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RISS 인기검색어
- 검색키워드
- 주제어 : Game bot detection (검색결과 4 건)
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Uncovering how game bots get detected through Explainable Artificial Intelligence (XAI)
박은지 Graduate School of Cybersecurity, Korea University 2021 국내석사
Game bots are either bots using automated hardware or artificial intelligence bots using software for collecting assets in a game. Bots disturb other game players and destroy the environmental system of the games. For these reasons, the game industry has long had problems with game bots. The game industry put its best efforts into detecting the game bots using activity history in a learning-based detection method. These detection methods have captured game bots with high performance; however, they do not provide a reasonable explanation of the detection results. To solve this problem, in this paper, we investigate the explainabilities of game bot detection, utilizing a dataset from MMORG game AION, which includes both game logs from normal players and game bots. We conduct the detection of game bots through two classification models and analyze the detection process by applying explainable AI modules. We propose the verification of the explanation of the bot’s behavior, and the truthfulness has been evaluated. Besides, explainability contributes to minimizing false detection.
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Game bot detection using data mining techniques in the MMORPG
강아름 Korea University 2016 국내박사
As the online service industry grows, illegal activities in the online world have drastically increased and become more diverse. Most of the illegal activities are due to the fact that cyber assets such as game items and cyber money in online games can be monetized into real currency. The aim of this thesis is to detect game bots in a Massively Multiplayer Online Role Playing Game (MMORPG). Game activity logs such as the party play and the chat data were used to discriminate abnormal users like game bots from normal users. Through a comprehensive statistical and data mining analysis of user behaviors, the game bot’s behavioral characteristics were investigated. Since these game bots execute repetitive tasks associated with gold-farming and real-money trading, the behavioral differences of game bots and human users were observed. This thesis proposes the game bot detection methodology based on users’ behavioral characteristics. The methodology of this thesis was applied to real data provided by a major MMORPG company. The detection accuracy rate was up to 96.06% on the banned account list.
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(A) study of RMT buyer detection for the collapse of GFG in MMORPG
강성욱 高麗大學校 情報保護大學院 2016 국내석사
A RMT (Real Money Trade) user begins to show up as the popularity of online games increases. As the number of RMT users increase, GFG (Gold Farming roup) who abuse RMT also increase. The GFG, a criminal group in the game, needs many bot characters to gather game money. It causes social problems such as identity theft and privacy leaks. They induce RMT that causes in-game problems such as destruction of game economy and account theft. Therefore, It is very important work to eliminate GFG for online game security. All of the previous studies to eliminate GFG are based on supply side detection. In this paper, we focused on demand side detection to collapse GFG by detecting RMT buyers. If RMT buyers decrease, the GFG will collapse at the perspective of Law of Demand and Supply. So we proposed the rule-based RMT buyer detection for the first time. By analyzing an actual game data set, we found two types of RMT behavior and detected RMT buyers with high recall ratio of 98%. In addition, we found game bots with an accuracy of 95%.
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Hard-core user and bot user classification using game character's growth types
이진 高麗大學校 情報保護大學院 2016 국내석사
Game bots collect items and money unfairly and deplete the in-game contents rapidly. As a result, honest users feel deprived and leave the game. It affects a lifetime of the online game and downturn in the game market. Moreover, recent game bots steal the gamer's personal information and cause account thefts problems. There have been various countermeasures to detect game bots. However, misclassification between game bots and hard-core users is the well-known problem for a long time. In this paper, we define the growth types by analyzing the growth processes of users with the Aion dataset, one of the famous MMORPGs in the world. We propose a framework that classifies hard-core users and game bots in the growth patterns. As a result, we successfully distinguish game bots from hard-core users with high accuracy value.
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