• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.157-167
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• 2009

Recently, the leakage of confidential information and personal information is taking place on the Internet more frequently than ever before. Most of such online security incidents are caused by attacks on vulnerabilities in web applications developed carelessly. It is impossible to detect an attack on a web application with existing firewalls and intrusion detection systems. Besides, the signature-based detection has a limited capability in detecting new threats. Therefore, many researches concerning the method to detect attacks on web applications are employing anomaly-based detection methods that use the web traffic analysis. Much research about anomaly-based detection through the normal web traffic analysis focus on three problems - the method to accurately analyze given web traffic, system performance needed for inspecting application payload of the packet required to detect attack on application layer and the maintenance and costs of lots of network security devices newly installed. The UTM(Unified Threat Management) system, a suggested solution for the problem, had a goal of resolving all of security problems at a time, but is not being widely used due to its low efficiency and high costs. Besides, the web filter that performs one of the functions of the UTM system, can not adequately detect a variety of recent sophisticated attacks on web applications. In order to resolve such problems, studies are being carried out on the web application firewall to introduce a new network security system. As such studies focus on speeding up packet processing by depending on high-priced hardware, the costs to deploy a web application firewall are rising. In addition, the current anomaly-based detection technologies that do not take into account the characteristics of the web application is causing lots of false positives and false negatives. In order to reduce false positives and false negatives, this study suggested a realtime anomaly detection method based on the analysis of the length of parameter value contained in the web client's request. In addition, it designed and suggested a WAF(Web Application Firewall) that can be applied to a low-priced system or legacy system to process application data without the help of an exclusive hardware. Furthermore, it suggested a method to resolve sluggish performance attributed to copying packets into application area for application data processing, Consequently, this study provide to deploy an effective web application firewall at a low cost at the moment when the deployment of an additional security system was considered burdened due to lots of network security systems currently used.

• Korean Journal of Animal Reproduction
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• v.25 no.1
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• pp.1-7
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• 2001

This study was to test whether in vitro matured Hanwoo oocytes can be successfully cryopreserved by a new vitrification procedure using MVC method. For the vitrification, oocytes were pretreated in 10% ethylene glycol (EG10) for 5~10 min, exposed in EG30 for 30 sec, each oocyte was individually put on the inner wall of 0.25 $m\ell$ straw, and then straws were directly plunged into L$N_2$. Thawing was taken by 4-step procedures 〔1.0 M sucrose (MS), 0.5 MS, 0.25 MS, and 0.125 MS〕 at 37$^{\circ}C$. In vitro developmental capacity (survival, cleavage ($\geq$2-cell) and blastocyst rates) in vitrified group was no significant difference compared to that in other treatment groups (exposed; 100.0, 74.4, 32.3% and control; 100.0, 78.3, 36.3%): high mean percentage of oocytes (91.2%) was survived, 69.4% of them were cleaved and 27.9% of cleaved embryos were developed to blastocyst. Especially, after transfer of in vitro developed embryos in vitrified group, four of six recipient animals were pregnant and three of them were ongoing-pregnant by manual palpation at 250 days after transfer. This result demonstrates that MVC method is very appropriate freezing method for the Hanwoo in vitro matured oocytes and that ovum bank can be maintained efficiently by MVC cryopreservation method.

• Korea Trade Review
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• v.42 no.6
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• pp.111-129
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• 2017

