• Journal of Korea Game Society
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• v.15 no.3
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• pp.177-188
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• 2015

In-game cheating by the use of unauthorized software programs has always been a big problem that they can damage in First Person Shooting games, although companies operate a variety of client security solutions in order to prevent games from the cheating attempts. This paper proposes a method for detecting cheaters in FPS games by using game log analysis in a server-side. To accomplish this, we did a comparative analysis of characteristics between cheaters and general users focused on commonly loaded logs in the game. We proposed a cheating detection model by using artificial neural network algorithm. In addition, we did the performance evaluation of the proposed model by using the real dataset used in business.

• Journal of KIISE
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• v.45 no.3
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• pp.234-241
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• 2018

The study of classifying gamer types or analyzing the characteristics of gamers is a field of interest for data analysis researchers. From the past to the present, much research has been done on gamer categorization and gamer analysis. However, most studies use surveys or bio-signals, which is not practical because it is difficult to obtain large amounts of data. Even if the game log is used, it is difficult to analyze the psychology of the gamer because the gamer is categorized and analyzed by extracting only statistical values. However, if we can extract the cognitive psychology information of the gamer from the basic game log, we can analyze the gamer more intuitively and easily. In this paper, we extracted eight cognitive psychological features representing the behavior and psychological information of the gamer using Crazy Dragon's game log, which is a mobile Role-Playing-Game (RPG). In addition, we classified gamers based upon cognitive psychological features and analyzed them using eight cognitive psychological features. As a result, most gamers were highly correlated with one or two types.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1431-1439
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• 2017

In the security field, log analysis is important to detect malware or abnormal behavior. Recently, image visualization techniques for malware dectection becomes to a major part of security. These techniques can also be used in online games. Users can leave a game when they felt bad experience from game bot, automatic hunting programs, malicious code, etc. This churning can damage online game's profit and longevity of service if game operators cannot detect this kind of events in time. In this paper, we propose a new technique of PNG image conversion based churn prediction to improve the efficiency of data analysis for the first. By using this log compression technique, we can reduce the size of log files by 52,849 times smaller and increase the analysis speed without features analysis. Second, we apply data mining technique to predict user's churn with a real dataset from Blade & Soul developed by NCSoft. As a result, we can identify potential churners with a high accuracy of 97%.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.733-738
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• 2020

It is really hard to find an infant's App, especially for the age under 5, even though there are lots of Apps developed and distributed nowadays. The selection of the proper infant's App is difficult since the infants' App should be useful, safe and helpful for the development of their intelligence. In this research, we design the useful infant's App for the development of their intelligence by applying the AI technology for increasing the learning effect in order to satisfy the characteristics of the infants' needs. A proposed App is the collection of interesting games for infants such as picture puzzle game, coloring shapes game, pasting stickers game, and fake mobile phone feature enables them to play interesting phone game. Furthermore, the proposed App is also designed to collect and analyze the log information generated while they are playing games, share and compare with other infants' log information to increase the learning effect. After then, it figures out and learns their game tendency, intelligibility, workmanship, and apply them to the next game in order to increase their interests and concentration of the game.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.1013-1018
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• 2022

Game Industry sustained growth for some time, but the lifespan of a game is shortening. Various efforts to improve the quality of services for the game players which play a role in extending the lifespan of games. When a game is serviced, the server of the game starts to store log informations, and the stored data became important measures to predict game user's activities. As the game's data gathers, it becomes highly useful big data. By analyzing the data of the game stored in this way, a game service issue analysis procedure is proposed to improve the quality of the game service and to proceed with a better service, and based on the analysis in this way, it was applied to the balance test process and verified through expert to the balance test process. If the log analysis process is applied through this paper, it will be a basic data that can improve the quality of game services.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.85-87
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• 2021

This work seeks to extract items necessary for the competitive structure of content or the balance of content through a study of the balance between users in MMORPG. The balance of the game is used in various terms and its definitions are different. To solve these problems, we conducted a literature study on the definition of balance, which extracted the log entries needed for balance. The big data of the game has very important research value, but it is not being utilized properly. Through this study, we hope to collect systematic log data and utilize it for real game operations.

• 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 Korea Information Processing Society Conference
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• 2014.11a
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• pp.294-297
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• 2014

리테일 업계의 최대 화두는 다양한 미디어 채널을 상호 연계하여 방문고객체험을 극대화시키는 것이다. 리테일 매장을 방문하는 고객들은 이미 스마트폰을 사용하여 관심상품을 미리 조회해보고, 상품평도 살펴본다. 본 논문에서는 리테일 매장 내 게임체험 전용공간을 마련하고 방문고객의 게임 앱 사용행태를 분석한다. 이를 바탕으로 리테일 매장을 게임 홍보채널로 활용하고자 하는 욕구를 가지는 게임 스타트업 업체에게 사용자 체험 극대화를 위한 제안 및 분석자료를 마련한다.

• 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 Society of Computer and Information
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• v.27 no.5
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• pp.157-163
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• 2022

As the online game market grows, the use of game bots is causing the most serious problem for game services. We propose a harvest coordinate analysis model to detect harvesting bots among game bots of the Massively Multiplayer Online Role-Playing Games(MMORPGs) genre. The proposed model analyzes the player's harvesting behavior using the coordinate data. Game bots can obtain in-game goods and items more easily than normal players and are not affected by realistic restrictions such as sleep time and character manipulation fatigue. As a result, there is a difference in harvesting coordinates between normal players and game bots. We divided the coordinate zones and used these coordinate zone differences to distinguish between game bot players and normal players. We created a dataset with NCSoft's AION log and applied it to a random forest model to detect game bots, and as a result, we derived performance with a recall of 0.72 and a precision of 0.92.