• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.150-159
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• 2005

With the increase of interest in ubiquitous computing, image code is attracting attention in various areas. Image code is important in ubiquitous computing in that it can complement or replace RFID (radio frequency identification) in quite a few areas as well as it is more economical. However, because of the difficulty in reading precise colors due to the severe distortion of colors, its application is quite restricted by far. In this paper, we present an efficient method of image code recognition including automatically locating the image code using the hue and saturation values. In our experiments, we use an image code whose design seems most practical among currently commercialized ones. This image code uses six safe colors, i.e., R, G, B, C, M, and Y. We tested for 72 true-color field images with the size of $2464{\times}1632$ pixels. With the color calibration based on the histogram, the localization accuracy was about 96%, and the accuracy of color classification for localized codes was about 91.28%. It took approximately 5 seconds to locate and recognize the image code on a PC with 2 GHz P4 CPU.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.933-943
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• 2022

As the Internet develops and the utilization rate of computers increases, the threats posed by malware keep increasing. This leads to the demand for a system to automatically analyzes a large amount of malware. In this paper, an automatic malware analysis technique using a deep learning algorithm is introduced. Our proposed method uses CNN (Convolutional Neural Network) to analyze the malicious features represented as images. To reflect semantic information of malware for detection, our method uses the opcode frequency data of binary for image generation, rather than using bytes of binary. As a result of the experiments using the datasets consisting of 20,000 samples, it was found that the proposed method can detect malicious codes with 91% accuracy.

• Review of KIISC
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• v.28 no.2
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• pp.33-42
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• 2018

머신러닝 및 인공지능 기술의 발전은 다양한 분야 활용되고 있고, 이는 보안 분야에서도 마찬가지로 로그 분석이나, 악성코드 탐지, 취약점 탐색 및 대응 등 다양한 분야에서 자동화를 위한 연구가 진행되고 있다. 특히 취약점 탐색 및 대응 분야의 경우 2016년 데프콘에서 진행된 CGC를 필두로 바이너리나 소스코드 내의 취약점을 정확하게 탐색하고 패치하기 위해 다양한 연구가 시도되고 있다. 이에 본 논문에서는 취약점을 탐색 및 대응하기 위해 각 연구 별 탐색 기술과 대응 기술을 분류 및 분석한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.169-177
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• 2020

In South Korea, the results of R&D in science and technology are submitted to the National Science and Technology Information Service (NTIS) in reports that have Korea national science and technology standard classification codes (K-NSCC). However, considering there are more than 2000 sub-categories, it is non-trivial to choose correct classification codes without a clear understanding of the K-NSCC. In addition, there are few cases of automatic document classification research based on the K-NSCC, and there are no training data in the public domain. To the best of our knowledge, this study is the first attempt to build a highly performing K-NSCC classification system based on NTIS report meta-information from the last five years (2013-2017). To this end, about 210 mid-level categories were selected, and we conducted preprocessing considering the characteristics of research report metadata. More specifically, we propose a convolutional neural network (CNN) technique using only task names and keywords, which are the most influential fields. The proposed model is compared with several machine learning methods (e.g., the linear support vector classifier, CNN, gated recurrent unit, etc.) that show good performance in text classification, and that have a performance advantage of 1% to 7% based on a top-three F1 score.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.11a
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• pp.33-45
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• 2001

Korean standard industrial/occupational classification has been the basis of producing accurate statistical data related with our industrial structure and distribution of industry and occupation since 1960. But coding over several million records not only requires high cost in the aspects of time and manpower but also has many problems in accuracy and consistency. Therefore, we got to develop the automatic coding system in order to work out these problems of manual coding. This paper shows the structure of our system and the result of experiment over survey data of 2,000 Census.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1579-1582
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• 2003