According to the World Bank statistics, China's contribution to global economic growth during the year of 2013-2016 was estimated at 31.6 percent. This figure is even larger than 29.0 percent, the contribution by summing each contribution of the United States, EU and Japan. The Chinese commodity trade accounts for up to 11.5 percent of world trade volume. Thus, we can consider that the Chinese economy has a strong influence on the global economy. The primary purpose of this study is to analyze the contribution level of Chinese seaborne trade volume on world economy. First, this study conducted a time-lag analysis using Moran test, so we can find that China's level of contribution to global economic growth varies from time to time. The contribution of the first phase (1999-2007) was nearly three times higher than the contributions from the second phase (2008-2016), suggesting that the overall contraction of the global trade volume starting from the subprime mortgage crisis in 2008 has continued until recently and recovery has not even occurred. Second, using the econometrics model, this study conducted an regression analysis of the impact of Chinese imports and exports in chemicals, grain, steel, crude oil, and container on global economic growth. Fixed effects model with time series data has been applied to examine the effect of Chinese seaborne trade volume on global economic growth. According to the empirical analysis of this study, China's exports of steel products, exports of container, imports of containers, imports of crude oil and imports of grain have significant contributions to global economic growth. Estimates of China's exports of steel products, exports of container, imports of containers, imports of crude oil and imports of grain are 1.023, 1.020, 1.019, 1.007 and 1.006, respectively. For example, the estimated value 1.023 of China's exports of steel products means that the growth rate can be 1.023 times higher than the current world GDP growth rate if Chinese seaborne trade volume of exports of steel products increased by one unit (one million tons). This study concludes that the expansion of China's imports and exports should be realized first to increase the global GDP growth rate. The expansion of Chinese trade can lead to a simultaneous stimulus of production and consumption in China, which can even lead to global economic growth ultimately. Thus, depending on how much China's trade will be broaden in the future, the width of global economic growth can be determined.

• Journal of Life Science
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• v.33 no.7
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• pp.565-573
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• 2023

In light of the complex interactions between the host animal and its resident gut microbiomes, studies of these microbial communities as a means to improve cattle production are important. This study was conducted to analyze the intestinal microorganisms of Holstein (HT) and Jersey (JS), raised in Korea and to clarify the differences in microbial structures according to cattle species through next-generation sequencing. The alpha-diversity analysis revealed that most species richness and diversity indices were significantly higher in JS than in HT whereas phylogenetic diversity, which is the sum of taxonomic distances, is not significant. Microbial composition analysis showed that the intestinal microbial community structure of the two groups differed. In the both groups, a significant correlation was observed among the distribution of several microbes at the family level. In particular, a highly significant correlation (p<0.0001) among a variety of microbial distributions was found in JS. Beta-diversity analyis was to performed to statistically verify whether a difference exists in the intestinal microbial community structure of the two groups. Principal coordinate analysis and unweighted pair group method with arithmetic mean (UPGMA) clustering analysis showed separation between the HT and JS clusters. Meanwhile, permutational multivariate analysis of variance (PERMANOVA) revealed that their microbial structures are significantly different (p<0.0001). LEfSe biomarker analysis was performed to discover the differenc microbial features between the two groups. We found that several microbes, such as Firmicutes, Bacilli, Moraxellaceae and Pseudomonadales account for most of the difference in intestinal microbial community structure between the two groups.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2015.05a
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• pp.545-570
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• 2015

Korea is among the ten countries with the largest R&D budget and the highest R&D investment-to-GDP ratio, yet the subject of security and protection of R&D results remains relatively unexplored in the country. Countries have implemented in their legal systems measures to properly protect cutting-edge industrial technologies that would adversely affect national security and economy if leaked to other countries. While Korea has a generally stable legal framework as provided in the Regulation on the National R&D Program Management (the "Regulation") and the Act on Industrial Technology Protection, many difficulties follow in practice when determining details on security management and obligations and setting standards in carrying out national R&D projects. This paper proposes to modify and improve security level classification standards in the Regulation. The Regulation provides a dual security level decision-making system for R&D projects: the security level can be determined either by researcher or by the central agency in charge of the project. Unification of such a dual system can avoid unnecessary confusions. To prevent a leakage, it is crucial that research projects be carried out in compliance with their assigned security levels and standards and results be effectively managed. The paper examines from a practitioner's perspective relevant legal provisions on leakage of confidential R&D projects, infringement, injunction, punishment, attempt and conspiracy, dual liability, duty of report to the National Intelligence Service (the "NIS") of security management process and other security issues arising from national R&D projects, and manual drafting in case of a breach. The paper recommends to train security and technological experts such as industrial security experts to properly amend laws on security level classification standards and relevant technological contents. A quarterly policy development committee must also be set up by the NIS in cooperation with relevant organizations. The committee shall provide a project management manual that provides step-by-step guidance for organizations that carry out national R&D projects as a preventive measure against possible leakage. In the short term, the NIS National Industrial Security Center's duties should be expanded to incorporate national R&D projects' security. In the long term, a security task force must be set up to protect, support and manage the projects whose responsibilities should include research, policy development, PR and training of security-related issues. Through these means, a social consensus must be reached on the need for protecting national R&D projects. The most efficient way to implement these measures is to facilitate security training programs and meetings that provide opportunities for communication among industrial security experts and researchers. Furthermore, the Regulation's security provisions must be examined and improved.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• The Korean Journal of Petroleum Geology
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• v.14 no.1
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• pp.1-11
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• 2008

