• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.1
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• pp.93-107
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• 2016

Game bot playing is one of the main risks in Massively Multi-Online Role Playing Games(MMORPG) because it damages overall game playing environment, especially the balance of the in-game economy. There have been many studies to detect game bot. However, the previous detection models require continuous maintenance efforts to train and learn the game bots' patterns whenever the game contents change. In this work, we have proposed a machine learning technique using the self-similarity property that is an intrinsic attribute in game bots and automated maintenance system. We have tested our method and implemented a system to major three commercial games in South Korea. As a result, our proposed system can detect and classify game bots with high accuracy.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.225-238
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• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. In this paper, we propose a framework for user behavior analysis for bot detection in online games. Specifically, we focus on party play that reflects the social activities of gamers: In a Massively Multi-user Online Role Playing Game (MMORPG), party play log includes a distinguished information that can classify game users under normal-user and abnormal-user. That is because the bot users' main activities target on the acquisition of cyber assets. Through a statistical analysis of user behaviors in game activity logs, we establish the threshold levels of the activities that allow us to identify game bots. Also, we build a knowledge base of detection rules based on this statistical analysis. We apply these rule reasoner to the sixth most popular online game in the world. As a result, we can detect game bot users with a high accuracy rate of 95.92%.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1415-1425
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• 2018

MMORPG genre is based on the belief that all users in virtual world are equal. All users are able to obtain the corresponding wealth or status as they strive under the same resource, time. However, game bot is the main factor for harming this fair competition, causing benign gamers to feel a relative deprivation and deviate from the game. Game bots mainly form GFG(Gold Farming Group), which collects the goods in the game indiscriminately and adversely affects the economic system of the game. A general game bot detection algorithm is useful for detecting each bot, but it only covers few portions of GFG, not the whole, so it needs a wider range of detecting method. In this paper, we propose a method of detecting GFG based on items used in MMORPG genre. Several items that are mainly traded in the game were selected and the flows of those items were represented by a network. We Identified the characteristics of exchanging items of GFG bots and can identify the GFG's item trade network with real datasets from one of the popular online games.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.6
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• pp.1097-1104
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• 2021

Online game-bots are already known for a lot of persons by various ways. It leads to problems such as declining game player's interest, in-game financial crisis, etc. Detecting and restricting of game-bot is now essential. Because both publishers and players get disadvantages from their long term abnormal working. But it is not easy to restrict, because of false restriction risks. Game publishers need to distinguish game-bot from server-side game logs. At last, it should can make reasons for game-bot restriction. In this paper, we classified game-bot users by using daily separated game logs for testing data. For daily-driven detection, we separated total dataset into one day logs. Preliminary detects game-bots with one day logs, and determines total results by using these data. Daily driven detection advantages on detection which contains combined game playing style. Which shows like normal user and game-bot. These methodology shows better F1-score, which one of indicator which demonstrate classification accuracy. It increases from 0.898 to 0.945 by using Random Forest classifier.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1097-1114
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• 2015

As the online game market grows, illegal activities such as cheating play using game bots or game hack programs, running private servers, hacking game companies' system and network, and account theft are also increasing. There are various security measures for online games to prevent illegal activities. However, the current security measures are not enough to prevent all highly evolving game attacks and frauds. Some security measure can do harm game players usability, game companies need to develop usable security measure that is well fit to game genre and contents design. In this study, we surveyed the recent trend of various security measure applied in online games. This research also classified illegal activities and their related countermeasure for detection and prevention.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1131-1141
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• 2015

In this paper, we proposed a new approach of machine learning based method for detecting game-bots from normal players in MMORPG by inspecting the player's action log data especially in-game money increasing/decreasing event log data. DBSCAN (Density Based Spatial Clustering of Applications with Noise), an one of density based clustering algorithms, is used to extract the attributes of spatial characteristics of each players such as a number of clusters, a ratio of core points, member points and noise points. Most of all, even game-bot developers know principles of this detection system, they cannot avoid the system because moving a wide area to hunt the monster is very inefficient and unproductive. As the result, game-bots show definite differences from normal players in spatial characteristics such as very low ratio, less than 5%, of noise points while normal player's ratio of noise points is high. In experiments on real action log data of MMORPG, our game-bot detection system shows a good performance with high game-bot detection accuracy.

• Proceedings of the KAIS Fall Conference
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• 2012.05a
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• pp.293-295
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• 2012

IT기술의 발달과 함께 온라인 게임 시장은 시장 규모가 늘어나고 고부가가치 산업으로 인식되고 있다. 하지만, 악의적인 프로그램을 이용한 게임 운용은 정상적인 게임을 즐기는 사용자에게 큰 피해를 주고 있다. 본 논문은 온라인 게임 환경에서 비정상적인 게임 플레이를 위하여 사용되는 프로그램에 대하여 알아보고 게임 봇 탐지를 위한 다양한 연구 사례를 소개한다.

• Journal of Korea Game Society
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• v.15 no.4
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• pp.69-78
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• 2015

As the online game industry has been growing rapidly, more and more malicious activities to gain economic benefits have been reported as well. Game bot is one of the biggest problems in the online game industry. So we proposed a bot detection method based on the ERG theory of motivation for the first time. Most of the previous studies focused on behavior-based detection by monitoring patterns of the specific actions. In this paper, we applied the motivation theory to analyze user behaviors on a real game dataset. The result shows that normal users in the game followed the ERG theory of motivation in the same way as it works in real world. But in the case of game bots, the theory could not be applied because the game bot has specific reasons, unlike normal game users. We applied the ERG theory to users to distinguish game bot users from normal users. We detected the game bot with high accuracy of 99.78% by applying the theory.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.585-600
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• 2012

Security issues in online games are increasing as the online game industry grows. Real money trading (RMT) by online game users has become a security issue in several countries including Korea because RMT is related to criminal activities such as money laundering or tax evasion. RMT-related activities are done by professional work forces, namely gold-farmers, and many of them employ the automated program, bot, to gain cyber asset in a quick and efficient way. Online game companies try to prevent the activities of gold-farmers using game bots detection algorithm and block their accounts or IP addresses. However, game bot detection algorithm can detect a part of gold-farmer's network and IP address blocking also can be detoured easily by using the virtual private server or IP spoofing. In this paper, we propose a method to detect gold-farmer groups by analyzing their connection patterns to the online game servers, particularly information on their routing and source locations. We verified that the proposed method can reveal gold-farmers' group effectively by analyzing real data from the famous MMORPG.