• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1077-1084
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• 2015

Security issues such as an illegal acquisition of personal information and identity theft happen due to using game bots in online games. Game bots collect items and money unfairly, so in-game contents are rapidly depleted, and honest users feel deprived. It causes a downturn in the game market. In this paper, we defined the growth types by analyzing the growth processes of users with actual game data. We proposed the framework that classify hard-core users and game bots in the growth patterns. We applied the framework in the actual data. As a result, we classified five growth types and detected game bots from hard-core users with 93% precision. Earlier studies show that hard-core users are also detected as a bot. We clearly separated game bots and hard-core users before full growth.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1730-1740
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• 2016

This paper introduces a guest identity provider system for eduroam which is a global Wi-Fi service targeting users enrolled in higher education and research institutions. Developed eduroam AND (AutheNtication Delegation) system enables users to create their eduroam user accounts and to access eduroam regardless of their locations. Users with no organizational eduroam account therefore can freely access eduroam using the system. A federated authentication model is implemented in the system, and thus the system has merits of having high accessibility, indirectly verifying users and organizations possible, saving management overhead. Status monitoring is essential because authentication request and response messages are routed by eduroam network. eduroam AND performs active monitoring to check service availability and visualizes the results, which increases operational and management efficiency. We leveraged open-source libraries to implement eduroam AND and run the system on KREONET (Korea REsearch Open NETwork). Lastly, we present implementation details and qualitively evaluate the system.

• Culinary science and hospitality research
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• v.15 no.4
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• pp.212-226
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• 2009

This study attempted to analyze the correlation between financial ratio and operating performance of foodservice companies, using the financial data by DART service. Financial ratio is an index to identify the management of foodservice companies from calculating the ratio associating two accounts in the financial statements. Managers, creditors and investors often have different purposes for using the ratio analysis to evaluate the contents of the financial statements. According to the analysis of financial ratio and operating performance, listed food and beverage companies proved to have a high correlation in all except for interest coverage. However, foodservice companies showed a high correlation in stability and growth ratio. Therefore, managers of the foodservice companies will need to improve operating performance for using efficient utilization plans of debt from assets and operating expenses(cost of goods sold, general and other expenses).

• Journal of Korea Game Society
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• v.15 no.1
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• pp.77-88
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• 2015

As the smart phone market is drastically expanding, there is a steady growth of recent vicious activities such as data manipulation, billing fraud, identity theft, and leakage of personal information that are security threats to Social Network Games(SNG). Due to the threats, Strong development standard is required for security enhancement of SNG. Nonetheless, short life-spans, additional expenses, and the necessities to provide a sound game service hinders developers from reaching their security goals. Therefore, this research investigates the weak points of SNG through memory manipulation experiments based on the currently provided SNG services. In addition, the research presents counter measures and security enforcements that are light in service load and simplistic which can be applied in the developing process.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.327-335
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• 2016

The People's Republic of China has vigorously been pursuing the internationalization of the Chinese Yuan or Renminbi after the financial crisis of 2008. In this view, an abrupt increase of use of the Chinese Yuan in the onshore and offshore markets are important milestones to be one of important currencies. One of the most frequently used methods to forecast volatility is GARCH model. Since a prediction error of the GARCH model has been reported quite high, a lot of efforts have been made to improve forecasting capability of the GARCH model. In this paper, we have proposed MLP-GARCH and a DL-GARCH by employing Artificial Neural Network to the GARCH. In an application to forecasting Chinese Yuan volatility, we have successfully shown their overall outperformance in forecasting over the GARCH.

• Journal of Distribution Research
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• v.8 no.2
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• pp.41-65
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• 2003

The objectives of this study are to investigate how the difference of consumer behavior between the expected and unexpected price discounts(increases), and mental accounting process affect spending account. Key findings of the study are as follows. First, it is shown that consumer would regard a windfall gain caused by the expected price discount and unexpected one as a different thing(gain}, Second, this study shows that if consumers are presented the price discount on the former purchased item in the case consumers purchase two kinds of items together, they would prefer spending more money on the later item to spending more money on the discounted item. Third, it is shown that consumers are willing to do a planned purchase when they find a store's price raise before arriving at a store(expected increasing) rather than after arriving at a store(unexpected increasing). The theoretical as well as practical implications were also discussed.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.873-877
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• 2010

CAPTCHA is a security tool that prevents the automatic sign-up by a spam or a robot. This CAPTCHA usually depends on the smart readability of humans. However, the common and plain CAPTCHA with text-based system is not difficult to be solved by intelligent web-bot and machine learning tools. In this paper, we propose a new sub-image based CAPTCHA system totally different from the text based system. Our system offers a set of cropped sub-image from a whole digital picture and asks user to identify the correct orientation. Though there are some nice machine learning tools for this job, but they are useless for a cropped sub-images, which was clearly revealed by our experiment. Experiment showed that our sub-image based CAPTCHA is easy to human solver, but very hard to all kinds of machine learning or AI tools. Also our CAPTCHA is easy to be generated automatical without any human intervention.

• Journal of KIISE
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• v.41 no.12
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• pp.1110-1116
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• 2014

Nowadays, social networking sites (SNSs), such as Twitter, LinkedIn, and Tumblr, are coming into the forefront, due to the growth in the number of users. While users voluntarily provide their information in SNSs, privacy leakages resulting from the use of SNSs is becoming a problem owing to the evolution of large data processing techniques and the raising awareness of privacy. In order to solve this problem, the studies on protecting privacy on SNSs, based on graph and machine learning, have been conducted. However, examples of privacy leakages resulting from the advent of a new SNS are consistently being uncovered. In this paper, we propose a technique enabling a user to detect privacy leakages beforehand in the case where the service provider or third-party application developer threatens the SNS user's privacy maliciously.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1153-1160
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• 2018

As the number of online game users increases and the market size grows, various kinds of cheating are occurring. Game bots are a typical illegal program that ensures playtime and facilitates account leveling and acquisition of various goods. In this study, we propose a method to detect game bots based on user action time interval (ATI). This technique observes the behavior of the bot in the game and selects the most frequent actions. We distinguish between normal users and game bots by applying Machine Learning to feature frequency, ATI average, and ATI standard deviation for each selected action. In order to verify the effectiveness of the proposed technique, we measured the performance using the actual log of the 'Aion' game and showed an accuracy of 97%. This method can be applied to various games because it can utilize all actions of users as well as character movements and social actions.

• The KIPS Transactions:PartC
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• v.15C no.6
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• pp.455-468
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• 2008

In this paper, we propose FAS model for establishing trust based on digital certificates in Grid security framework. The existing RBAC(Role Based Access Control) model is extended to provide permissions depending on the users‘ roles. The FAS model is designed for a system independent integrated Grid security by detailing and extending the fundamental architecture of user, role, and permission. FAS decides each user’s role, allocates access right, and publishes attribute certificate. FAS is composed of three modules: RDM, PCM, and CCM. The RDM decides roles of the user during trust negotiation process and improves the existing low level Grid security in which every single user maps a single shared local name. Both PCM and CCM confirm the capability of the user based on various policies that can restrict priority of the different user groups and roles. We have analyzed the FAS strategy with the complexity of the policy graph-based strategy. In particular, we focused on the algorithm for constructing the policy graph. As a result, the total running time was significantly reduced.