As conventional oil and gas reservoirs become depleted, interests for oil sands has rapidly increased in the last decade. Oil sands are mixture of bitumen, water, and host sediments of sand and clay. Most oil sand is unconsolidated sand that is held together by bitumen. Bitumen has hydrocarbon in situ viscosity of >10,000 centipoises (cP) at reservoir condition and has API gravity between $8-14^{\circ}$. The largest oil sand deposits are in Alberta and Saskatchewan, Canada. The reverves are approximated at 1.7 trillion barrels of initial oil-in-place and 173 billion barrels of remaining established reserves. Alberta has a number of oil sands deposits which are grouped into three oil sand development areas - the Athabasca, Cold Lake, and Peace River, with the largest current bitumen production from Athabasca. Principal oil sands deposits consist of the McMurray Fm and Wabiskaw Mbr in Athabasca area, the Gething and Bluesky formations in Peace River area, and relatively thin multi-reservoir deposits of McMurray, Clearwater, and Grand Rapid formations in Cold Lake area. The reservoir sediments were deposited in the foreland basin (Western Canada Sedimentary Basin) formed by collision between the Pacific and North America plates and the subsequent thrusting movements in the Mesozoic. The deposits are underlain by basement rocks of Paleozoic carbonates with highly variable topography. The oil sands deposits were formed during the Early Cretaceous transgression which occurred along the Cretaceous Interior Seaway in North America. The oil-sands-hosting McMurray and Wabiskaw deposits in the Athabasca area consist of the lower fluvial and the upper estuarine-offshore sediments, reflecting the broad and overall transgression. The deposits are characterized by facies heterogeneity of channelized reservoir sands and non-reservoir muds. Main reservoir bodies of the McMurray Formation are fluvial and estuarine channel-point bar complexes which are interbedded with fine-grained deposits formed in floodplain, tidal flat, and estuarine bay. The Wabiskaw deposits (basal member of the Clearwater Formation) commonly comprise sheet-shaped offshore muds and sands, but occasionally show deep-incision into the McMurray deposits, forming channelized reservoir sand bodies of oil sands. In Canada, bitumen of oil sands deposits is produced by surface mining or in-situ thermal recovery processes. Bitumen sands recovered by surface mining are changed into synthetic crude oil through extraction and upgrading processes. On the other hand, bitumen produced by in-situ thermal recovery is transported to refinery only through bitumen blending process. The in-situ thermal recovery technology is represented by Steam-Assisted Gravity Drainage and Cyclic Steam Stimulation. These technologies are based on steam injection into bitumen sand reservoirs for increase in reservoir in-situ temperature and in bitumen mobility. In oil sands reservoirs, efficiency for steam propagation is controlled mainly by reservoir geology. Accordingly, understanding of geological factors and characteristics of oil sands reservoir deposits is prerequisite for well-designed development planning and effective bitumen production. As significant geological factors and characteristics in oil sands reservoir deposits, this study suggests (1) pay of bitumen sands and connectivity, (2) bitumen content and saturation, (3) geologic structure, (4) distribution of mud baffles and plugs, (5) thickness and lateral continuity of mud interbeds, (6) distribution of water-saturated sands, (7) distribution of gas-saturated sands, (8) direction of lateral accretion of point bar, (9) distribution of diagenetic layers and nodules, and (10) texture and fabric change within reservoir sand body.