• Journal of Convergence Society for SMB
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• v.6 no.3
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• pp.13-19
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• 2016

In various ICBM (IoT, Bigdata, Cloud, Mobile) IT convergence environments, IT technologies have been evolved, new information security threats have been occurred. As information security incidents in major public agencies, financial institutions and companies occurred, it was emphasized that the importance of human security was disclosed. Thus, implementing of information security management system could protect hacks and security breaches and respond quickly to accidents so it minimized the sized of loss. In this paper, comparison of human security controls shown in ISO27001, COBIT, NIST 800-53, K-ISMS, Cyber Security Framework such as the main information security management systems was analyzed, and proposed of the security implications about effective controls of human resources security issues.

• Journal of Convergence for Information Technology
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• v.9 no.11
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• pp.8-14
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• 2019

Due to the development of computer technology, IoT technology is being used in various fields of industry, economy, medical service and education. However, multimedia information processed through IoT equipment is still one of the major issues in the application sector. In this paper, a big data protection model for users of IoT based IoT is proposed to ensure integrity of users' multimedia information processed through IoT equipment. The proposed model aims to prevent users' illegal exploitation of big data information collected through IoT equipment without users' consent. The proposed model uses signatures and authentication information for IoT users in a hybrid cryptographic method. The proposed model feature ensuring integrity and confidentiality of users' big data collected through IoT equipment. In addition, the user's big data is not abused without the user's consent because the user's signature information is encrypted using a steganography-based cryptography-based encryption technique.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.179-183
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• 2020

The online education platform market is developing rapidly after the coronavirus infection-19 pandemic. As school classes at various levels are converted to non-face-to-face classes, interest in non-face-to-face online education is increasing more than ever. However, the majority of online platforms currently used are limited to the fragmentary functions of simply delivering images, voice and messages, and there are limitations to online hands-on training. Indeed, digital transformation is a traditional business method for increasing coding education and a corporate approach to service operation innovation strategy computing thinking power and platform model. There are many ways to evaluate a computer programmer's ability. Generally, piecemeal evaluation methods are used to evaluate results in time through coding tests. In this study, the purpose of this study is to propose a comprehensive evaluation of not only the results of writing, but also the execution process of the results, etc., and to evaluate the programmer's propensity habits based on the programmer's coding experience to evaluate the programmer's ability and productivity.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.171-172
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• 2024

본 논문은 대기 중 탄소 배출로 인한 기후 변화의 분석과 개선 방안을 목적으로 하고 있다. 이 연구는 탄소 배출이 1990년부터 2020년까지 어떻게 변해왔는지를 분석하여, 세계적으로 탄소 절감을 위한 노력으로 18년도부터 탄소 배출량이 점진적으로 감소하는 추세를 확인하였다. 이러한 추세는 앞으로의 탄소 배출으로 인한 기후 변화를 예측하는 데 중요한 정보를 제공한다. 또한, 이를 통해 재산 피해를 최소화하기 위한 예측을 수행하였다. 이러한 연구 결과는 탄소 배출이 줄어들면서 기후 변화의 미래에 긍정적인 영향을 미칠 수 있음을 시사한다. 추가적으로, 더 나은 대기 환경을 위한 노력과 기술적 개선이 필요하며, 이는 우리의 지구를 보다 지속 가능한 방향으로 이끌어 갈 것이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.301-302
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• 2022

본 논문에서는 사용자 정보를 바탕으로, 사용자에게 적합한 다이빙 플랜을 자동으로 매칭하고, 해양생태정보를 수집하는 시스템을 제안한다. 이 시스템은 사용자의 정보를 바탕으로 사용자에게 적합한 다이빙 플랜이 자동으로 매칭되므로, 최적 조건의 다이빙 플랜이 사용자에게 제공될 수 있다. 또한, 해양 생태 정보를 수집하여 데이터화함으로써 해양 생태 변화에 대한 자료가 사용자에게 제공될 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.434-436
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• 2023

본 연구는 관세 데이터를 BERT 기반 모델을 활용한 개체명 인식(NER)모델을 제안한다. 관세 분야 국내 첫 시도이며, 선행연구들과 달리 개체명 인식에 초점을 맞춘다. 관세 관련 텍스트에서 고유한 의미의 개체를 인식하는 것이 주요 목표이다. 이 연구는 관세 분야의 개체명 인식에 대한 이해도를 높이고 향후 HS 코드 검색 시스템 개발에 대한 기초 연구를 제공한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.45-46
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• 2020

최근 의료데이터의 표준화를 기반으로 다양한 임상연구가 국내외에서 활발하게 진행되고 있다. 그러나 대부분 개발기술이 임상현장에 적용되지 못하는 이유는 상이한 인프라로 인한 일관성있는 결가를 도출하지 못하는 문제점과 부족한 진단지표와 기준 그리고 충분하지 못한 기술적·임상적 검증이 문제가 되고 있다. 본 논문에서는 이러한 문제점을 해결하기위한 새로운 통합 플랫폼을 제안하고자 한다. 이를 위해서 임상데이터는 OHDSI의 OMOP-CDM으로 표준화되어야 하며, 이외에 의료영상 정보를 포함한다. 제안한 플랫폼은 표준화된 데이터를 통해 지속적인 자가 학습을 수행하며, 질환별 진단에 필요한 개발 도구와 분석 소프트웨어 도구를 통해 다양한 타겟 질환연구를 지원한다. 제안한 플랫폼은 질환에 대한 비침습적 진단을 위해 의료영상을 기반으로 데이터표준화을 기반으로하며, 이를통해 인공지능 기술을 개발하고 병원 정보시스템과 연계하여 임상현장에 실증을 통해 검증하고자 한다.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.39-47
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• 2020

This study was conducted with the aim of investigating the actual state of data utilization and the perception of big data utilization by officials of the provincial Office of Education and to derive implications for the establishment of strategies for big data utilization. An online survey of 440 people was conducted. As a result, the types and sources of data used for work varied, and data collection and refining were the most difficult parts. The infrastructure for data utilization was insufficient and the most necessary factor. The purpose of big data utilization was related to the establishment of educational policy agenda.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.122-130
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• 2021

This study is about an Artificial Intelligence-based fake news identification system and its methods to determine the authenticity of content distributed over the Internet. Among the news we encounter is news that an individual or organization intentionally writes something that is not true to achieve a particular purpose, so-called fake news. In this study, we intend to design a system that uses Artificial Intelligence techniques to identify fake content that exists within the news. The proposed identification model will propose a method of extracting multiple unit factors from the target content. Through this, attempts will be made to classify unit factors into different types. In addition, the design of the preprocessing process will be carried out to parse only the necessary information by analyzing the unit factor. Based on these results, we will design the part where the unit fact is analyzed using the deep learning prediction model as a predetermined unit. The model will also include a design for a database that determines the degree of fake news in the target content and stores the information in the identified unit factor through the analyzed unit factor.