• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.730-736
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• 2011

In this paper, we designed the models for pattern classification which can reflect the latest trend in time series. It has been shown that fusion models based on statistical and AI methods are superior to traditional ones for the pattern classification model supporting decision making. Especially, the hit rates of pattern classification models combined with fuzzy theory are relatively increased. The statistical SVM models combined with fuzzy membership function, or the models combining neural network and FCM has shown good performance. BPN, PNN, FNN, FCM, SVM, FSVM, Decision Tree, Time Series Analysis, and Regression Analysis were used for pattern classification models in the experiments of this paper. The economical indices DB with time series properties of the financial market(Korea, KOSPI200 DB) and the electrocardiogram DB of arrhythmia patients in hospital emergencies(USA, MIT-BIH DB) were used for data base.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.628-629
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• 2011

E-commerce is a kind of trade type between buyers and sellers rely on computer networks. Carried out according to certain standards of various business activities. e-commerce is based on modern information technology and network technology, financial electronic collection, management, information technology, business and trade information networks into one, aimed at logistics, capital flow and information flow of harmony and unity of the new trade, trade activities of the entire process, electronic, networking and digital. E-commerce has brought to the traditional ways of trading a huge impact, led to changes in economic structures, is a business revolution in the way and is recognized internationally as the 21st century is an important driver of economic development compared to current traditional companies. In this material, we first consider the current problem, analyze particular characteristics of clothing shopping mall to increase the purchasing power of customer. For these reasons, we describe the process for building the clothing site.

• The Journal of Information Technology
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• v.1 no.1
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• pp.155-172
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• 1998

Electronic commerce is more than just handling purchase transactions and funds transfers over the internet. Despite electronic commerce's past roots in transactions between large corporations, banks, and other financial institutions, the use of the internet as a way to bring electronic commerce to the individual consumer has led to a shift in viewpoint. Over the past few years, both the press and the business community have increased their focus on electronic commerce involving the consumer Electronic commerce includes the tasks that support the buying and selling of goods and services, and interactions among those tasks. Electronic commerce enables companies to close stores, reduce inventory requirements, and distribute products over the internet. Electronic commerce can simplify communication and change relationships The economics of electonic commerce is concernec with a new market whose delivery and communication infrastructure happens to be the internet. The economics of electronic commerce focuses on markets whose transactions are facilitated by communications networks and delivery systems. However, any digital communications media will soon be capable of supporting virtual transactions In the electronic marketplace, including telephone wires, cables, microwaves, and satellites. Thus, electronic commerce can offer your company both short-term and long-term befits. moving business practices, such as ordering, invoicing, and consumer support, to network-based systems can also reduce the paperwork involved in business-to-business transactions. This study conducted a study on economic effects of electronic commerce

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.155-165
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• 2018

As medical healthcare industry is growing up rapidly these days, providing various new healthcare service is considered carefully. Health information is considered to be more important than financial information; therefore, protecting health information becomes a very significant task. Ransomware is now targeting industry groups that have high information value. Especially, ransomware has grown in various ways since entering maturity in 2017. Healthcare industry is highly vulnerable to ransomeware since most healthcare organizations are configured in closed network with lack of malware protection. Only meeting the security criteria is not the solution. In the case of a successful attack, restoration process must be prepared to minimize damages as soon as possible. Ransomware is growing rapidly and becoming more complex that protection must be improved much faster. Based on ISO 27799 and 27002 standard, we extract and present security measures against advanced ransomware to maintain and manage healthcare system more effectively.

