• Journal of Internet Computing and Services
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• v.8 no.6
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• pp.135-144
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• 2007

Feature models are widely used in domain engineering phase of software product lines development to model the common and variable concepts among products. From the feature model, the feature configurations are generated by selecting the features to be included in target product. The feature configuration represents the requirements for the specific product to be implemented. Although there are a lot of researches on how to build and use the feature models and feature configurations, the researches on the formal semantics and reasoning of them are rather inactive. This paper proposes the feature configuration verification approach based on JESS, java-based rule-base system. The Graph Product Line, a standard problem for evaluating the software product line technologies, is used throughout the paper to illustrate this approach. The approach in this paper has advantage of presenting the exact reason causing inconsistency in the feature configuration. In addition, this approach should be easily applied into other software product lines development environments because JESS system can be easily integrated with Java language.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.363-370
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• 2018

Ransomware detection has become a hot topic in computer security for protecting digital contents. Unfortunately, current signature-based and static detection models are often easily evadable by compress, and encryption. For overcoming the lack of these detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as RF, SVM, SL and NB algorithms. We monitor the actual behaviors of software to generate API calls flow graphs. Thereafter, data normalization and feature selection were applied to select informative features. We improved this analysis process. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. We conduct our experiment using more suitable real ransomware samples. and it's results show that our proposed system can be more effective to improve the performance for ransomware detection.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.769-770
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• 2017

최근 데이터 기술의 발달에 따라, 기업에서는 중요 데이터를 서버와 같은 데이터 저장 장치에 보관하고 있다. 하지만 기업 내부 직원에 의해 기업의 기밀 데이터가 유출될 수 있는 위험성이 있기 때문에, 내부 직원에 의한 데이터 유출을 탐지 및 방지해야 할 필요성이 있다. 따라서 본 논문에서는 각 보안 솔루션에서 수집한 보안 로그를 데이터 유출 시나리오를 바탕으로 시계열 그래프로 작성하여, 이미지 인식에 뛰어난 성능을 보이는 합성곱 신경망을 통해 데이터 유출을 탐지하는 시스템을 제안한다. 실험 결과 유출된 데이터의 크기에 상관없이 95% 이상의 정확도를 보였으며, 복합적인 행동을 통해 데이터 유출을 시도한 경우에도 97% 이상의 정확도를 보였다.

• Proceedings of the Safety Management and Science Conference
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• 2005.05a
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• pp.453-459
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• 2005

On this research, we show some concrete examples as software design, 2D/3D display, graph display, and gage display to develop a data monitoring system for real time safe fire testing. Developed software which is simulation software for live fire testing, has been designed to display informations about whole test status in a live fire testing, and with this, user can control a live fire testing under the safe environment. Beside, we increase a security by using a authority of user to access on this software. and we develop it based on module designed to apply a requirement of user later on.

• Journal of the Korea Safety Management & Science
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• v.7 no.2
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• pp.65-72
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• 2005

On this research, we show some concrete examples as software design, 2D/3D display, graph display, and gage display to develop a data monitoring system for real time safe fire testing. Developed software which is simulation software for live fire testing, has been designed to display informations about whole test status in a live fire testing, and with this, user can control a live fire testing under the safe environment. Beside, we increase a security by using a authority of user to access on this software. and we develop it based on module designed to apply a requirement of user later on.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.437-438
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• 2014

본 논문에서는 동적 API 콜 그래프를 기반으로 하는 버스마킹 기법을 제안한다. API 콜 그래프를 이용함으로써 기존 방법들에 비해 프로그램의 정보를 보다 많이 반영하였다. 상용 Windows 프로그램들을 대상으로 실험을 수행하였으며, 실제로 기존의 유사성 분석 기법들에 비해 신뢰성과 강인성 측면에서 모두 성능 향상을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.9-11
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• 2022

Community detection is a hot topic in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper focuses on detecting absolute fair maximal balanced cliques in signed attributed social networks, which can satisfy ensuring the fairness of complex networks and break the bottleneck of the "information cocoon".

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.2041-2049
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• 1998

This paper suggests a technique for program segmentation and maintenance using PSDG(Post-State Dependency Graph) that improves the quality of a software by identifying and detecting defects in already fixed source code. A program segmentation is performed by utilizing source code analysis which combines the measures of static, dynamic and semantic slicing when we need understandability of defect in programs for corrective maintanence. It provides users with a segmental principle to split a program by tracing state dependency of a source code with the graph, and clustering and highlighting, Through a modeling of the PSDG, elimination of ineffective program deadcode and generalization of related program segments arc possible, Additionally, it can be correlated with other design modeb as STD(State Transition Diagram), also be used as design documents.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.520-537
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• 2019

This paper proposes a system that can detect the data leakage pattern using a convolutional neural network based on defining the behaviors of leaking data. In this case, the leakage detection scenario of data leakage is composed of the patterns of occurrence of security logs by administration and related patterns between the security logs that are analyzed by association relationship analysis. This proposed system then detects whether the data is leaked through the convolutional neural network using an insider malicious behavior graph. Since each graph is drawn according to the leakage detection scenario of a data leakage, the system can identify the criminal insider along with the source of malicious behavior according to the results of the convolutional neural network. The results of the performance experiment using a virtual scenario show that even if a new malicious pattern that has not been previously defined is inputted into the data leakage detection system, it is possible to determine whether the data has been leaked. In addition, as compared with other data leakage detection systems, it can be seen that the proposed system is able to detect data leakage more flexibly.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.875-890
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• 2009

Metabolimics interprets an organism as a network of functional units and an organism is represented by a metabolic pathway i.e., well-displayed graph. So a software tool for drawing pathway is necessary to understand it comprehensively. These tools have a problem that edge-crossings exponentially increase as the number of nodes grows. To apply automatic graph layout techniques to the genome-scale metabolic flow, it is very important to reduce unnecessary edge-crossing on a metabolic pathway layout. In this paper, we design and implement 2.5D metabolic pathway layout modules. Metabolic pathways are represented hierarchically by making use of the '2-layered layout algorithm' in 3D. It enhances the readability and reduces unnecessary edge-crossings by using 3D layout modules instead of 2D layout algorithms.