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http://dx.doi.org/10.13089/JKIISC.2015.25.5.1077

A study on hard-core users and bots detection using classification of game character's growth type in online games  

Lee, Jin (Graduate School of Information Security, Korea University)
Kang, Sung Wook (Graduate School of Information Security, Korea University)
Kim, Huy Kang (Graduate School of Information Security, Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.25, no.5, 2015 , pp. 1077-1084 More about this Journal
Abstract
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.
Keywords
Bot detection; User behavior analysis; Hard-core user; Data mining; MMORPG;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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