• 한국노년학
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• v.38 no.3
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• pp.453-466
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• 2018

The purpose of this study is to explore the enlightenment of the Korean long-term care insurance policy on Qingdao's long-term care insurance policy through policy comparison. China and Korea are very similar in terms of cultural background, living habits, and population structure. Therefore, the successful experience of Korean long-term care insurance has great implications for Qingdao even China to build a long-term care insurance system. Through the literature review, this article compares the long-term care insurance policy implemented by Qingdao City and Korea in Gilbert & Terrell's social welfare policy analysis framework. With the comparison this article discusses about the existing problems of the current pilot policy system in Qingdao, such as lack of legislation support and financial independence, assessment standards are not detailed, and human resources are insufficient. The author raises five suggestions to improve Qingdao's long-term care policy as the conclusion of this paper: legislation support, detailed assessment standard, expand categories of benefits, enrich delivery network, optimize financing sources.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.123-130
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• 2021

With the recent developments in computer technology, there has been an increasing interest in the field of machine learning. This also has led to a significant increase in real business cases of machine learning theory in various sectors. In finance, it has been a major challenge to predict the future value of financial products. Since the 1980s, the finance industry has relied on technical and fundamental analysis for this prediction. For future value prediction models using machine learning, model design is of paramount importance to respond to market variables. Therefore, this paper quantitatively predicts the stock price movements of individual stocks listed on the KOSPI market using machine learning techniques; specifically, the reinforcement learning model. The DQN and A2C algorithms proposed by Google Deep Mind in 2013 are used for the reinforcement learning and they are applied to the stock trading strategies. In addition, through experiments, an input value to increase the cumulative profit is selected and its superiority is verified by comparison with comparative algorithms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.47-60
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• 2011

With the increase in the use of mobile banking service, mobile banking has become an attractive target to attackers. Even though many security measures are applied to the current mobile banking service, some threats such as physical theft or penetration to a mobile device from remote side are still remained as unsolved. With aiming to fill this void, we propose a novel approach to prevent e-financial incidents by analyzing mobile device user's input patterns. This approach helps us to distinguish between original user's usage and attacker's usage through analyzing personal input patterns such as input time-interval, finger pressure level on the touch screen. Our proposed method shows high accuracy, and is effective to prevent the e-finance incidents proactively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.4
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• pp.63-73
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• 2010

Malicious bot programs, the source of distributed denial of service attack, are widespread and the number of PCs which were infected by malicious bot program are increasing geometrically thesedays. The continuous distributed denial of service attacks are happened constantly through these bot PCs and some financial incident cases have found lately. Therefore researches to response distributed denial of service attack are necessary so we propose an effective feature generation method for distributed denial of service attack detection using entropy. In this paper, we apply our method to both the DARPA 2000 datasets and also the distributed denial of service attack datasets that we composed and generated ourself in general university. And then we evaluate how the proposed method is useful through classification using bayesian network classifier.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.193-204
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• 2022

The 2018 Seoul Zero Pay is a policy actively promoted by the government as an economic stimulus package for small business owners and the self-employed who are experiencing economic depression due to COVID-19. However, the controversy over the effectiveness of Zero Pay continues even after two years have passed since the implementation of the policy. Zero Pay is a joint QR code mobile payment service introduced by the government, Seoul city, financial companies, and private simple payment providers to reduce the burden of card merchant fees for small business owners and self-employed people who are experiencing economic difficulties due to the economic downturn., it was attempted in the direction of economic revitalization for the return of alleyways[1]. Therefore, this study intends to draw implications for improvement measures so that the ongoing zero-pay can be further activated and the economy can be settled normally. The analysis results of this study are as follows. First, it shows the effect of increasing the income of small business owners by inducing consumption in alleyways through the economic revitalization policy of Zero Pay. Second, the issuance and distribution of Zero Pay helps to revitalize the local economy and contribute to the establishment of a virtuous cycle system. Third, stable operation is being realized by the introduction of blockchain technology to the Zero Pay platform. In terms of academic significance, the direction of Zero Pay's policies and systems was able to identify changes in the use of Zero Pay through big data analysis. The implementation of the zero-pay policy is in its infancy, and there are limitations in factors for examining the consumer image perception of zero-pay as there are insufficient prior studies. Therefore, continuous follow-up research on Zero Pay should be conducted.

• 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.