• 융합보안논문지
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• 제19권2호
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• pp.11-19
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• 2019

블록체인은 중앙신뢰 기관의 개입 없이 분산 컴퓨팅 환경에서 데이터를 관리하는 기술이다. 블록체인의 보안성, 효율성, 응용성으로 인하여 현재 금융 분야뿐만 아니라 제조, 문화, 공공 등 다양한 분야에서 블록체인 기술이 활용되고 있다. 그동안 블록체인에서 공격자는 51% 이상의 해시 파워를 갖출 수 없다고 여겨졌지만 최근 이에 대한 공격과 피해사례가 발생하고 있으며, 이기적인 채굴자 공격을 포함한 대용량 컴퓨팅 능력을 갖춘 공격의 빈도가 증가하고 있다. 또한, 일반 컴퓨터와 차원이 다른 성능을 발휘하는 양자컴퓨터의 발전은 블록체인의 새로운 위협이 되고 있다. 본 논문에서는 블록체인 특징과 합의 알고리즘에 대해 소개하고 컴퓨팅 연산력을 이용한 블록체인 공격기법을 설명한다. 그리고, 대용량 컴퓨팅 환경 구축방법과 양자 컴퓨터를 사용하는 공격 알고리즘이 블록체인 보안성에 미치는 영향을 분석한다. 마지막으로, 블록체인의 보안성을 향상하기 위한 대용량 컴퓨팅 공격 보호 기술 및 앞으로의 발전 방향을 제시한다.

• 융합보안논문지
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• 제21권2호
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• pp.19-28
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• 2021

산업의 디지털 전환을 촉진하고 혁신을 가속화하고자 우리나라는 디지털 뉴딜 정책을 추진하고 있다. 그러나 엄격한 기존의 데이터 관련 법제 하에서는 디지털 뉴딜정책을 위한 산업계의 데이터 활용에 여전히 제약이 있는 상황이다. 이러한 문제를 해결하기 위해서 데이터 3법 개정안이 발의되었으나 실제로 산업계의 데이터 이용 활성화에 어떠한 영향을 미칠지에 대한 논의는 아직 부족한 상황이다. 이에 본 연구에서는 데이터 3법에 대한 여론의 인식을 분석하여 데이터 3법 개정안의 시사점을 분석하고자 한다. 이를 위하여 데이터 3법 개정안과 관련 연구동향을 분석하고, 빅데이터 분석 기법을 이용하여 데이터 3법에 대한 인식을 분석하였다. 분석결과에 따르면 데이터 3법은 개정 취지에 부합하게 데이터 산업 활성화를 촉진하는 반면에 특정산업 분야에 치중될 우려가 있는 것으로 나타났다. 본 연구의 결과는 빅데이터 분석을 통해 시행 초기인 데이터 3법의 산업영향에 대한 온라인 인식을 분석함으로써 향후 개선방안에 대한 시사점을 제공한다는 점에서 의의가 있다.

• 정보보호학회논문지
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• 제16권6호
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• pp.111-121
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• 2006

인터넷이 빠르게 성장함에 따라 네트워크 공격기법이 변화되고 새로운 공격 형태가 나타나고 있다. 네트워크상에서 알려진 침입의 탐지는 효율적으로 수행되고 있으나 알려지지 않은 침입에 대해서는 오탐지(false negative)나 과탐지(false positive)가 너무 높게 나타난다. 또한, 네트워크상에서 지속적으로 처리되는 대량의 패킷에 대하여 실시간적인 탐지와 새로운 침입 유형에 대한 대응방법과 인지능력에 한계가 있다. 따라서 다양한 대량의 트래픽에 대해서 탐지율을 높이고 과탐지를 감소할 수 있는 방법이 필요하다. 본 논문에서는 네트워크 기반의 이상 침입 탐지 시스템에서 과탐지를 감소하고, 침입 탐지 능력을 향상시키기 위하여 다차원 연관 규칙 마이닝과 수정된 부정 선택 알고리즘(Negative Selection Algorithm)을 결합한 다중 레벨 이상 침입 탐지 기술을 제안한다. 제안한 알고리즘의 성능 평가를 위하여 기존의 이상 탐지 알고리즘과 제안된 알고리즘을 수행하여, 각각의 과탐지율을 평가, 제시하였다.

