detecting new malicious codes. Therefore, most of vaccines have recognized these drawbacks and additionally make use of "heuristic" techniques. This paper proposes a technology to detecting unknown malicious code using deep learning. In addition, detecting malware skill using Supervisor Learning approach has a clear limitation. This is because, there are countless files that can be run on the devices. Thus, this paper utilizes Stacked Convolution AutoEncoder(SCAE) known as Semi-Supervisor Learning. To be specific, byte information of file was extracted, imaging was carried out, and these images were learned to model. Finally, Accuracy of 98.84% was achieved as a result of inferring unlearned malicious and non-malicious codes to the model.