• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.835-846
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• 2020

BEC (Business Email Compromise) attacks are frequently occurring by impersonating accounts or management through e-mail and stealing money or sensitive information. This type of attack accounts for the largest portion of the recent trade fraud, and the FBI estimates that the estimated amount of damage in 2019 is about $17 billion. However, if you look at the response status of the companies compared to this, it relies on the traditional SPAM blocking system, so it is virtually defenseless against the BEC attacks that social engineering predominates. To this end, we will analyze the types and methods of BEC accidents and propose ways to effectively counter BEC attacks by companies through AI(Artificial Intelligence).

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.99-110
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• 2000

오늘날의 기업은 상품을 판매하는 것 뿐만 아니라 기업의 신용과 이미지를 위해 그 상품에 대한 사후처리(After Service) 업무에 많은 투자를 하고 있다. 이러한 양질의 사후서비스를 고객에게 공급하기 위해서는 많은 인력을 합리적으로 관리해야 하며 요청되는 고장수리 서비스 업무의 신속한 해결을 위해 업무를 인력에게 합리적으로 배정을 해야 한다. 그러므로, 회사의 비용을 최소화하면서 정해진 시간에 요청된 작업을 처리하기 위해서는 인력들에게 작업을 배정하고 스케줄링하는 문제가 발생된다. 본 논문에서는 이러한 문제를 해결하기 위해서 화학계기의 A/S작업을 인력에게 합리적으로 배정 하는 스케줄링 시스템에 관한 연구이다. 먼저 스케줄링 모델을 HP사의 화학분석 및 시스템을 판매, 유지보수 해주는 Y사의 작업 스케줄을 분석하여 필요한 도메인과 고객서비스전략과 인력관리전략에서 제약조건을 추출하였고 여기에 스케줄링 문제를 해결하기 위한 방법으로 제약만족문제(CSP) 해결기법인 도메인 여과기법을 적용하였다. 도메인 여과기법은 제약조건에 의해 변수가 갖는 도메인의 불필요한 부분을 여과하는 것으로 제약조건과 관련되어 있는 변수의 도매인 크기가 축소되는 것이다. 또한, 스케줄링을 하는데에 있어서 비용적인 측면에서의 스케줄링방법과 고객만족도에서의 스케줄링 방법을 비교하여 가장 이상적인 해를 찾는데 트래이드오프(Trade-off)를 이용하여 최적의 해를 구했으며 실험을 통해 인력에게 더욱 효율적으로 작업들을 배정 할 수 있었고 또한, 정해진 시간에 많은 작업을 처리 할 수 있었으며 작업을 처리하는데 있어 소요되는 비용을 감소하는 결과를 얻을 수 있었다.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.77-84
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• 2022

The study highlights the features and trends of digital marketing for international business. To achieve these goals, the authors used a systematic approach that allows a comprehensive approach to the object of study, as well as used general and specific methods of scientific knowledge on the application of digital marketing for international business. The dynamics of the number of users of social networks in the world is analyzed, which allowed us to conclude about the steady trend of increasing the number of users of the Internet and social networks, as well as the time spent by users on social networks. The study of the dynamics of the number of users of social networks provides increased efficiency in the use of digital marketing tools to enhance international business. The most effective digital marketing tools for international business, including artificial intelligence, conversational marketing, chatbots, personalization, video marketing, live shopping, social media stories, interactive content, omnic marketing, augmented reality and technology immersion, native advertising, green marketing and mobile commerce.

• Structural Engineering and Mechanics
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• v.89 no.2
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• pp.213-223
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• 2024

This paper proposes a composite design of fuzzy adaptive control scheme based on TMD RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in LMI then produces performance indicators with a weighting matrix as a function of cost. It allows to control the trade-off between the two efficiencies by adjusting the appropriate weighting matrix. The two-dimensional Boost control model is equivalent to the LMI-constrained multi-objective optimization problem under dual performance criteria. By using the proposed intelligent control model, the fuzzy nonlinear criterion is satisfied. Therefore, the data connection can be further extended. Evaluation of controller performance the proposed controller is compared with other control techniques. This ensures good performance of the control routines used for position and trajectory control in the presence of model uncertainties and external influences. Quantitative verification of the effectiveness of monitoring and control. The purpose of this article is to ensure access to adequate, safe and affordable housing and basic services. Therefore, it is assumed that this goal will be achieved in the near future through the continuous development of artificial intelligence and control theory.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• 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.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.109-128
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• 2010