• 한국정보시스템학회지:정보시스템연구
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• 제30권4호
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• pp.277-301
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• 2021

Purpose The purpose of this study is to analyze the sentiment responses of the general public to non-face-to-face work using text mining methodology. As the number of non-face-to-face complaints is increasing over time, it is difficult to review and analyze in traditional methods such as surveys, and there is a limit to reflect real-time issues. Approach This study has proposed a method of the research model, first by collecting and cleansing the data related to non-face-to-face work among tweets posted on Twitter. Second, topics and keywords are extracted from tweets using LDA(Latent Dirichlet Allocation), a topic modeling technique, and changes for each section are analyzed through DTM(Dynamic Topic Modeling). Third, the complaints of non-face-to-face work are analyzed through the classification of positive and negative polarity in the COVID-19 section. Findings As a result of analyzing 1.54 million tweets related to non-face-to-face work, the number of IDs using non-face-to-face work-related words increased 7.2 times and the number of tweets increased 4.8 times after COVID-19. The top frequently used words related to non-face-to-face work appeared in the order of remote jobs, cybersecurity, technical jobs, productivity, and software. The words that have increased after the COVID-19 were concerned about lockdown and dismissal, and business transformation and also mentioned as to secure business continuity and virtual workplace. New Normal was newly mentioned as a new standard. Negative opinions found to be increased in the early stages of COVID-19 from 34% to 43%, and then stabilized again to 36% through non-face-to-face work sentiment analysis. The complaints were, policies such as strengthening cybersecurity, activating communication to improve work productivity, and diversifying work spaces.

• Journal of Radiation Protection and Research
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• 제47권2호
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• pp.67-76
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• 2022

Background: We conducted a cross-sectional study of residents within and outside Fukushima Prefecture to clarify their perceptions of the need for smartphone applications (apps) for explaining exposure doses. The results will lead to more effective methods for identifying target groups for future app development by researchers and municipalities, which will promote residents' understanding of radiological situations. Materials and Methods: In November 2019, 400 people in Fukushima Prefecture and 400 people outside were surveyed via a web-based questionnaire. In addition to basic characteristics, survey items included concerns about radiation levels and intention to use a smartphone app to keep track of exposure. The analysis was conducted by stratifying responses in each region and then cross-tabulating responses to concerns about radiation levels and intention to use an app by demographic variables. The intention to use an app was analyzed by binomial logistic regression analysis. Text-mining analyses were conducted in KH Coder software. Results and Discussion: Outside Fukushima Prefecture, concerns about the medical exposure of women to radiation exceeded 30%. Within the prefecture, the medical exposure of women, purchasing food products, and consumption of own-grown food were the main concerns. Within the prefecture, having children under the age of 18, the experience of measurement, and having experience of evacuation were significantly related to the intention to use an app. Conclusion: Regional and individual differences were evident. Since respondents differ, it is necessary to develop and promote app use in accordance with their needs and with phases of reconstruction. We expect that a suitable app will not only collect data but also connect local service providers and residents, while protecting personal information.

• 한국의류산업학회지
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• 제24권5호
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• pp.593-604
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• 2022

The purpose of this study is to analyze the flow of change in perception of metaverse before and after COVID-19 through big data analysis. This research method used Textom to collect all data, including metaverse for two years before COVID-19 (2018.1.1~2019.11.30) and after COVID-19 outbreak (2020.1.11~2021.12.31), and the collection channels were selected by Naver and Google. The collected data were text mining, and word frequency, TF-IDF, word cloud, network analysis, and emotional analysis were conducted. As a result of the analysis, first, hotels, weddings, and glades were commonly extracted as social issues related to metaverse before and after COVID-19, and keywords such as robots and launches were derived, so the frequency of keywords related to hotels and weddings was high. Second, the association of the pre-COVID-19 metaverse keywords was platform-oriented, content-oriented, economic-oriented, and online promotion-oriented, and post-COVID-19 clusters were event-oriented, ontact sales-oriented, stock-oriented, and new businesses. Third, positive keywords such as likes, interest, and joy before COVID-19 were high, and positive keywords such as likes, joy, and interest after COVID-19. In conclusion, through this study, it was found that metaverse has firmly established itself as a new platform business model that can be used in various fields such as tourism, travel, festivals, and education using smart technology and metaverse.

• 대한한방내과학회지
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• 제44권1호
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• pp.35-52
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• 2023

Objective: Internal injuries and consumptive disease have different causes, yet they can affect each other. The relationship and combination of prescription drugs in the clinical practice of internal injuries and consumptive disease were analyzed for various diseases of "Donguibogam" through network analysis. Methods: The prescriptions used in consumptive disease and internal injury were established by conducting a full survey on the papers extracted from Donguibogam. The R version 4.0.3 (2020-10-10) and the igraph and arules package were used to perform network analysis and association rule relationship mining analysis in the first and second prescription compositions. Results: The herb frequently used for internal injury was Glycyrrhizae Radix, while the herb combination frequently used was Citri Pericarpium-Glycyrrhizae Radix. For centrality, the main factor was generally Glycyrrhizae Radix. In the case of consumptive disease, the herb most frequently used was Angelicae Gigantis Radix, and the combination most frequently used was Rehmanniae Radix Preparata-Angelicae Gigantis Radix. In terms of centrality, it was Angelicae Gigantis Radix. As a result of the network analysis of herbal prescription frequency, each group was divided into three. Conclusion: The interrelationship between internal injury and consumptive disease prescription drugs may reveal the differences and similarities between internal injury and consumptive disease and may serve as a basis for the development of new drugs or materials that can enhance mutual effectiveness in the treatment of internal injury and consumptive diseases.

