• Journal of Korea Game Society
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• v.11 no.6
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• pp.81-93
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• 2011

Identity theft happens frequently in popular MMORPG(Massively Multi-player Online Role Playing Games) where profits can be gained easily. In spite of the importance of security about identity theft in MMORPG, few methods to prevent and detect identity theft in online games have been proposed. In this study, we investigate real identity theft cases of an online game and define the representative patterns of identity theft as the speedy type, cautious type, and bold type. We then propose the automatic identity theft detection model based on the multi-class classification. We verify the system with one of the leading online games in Korea. The multi-class detection model outperforms the existing binary-class one(hacked or not).

• Journal of Korea Game Society
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• v.16 no.2
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• pp.51-60
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• 2016

The incidents of massive personal information being leaked are occurring continuously over recent years. Personal information leaked outside is used for an illegal use of other's name and account theft. Especially it is happening on online games whose virtual goods, online game money and game items can be exchanged with real cash. When we research the real identity theft cases that happened in an online game, we can see that they happen massively in a short time. In this study, we define the characteristics of the mass attacks of the automated identity theft cases that occur in online games. Also we suggest a system to detect and prevent identity theft attacks in real time.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.3
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• pp.627-637
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• 2015

As game item trading becomes more popular with the rapid growth of online game market, the market for trading game items by cash has increased up to KRW 1.6 trillion. Thanks to this active market, it has been easy to turn these items and game money into real money. As a result, some malicious users have often attempted to steal other players' rare and valuable game items by using their account. Therefore, this study proposes a detection model through analysis on these account thieves' behavior in the Massive Multiuser Online Role Playing Game(MMORPG). In case of online game identity theft, the thieves engage in economic activities only with a goal of stealing game items and game money. In this pattern are found particular sequences such as item production, item sales and acquisition of game money. Based on this pattern, this study proposes a detection model. This detection model-based classification revealed 86 percent of accuracy. In addition, trading patterns when online game identity was stolen were analyzed in this study.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.455-461
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• 2020

Electronic payment system using Internet banking is a very important application for users of e-commerce environment. With rapidly growing use of fintech applications, the risk and damage caused by malicious hacking or identity theft are getting significant. To prevent the damage, fraud detection system (FDS) calculates the risk of the electronic payment transactions using user profiles including types of goods, device status, user location, and so on. In this paper, we propose a new risk calculation method using relative location of users such as SSID of wireless LAN AP and MAC address. Those relative location information are more difficult to imitate or copy compared with conventional physical location information like nation, GPS coordinates, or IP address. The new method using relative location and cumulative user characteristics will enable stronger risk calculation function to FDS and thus give enhanced security to electronic payment systems.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.213-218
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• 2021

This paper proposes a technique for detecting a significant threat that attempts to get sensitive and confidential information such as usernames, passwords, credit card information, and more to target an individual or organization. By definition, a phishing attack happens when malicious people pose as trusted entities to fraudulently obtain user data. Phishing is classified as a type of social engineering attack. For a phishing attack to happen, a victim must be convinced to open an email or a direct message [1]. The email or direct message will contain a link that the victim will be required to click on. The aim of the attack is usually to install malicious software or to freeze a system. In other instances, the attackers will threaten to reveal sensitive information obtained from the victim. Phishing attacks can have devastating effects on the victim. Sensitive and confidential information can find its way into the hands of malicious people. Another devastating effect of phishing attacks is identity theft [1]. Attackers may impersonate the victim to make unauthorized purchases. Victims also complain of loss of funds when attackers access their credit card information. The proposed method has two major subsystems: (1) Data collection: different websites have been collected as a big data corresponding to normal and phishing dataset, and (2) distributed detection system: different artificial algorithms are used: a neural network algorithm and machine learning. The Amazon cloud was used for running the cluster with different cores of machines. The experiment results of the proposed system achieved very good accuracy and detection rate as well.

• Convergence Security Journal
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• v.8 no.4
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• pp.1-8
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• 2008

Not only the recent hacking techniques are becoming more malicious with the sophisticated technology but also its consequences are bringing more damages as the broadband Internet is growing rapidly. These may include invasion of information leakage, or identity theft over the internet. Its intent is very destructive which can result in invasion of information leakage, hacking, one of the most disturbing problems on the net. This thesis describes the technology of how you can effectively analyze and detect these kind of E-Mail malicious codes. This research explains how we can cope with malicious code more efficiently by detection method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.607-610
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• 2008

Phishing is a form of online identity theft that aims to steal sensitive information such as online banking passwords and credit card information from users. Phishing scams have been receiving extensive press coverage because such attacks have been escalating in number and sophistication. According to a study by Gartner, Many Internet users have identified the receipt of e-mail linked to phishing scams and about 2 million of them are estimated to have been tricked into giving away sensitive information. This paper presents a novel browser extension, AntiPhish, that aims to protect users against spoofed web site-based phishing attack.

• Advances in environmental research
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• v.12 no.2
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• pp.113-122
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• 2023

Biometric authentication has become an essential part of modern-day security systems, especially in financial institutions like banks. A face recognition-based ATM is a biometric authentication system, that uses facial recognition technology to verify the identity of bank account holders during ATM transactions. This technology offers a secure and convenient alternative to traditional ATM transactions that rely on PIN numbers for verification. The proposed system captures users' pictures and compares it with the stored image in the bank's database to authenticate the transaction. The technology also offers additional benefits such as reducing the risk of fraud and theft, as well as speeding up the transaction process. However, privacy and data security concerns remain, and it is important for the banking sector to instrument solid security actions to protect customers' personal information. The proposed system consists of two stages: the first stage captures the user's facial image using a camera and performs pre-processing, including face detection and alignment. In the second stage, machine learning algorithms compare the pre-processed image with the stored image in the database. The results demonstrate the feasibility and effectiveness of using face recognition for ATM authentication, which can enhance the security of ATMs and reduce the risk of fraud.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.849-861
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• 2015

As the rise in popularity of online games, the users start exchanging rare items for real money. As RMT (Real Money Trade) is prevalent, GFG (Gold Farming Group) who abuse RMT shows up. GFG causes social problems such as identity theft, privacy leaks. Because they needs many bot characters to gather game items. In addition, GFG induce RMT that makes in-game problems such as a destroying game economy, account hacking. Therefore, It is very important work to collapse GFG at the perspective of social and in-game. In this paper, we proposed a fundamental method for detecting RMT buyers for the collapse of GFG at the perspective of buyer by Law of Demand and Supply. We found two type of RMT by analyzing actual game data and detected RMT buyers with high recall ratio of 98% by ruled-based detection.

• 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.