• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.94-103
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• 2017

As automatic hacking tools and techniques have been improved, the number of new vulnerabilities has increased. The CVE registered from 2010 to 2015 numbered about 80,000, and it is expected that more vulnerabilities will be reported. In most cases, patching a vulnerability depends on the developers' capability, and most patching techniques are based on manual analysis, which requires nine months, on average. The techniques are composed of finding the vulnerability, conducting the analysis based on the source code, and writing new code for the patch. Zero-day is critical because the time gap between the first discovery and taking action is too long, as mentioned. To solve the problem, techniques for automatically detecting and analyzing software (SW) vulnerabilities have been proposed recently. Cyber Grand Challenge (CGC) held in 2016 was the first competition to create automatic defensive systems capable of reasoning over flaws in binary and formulating patches without experts' direct analysis. Darktrace and Cylance are similar projects for managing SW automatically with artificial intelligence and machine learning. Though many foreign commercial institutions and academies run their projects for automatic binary analysis, the domestic level of technology is much lower. This paper is to study developing automatic detection of SW vulnerabilities and defenses against them. We analyzed and compared relative works and tools as additional elements, and optimal techniques for automatic analysis are suggested.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.1 s.39
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• pp.39-46
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• 2007

Most areas in Kangwon Province are mountainous and vulnerable to landslide due to the rainy season in summer and the localized torrential downpour triggered by abnormal climate. In particular, the rainfall is one of direct reasons for landslide. In accordance with the analysis of the relevance between the landslide areas and the accumulated rainfall for four months, there are severe damages of landslide to the areas having more than 1,100 mm of rainfall during three(3) months. Further, it indicates that the more the accumulated rainfall is the greater the size of landslide. These analyses show that the rainfall causes the possible and potential landslide in the vulnerable areas. And also, it means that there exist strong possibilities of landslide even in the areas of lower vulnerability if the amount of rainfall is above certain standard level. Accordingly, in this study we stored the GIS database on the causes and factors of landslide in the southern parts of Kangwon province and conducted simulations on the change of distribution of vulnerable areas by varying the rainfall conditions and by using the evaluation data of landslide vulnerability. As such a result, we found that the landslide could potentially occur if the amount of rainfall is 200 mm and more.

• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.199-230
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• 2021

This study is to present an analysis and activation plan for the effect of reuse of research data through investigation of researchers and reuse data on reuse of research data. To this end, 178 copies were analyzed based on the distribution and collection of surveys targeting academic researchers in the field of social science in Korea who have experience in calculating new research results by reusing research data. As a result, 1) Most researchers acquire reuse data through systems such as data repositories, data management systems, and research data DBs, and mainly reuse analysis data produced through experiments and observations. In addition, despite being a researcher who successfully reused research data, the awareness of research data sharing was low and did not share it in the face of various problems. 2) The reliability and validity of 10 factors derived through literature review and factor analysis (academic usefulness, research efficiency, researcher concerns, data vulnerability, direct effort, indirect effort, suitability for reuse, data completeness, data usefulness, and social conditions) were verified. 3) As a result of correlation analysis, research efficiency, social conditions showed a quantitative correlation with research data reuse intention, researcher concerns, data vulnerability, and direct effort showed a negative correlation with research data reuse intention. As a result of regression analysis, all of these factors had a significant effect on the intention to reuse research data, and in the order of research efficiency, social conditions, direct efforts, researchers' concerns, and data vulnerability. Based on this, a plan to revitalize the reuse of research data was proposed.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.1 s.35
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• pp.3-12
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• 2006

Kangwondo area is mountainous and landslide happens easily during the rainy period in summer time. Especially, when there is torrential downpour caused by the unusual weather change, there will be greater possibility to see landslide. It is very difficult to analyze and study a natural phenomenon like the landslide because there are so many factors behind it. And the way to conduct the analysis is also very complicated. However, if GIS is used, we can classify and analyze data efficiently by modeling the real phenomenon with a computer. Based upon the analysis on the causes of landslide in the areas where it occurred in the past, therefore, this study shows several factors leading to landslide and contains the GIS database categorized by grade and stored in the computer. In order to analyze the influence of every factor causing landslide, we calculated the rates of weight by AHP and evaluated landslide vulnerability in the study area by using GIS. As a result of such analysis, we found that the forest factor has most potential influences among other factors in landslide.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.172-177
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• 2012

In areas around Deoksan Li and Deokjeon Li, Inje Eup, Inje Gun, located between $38^{\circ}2^{\prime}55^{{\prime}{\prime}}N$ and $38^{\circ}5^{\prime}50^{{\prime}{\prime}}N$ in latitude and $128^{\circ}11^{\prime}20^{{\prime}{\prime}}E$ and $128^{\circ}18^{\prime}20^{{\prime}{\prime}}E$ in longitude, large-sized avalanche disasters occurred due to Typhoon Ewiniar in 2006. As a result, 29 people were dead or missing, along with a total of 37.25 billion won of financial loss(Gangwon Province, 2006). To evaluate such landslide and debris flow risk areas and their vulnerability, this study applied a technique called 'Weight of Evidence' based on GIS. Especially based on the overlay analysis of aerial images before the occurrence of landslides and debris flows in 2005 and after 2006, this study extracted 475 damage-occurrence areas in a shape of point, and established a DB by using such factors as topography, hydrologic, soil and forest physiognomy through GIS. For the prediction diagram of debris flow and landslide risk areas, this study calculated W+ and W-, the weighted values of each factor of Weight Evidence, while overlaying the weighted values of factors. Besides, the diagram showed about 76% in prediction accuracy, and it was also found to have a relatively high correlationship with the areas where such natural disasters actually occurred.