• 한국ITS학회 논문지
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• 제21권6호
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• pp.36-56
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• 2022

국내에서 발생하는 교통사고는 손해보험협회에서 작성한 「자동차사고 과실비율 인정기준」에 따라 과실비율을 산정하며, 이를 통해 보험사의 합의나 판결이 내려진다. 하지만, 과실비율 산정에 있어 분쟁이 빈번하게 일어나고 있다. 따라서, 교통사고 발생 시 경찰공무원에 의해 작성되는 교통사고 정보를 이용하여 「자동차사고 과실비율 인정기준」 상의 교통사고 유형을 신속하게 확인할 수 있다면, 보다 효과적인 대응이 가능할 것으로 사료된다. 이에 본 연구에서는 경찰에 의해 작성된 교통사고 정보를 학습시켜 「자동차사고 과실비율 인정기준」 에서 제시하는 교통사고 유형으로 분류하는 모델을 개발하고자 한다. 특히, 데이터마이닝을 통해 경찰청 교통사고 데이터에서 「자동차사고 과실비율 인정기준」 의 교통사고 유형으로 분류하는 데 필요한 핵심어들을 추출하였다. 그리고, 키워드를 의사결정나무 및 랜덤 포레스트 모델을 통해 학습시켜 교통사고 유형을 도출하는 모델을 개발하였다.

• 한국산업정보학회논문지
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• 제15권4호
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• pp.17-27
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• 2010

주요 IT 벤더들은 클라우드 컴퓨팅 기술을 이용하여 설치과정 생략, 운용비용 절감, 서비스품질 등에 중점을 두어 대규모 콘텐츠 서비스를 제공하고 있다. 반면에, 대규모 콘텐츠 데이터의 가공, 분석을 수행하는 데이터 처리 프로세스는 처리 시간의 단축을 위한 방법론이 요구되고 있다. 이에 본 논문에서는 클라우드 환경에서 대규모 콘텐츠를 위한 효율적인 자원처리 기법(Efficient_Cloud_Processing_Scheme : ECPS)을 제안한다. 제안한 기법은 리소스 확장 방안을 CPU 및 스토리지 등의 인프라스트럭처 단계에서 설계한다. 대규모 콘텐츠에 대한 자원 할당 방안을 Hadoop 플랫폼 기반의 MapReduce 프로그래밍 기법과 데이터마이닝 분야에서 숨겨진 패턴을 탐지하는데 사용되는 연관규칙을 이용하여 제시한다. 기존 설정값으로 자원을 할당하여 비교하여 ECPS기법을 적용한 결과, 제안 기법이 20% 이상의 성능 및 속도가 향상되었음을 확인하였다.

• 한일민족문제연구
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• 제33호
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• pp.67-111
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• 2017

This article aims to shed light on the wartime labor mobilization of prisoners on a large scale in/across colonial Korea and beyond during the late wartime period. More specifically, this article reveals the logic and mode of mobilization, and sorts out nationwide mobilization cases in colonial Korea. To this end, this article draws on documents and magazines published by the criminal administration of the Japanese Government-General of Korea, as well as the memoirs of prisoners and prison staff including prison administrators and prison chaplains. With the onset of the wartime system, the labor work in prisons centered on the production of military supplies. In 1943, the labor mobilization began to organize the National Protection Corps and dispatch them to remote workplaces. For example, at the requests of the military, prisoners were selected and sent to Hainan Island, while others were sent to military factories and mining fields in the northern part of the country. The authorities specified and adjusted the criteria for imprisonment based on education, physical strength, and other physical and mental conditions. Unconverted ideological offenders were excluded from the mobilization, and instead put under separate control. In preparation for mobilization, the prisoners trained in military drills, received Japanese language education, and underwent assimilation as imperial subjects through the preaching in prison. In order to induce prisoners to volunteer, a legislation system based on the shortening of the prison terms, including the parole system, was also promoted under the wartime system. As a result, prisoners were forced to work harder and faster even under the lowest of wages, poor food and poor housing conditions, and they also filled vacancies in managerial positions by serving as supervisory assistants. The reward system for them, however, did not function properly towards the end of the war, and the number of escapes and infectious outbreaks, as well as mortality rates rapidly increased under the harsh conditions.