The purpose of our research is to figure out the 'non-financial value' of consumers applying networks amongst consumer groups, the data-based marketing strategy to the analysis and delve into the ways for enhancing effectives in marketing activities by adapting the value to the marketing. To verify the authenticity of the points, we did the empirical test on the consumer group using 'the Essence Cosmetics Products' of high involvement that is deeply affected by consumer perceptions and the word-of-mouth activities. 1) The empirical analysis reveals the following features. First, the segmented market for 'Essence Consumer' is composed of several independent networks, each network shows to have the consumers that is high degree centrality and closeness centrality. Second, the result proves the authenticity of the non-financial value for boosting corporate profits by the high degree centrality and closeness centrality consumer's word-of-mouth activities. Lastly, we verify that there lies a difference in the network structure of 'Essence Cosmetics Market'per each product origin(domestic, foreign) and demographic characteristics. It does, therefore, indicate the need to consider the features applying mutually complementary for the network targeting.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.585-594
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• 2018

Virtual currencies have emerged along with new technologies such as block chain, artificial intelligence (AI), and big data. This study examines the benefits of a security-enhanced block chain resulting from individual trading, decentralized from governments, as well as the problems associated with misuse of virtual currencies. Virtual currencies, due to its anonymity, is vulnerable to financial crimes, such as ransom-ware, fraud, drug trafficking, tax evasion and money laundering. Use of virtual currencies can facilitate criminals avoid detection from investigative agencies. Government regulatory policy continues to address these concerns, and the virtual currency exchange has also announced a self-regulation proposal. However, a fundamental solution remains necessary. The purpose of this paper is to investigate the problems regarding abuse of virtual currency and to identify a practical system for transactions involving virtual currencies. However, in order to promote transactions involving virtual currencies and to institutionalize a governance system, multilateral cooperation is required. Although the restricting the use of virtual currencies regarding minors and foreign trade, as well as the introduction of a real-name system are considered promising prospects, many problems remain. Virtual currency is not a simple digital item but a method of redesigning the function of money. Coordinated efforts are needed globally to be able to further activate the positive aspects concerning the use of virtual currencies.

• Journal of Industrial Convergence
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• v.16 no.1
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• pp.21-26
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• 2018

The Seaborne Trade Volume accounts for 97% of the total. This means that the port operation management system can improve port efficiency, reducing operating costs, and the manager who manages all operations at the port needs to check and respond quickly when delays of work and equipment support is needed. Based on the real-time location information confirmation of yard automation equipment used the existing system GPS, the real-time location information confirmation system is a GPS system of the tablet, rather than a port operation system that monitors location information for the entered information, depending on the completion of the task or the start of the task. Network configurations also reduce container processing delays by using commercial LTE services that do not have shading due to containers in the yard also reduce container processing delays. Trough introduction of smart devices using Android or IOS and container processing scheduling utilizing artificial intelligence, we will build a minimum delay system with Smart Device usage of container processing applications and optimization of container processing schedule. The adoption of smart devices and the minimization of container processing delays utilizing artificial intelligence are expected to improve the quality of port services by confirming the processing containers in real time to consumers who are container information demanders.

• The Journal of Industrial Distribution & Business
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• v.9 no.3
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• pp.57-68
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• 2018

Purpose - The purpose of this study is to classify that the quality factors for comparing the Visegrard Group with the Nordic Council have historical similarities against Germany and the Soviet Union. However, this is because in the integrated European market, the competitiveness possessed by the two groups of countries is invested in the priority order to grow. Research design, data, and methodology - The study was conducted on the research design, and the reason for trying to compare the competence of the automobile industry in the assessment of industrial capability is that the Visegrard Group focuses on automotive production and the Nordic Association focuses on the commercialization of the automobile(market). In this study, searching and quantifying indirect evidence was made through standards are more complementary in Europe since each country acts like the role of the European automotive industry for example, which is different from the realistic evaluation criteria, are more important than those of the United States(first in the world) or Germany(first in Europe). Results - The results of this study are as follows: In the global EV market U.S.(export: $ 2.62 billion /share: 36.7%), Germany($ 1.29 billion /18.1%), France($ 390 million /5.4%), United Kingdom($ 380 million /5.4%), and South Korea($ 320 million/ 4.4%). South Korea's share of the EV market is 4.4%, while TSI reaches at +0.9 which measures the comparative advantage of a specific commodity in the world trade market. There is great potential for evaluated as products processing in export competition. But, commercialization, standardization, and overall market expansion did not have a positive impact on global satisfaction. Conclusions - EVs put importance on various utilities. So this suggests that Korea's exports to the EU, including the Visegrard Group, should be more focused on marketability when illuminating with a sharing industrial system under the European Union. It is necessary to specialize in manufacturing and commercialization by country(region) to prepare sharing economy and blockchain in order to create a smart-sharing city linked on artificial intelligence, as the commercialization of electric vehicles, which will have a larger growth rate than that of manufacturing in the fourth revolutionary era.