• Journal of KIISE:Databases
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• v.35 no.2
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• pp.112-124
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• 2008

RDF and OWL are the primary base technologies for implementing Semantic Web. Recently, many researches related with them, or applying them into the other application domains, have been introduced. However, relatively little work has been done for securing the RDF and OWL data. In this article, we briefly introduce an RDF triple based model for specifying RDF access authorization related with RDF security. Next, to efficiently find the authorization conflict by RDF inference, we introduce a method using prime number graph labeling in detail. The problem of authorization conflict by RDF inference is that although the lower concept is permitted to be accessed, it can be inaccessible due to the disapproval for the upper concept. Because by the RDF inference, the lower concept can be interpreted into the upper concept. Some experimental results show that the proposed method using the prime number graph labeling has better performance than the existing simple method for the detection of the authorization conflict.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.735-736
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• 2021

인터넷에서 댓글 시스템은 자신의 의사표현을 위한 시스템으로 널리 사용되고 있다. 하지만 이를 악용하여 상대방에 대한 혐오를 드러내기도 한다. 악성댓글에 대한 적절한 대처를 위해 빠르고 정확한 탐지는 필수적이다. 본 연구에서는 악성 댓글 분류 문제를 해결하기 위해서 순서가 있는 분류 레이블의 성질을 활용한 순서형 회귀 (Ordinal regression) 기반의 분류 모델을 제안한다. 일반적인 분류 모형과는 달리 혐오 발언 정도에 따라 다중 레이블을 부여하여 학습을 진행하였다. 실험을 통해 Korean Hate Speech Dataset에 대해 LSTM기반의 모형의 출력층을 다르게 구성하여 순서형 회귀 기반의 모형들의 성능을 비교하였다. 결과적으로 예측 결과에 대한 조율이 가능한 순서형 회귀 모형이 일반적인 순서형 회귀 모형에 비해서 편향된 예측에 대해 추가적인 성능 향상을 보였다.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.967-974
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• 1998

Access control scheme of the firewall systems protects the systems from threats by using the conventional discretionary access control mechanism. The discretionary access control mechanism is insufficient to control secure information flow on the multievel network. Thus, it is necessary to provide the mandatory access control mechanism to the firewall systems for the multilevel security environment. In this paper, we present a design scheme of the security mechanisms concerning the sensitivity label and the mandatory access control for securely processing the multilevel information.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.6
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• pp.173-180
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• 2015

The advent of personal healthcare record(PHR) technology has been changing the uses as well as the paradigm of internet services, and emphasizing the importance of services being personalization. But the problem of user's privacy infringement and leaking user's sensitive medical information is increasing with the fusion of PHR technology and healthcare. In this paper, we propose a security labeling scheme for privacy protection in PHR system. In the proposed scheme, PHR data can be labeled also manually based on patient's request or the security labelling rules. The proposed scheme can be used to control access, specify protective measures, and determine additional handling restrictions required by a communications security policy.

• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.84-90
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• 2000

This paper presents a design of an access control mechanism that can resolves the complicated problems of access control requirements in internet environment. In this paper, we proposed an access control mechanism which can satisfy the combined goals of confidentiality integrity and availability of any resource. We defined an access control mechanism from the viewpoints of identity-based, rule-based and role-based policy and implemented 6 access control operations. The Proposed access control mechanism can protect resources from unauthorized accesses based on the multi-level security policies of security label, integrity level, role and ownership.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.1-13
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• 2022

With the development of network technologies, the security to protect organizational resources from internal and external intrusions and threats becomes more important. Therefore in recent years, the anomaly detection algorithm that detects and prevents security threats with respect to various security log events has been actively studied. Security anomaly detection algorithms that have been developed based on rule-based or statistical learning in the past are gradually evolving into modeling based on machine learning and deep learning. In this study, we propose a deep-autoencoder model that transforms LSTM-autoencoder as an optimal algorithm to detect insider threats in advance using various machine learning analysis methodologies. This study has academic significance in that it improved the possibility of adaptive security through the development of an anomaly detection algorithm based on unsupervised learning, and reduced the false positive rate compared to the existing algorithm through supervised true positive labeling.

• Journal of Industrial Convergence
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• v.22 no.6
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• pp.51-59
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• 2024

The purpose of the paper is to suggest government policies that are practically helpful to small business owners in pandemic situations such as COVID-19. To this end, keyword frequency analysis and word cloud analysis of text mining analysis were performed by crawling news articles centered on the keywords "COVID-19 Support for Small Businesses", "The Impact of Small Businesses by Response System to COVID-19 Infectious Diseases", and "COVID-19 Small Business Economic Policy", and major issues were identified through LDA topic modeling analysis. As a result of conducting LDA topic modeling, the support policy for small business owners formed a topic label with government cash and financial support, and the impact of small business owners according to the COVID-19 infectious disease response system formed a topic label with a government-led quarantine system and an individual-led quarantine system, and the COVID-19 economic policy formed a topic label with a policy for small business owners to acquire economic crisis and self-sustainability. Focusing on the organized topic label, it was intended to provide basic data for small business owners to understand the damage reduction policy for small business owners and the policy for enhancing market competitiveness in the future pandemic situation.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2354-2365
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• 1997

This paper presents a design of an access control mechanism that can resolves the complicated problems of access control requirements in modern information communication applications. In this paper, we proposed an integrated information model which can satisfy the combined goals of confidentiality, integrity and availability of any resource. We defined an integrated information model from the view points of identity-based, rule-based and role-based policy and implemented six access control operations. The proposed integrated information model can protect to unauthorized access to any resource based on the multilevel security policies of security label, integrity level, role and ownership.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2001.11a
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• pp.375-378
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• 2001

엔터프라이즈 환경에서 데이터의 교환을 위해 메시지중계시스템을 사용하고 있다. 그러나, 현재 사용하고 있는 메시지중계시스템 자체에는 정보보호서비스가 적용된 예가 많지 않으며 일부 적용된 경우에도 접근제어 서비스를 제공하고 있지 못하다. 본 논문에서 제안하는 PMI(Privilege Management Infrastructure)기반의 메시지중계시스템은 기존의 구축되어 있는 PKI(Public Key Infrastructure)를 이용하여 정보보호서비스의 인증, 무결성, 기밀성, 부인방지 서비스를 제공하며 PMI을 적용하여 접근제어 서비스를 제공한다. PMI의 속성인증서를 사용하여 실시간적으로 변하는 접근제어 정보의 변화를 수용하며 SOA(Sorce of Authority)를 통해 중앙에서 접근제어정보를 관리한다. 교환되는 데이터는 S/MIME을 기본으로 하여 국제표준을 따르며 S/MIME의 보안 레이블을 이용 데이터의 변경없이 접근제어 정보를 전송할 수 있도록 하였다.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.