본 연구는 컴포넌트 검색을 위해서 컴포넌트를 컨덱스트에 의해 패싯 분류하고, 컨텍스트와 특성들간의 관련값에 대한 통계적 분석에 의해 시소러스를 구축하여 다중 패싯 분류된 컴포넌트를 효율적으로 검색할 수 있는 방법을 제안하였다. 소스 코드로부터 추출된 특성은 카이제곱 방법을 통하여 간소화가 이루어지며, E-SARM 방법을 사용하여 컨텍스트의 자동 검색이 이루어질 수 있도록 하였다. 쿼리에 대해 자동 검색된 컨덱스트에 의해 후보 컴포넌트가 선정되고, 쿼리와 컴포넌트 간의 유사도가 계산됨으로써 컴포넌트가 검색될 수 있도록 하였다. 본 연구는 다중 패싯 분류된 컴포넌트의 검색에 효율적이며, 컴포넌트의 재사용성을 높일 수 있도록 하였다.

• Journal of the korean Society of Automotive Engineers
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• v.17 no.1
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• pp.31-43
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• 1995

본 연구에서는 교통부 자동차 등록 원시자료를 이용하여 자동차제작사가 직접 사용할 수 있는 사후관리를 위한 등록통계 시스템을 개발해서 결과를 제시하고자 한다. 특히 통계 활용범위를 극대화하도록 하기 위해 차명은 코드화로 작성하여 통합차명으로 표준화, 업계재편과정으로 인한 종전 자동차 제작사는 합병, 인수한 최종 자동차제작사에 통합, 단산 및 양산 차명별로 차령별분류, 시.군.구의 행정단위별의 통계정보 수록 및 자동차소유자의 구매성형분석을 위한 년령별분류외 15개 유형별로 개발결과를 제시함으로써 효율적이고 과학적인 통계를 산출할 수 있도록 하고자 한다.

• Review of KIISC
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• v.29 no.3
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• pp.22-28
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• 2019

딥러닝 기법이 영상 분야를 시작으로 여러 분야에서 빠르게 적용되고 있고, 관련된 다양한 연구도 함께 같이 발전하고 있다. 정보보안 분야 역시 악성코드를 위주로 다양한 데이터에 대해서 딥러닝 기법을 적용하기 위한 많은 연구들이 진행되고 있지만, 본 논문에서는 IDPS에서 탐지된 이벤트들에 대해서 정/오탐을 자동으로 식별할 수 있는 딥러닝 기반의 분류방법을 소개 하고자 한다.

• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.170-177
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• 2016

Character encoding is a method for changing a document to a binary document file using the code table for storage in a computer. When people decode a binary document file in a computer to be read, they must know the code table applied to the file at the encoding stage in order to get the original document. Identifying the code table used for encoding the file is thus an essential part of decoding. In this paper, we propose a method for detecting the character code of the given binary document file automatically. The method uses many techniques to increase the detection rate, such as a character code range detection, escape character detection, character code characteristic detection, and commonly used word detection. The commonly used word detection method uses multiple word database, which means this method can achieve a much higher detection rate for multi-language files as compared with other methods. If the proportion of language is 20% less than in the document, the conventional method has about 50% encoding recognition. In the case of the proposed method, regardless of the proportion of language, there is up to 96% encoding recognition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.857-865
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• 2020

This paper presents a method for code generation of UML sequence diagrams based on message patterns. In the sequence diagrams, it is shown that messages are some types of forms typically. This paper classifies according to type as three patterns, and construct meta-information for code generation analysing structural infomation for each patterns. The meta-message of structural information (MetaMessage) is stored in the MetaMessage datastore and the meta-method information from the MetaMessage is stored in the MetaMethod datastore. And then, the structural information of MetaClass and MetaObject is constructed in each datastore too. For each pattern, this paper presents a method for code generation based on the meta information of message patterns and the syntax of target progamming language. Also, branching and looping that has been seldom handled integratedly in the previous works are handled as same as the basic patterns by classifying the branching pattern and the looping pattern for code generation integratedly.