Multimodal Game Bot Detection using User Behavioral Characteristics
This addresses illegal activities like gold farming and real money trading in online games, which monetize cyber assets into real currency, but it is incremental as it applies existing behavioral analysis methods to a specific domain.
The study tackled the problem of detecting game bots in MMORPGs by analyzing user behavioral characteristics, achieving a detection accuracy rate of 96.06% on a banned account list.
As the online service industry has continued to grow, illegal activities in the online world have drastically increased and become more diverse. Most illegal activities occur continuously because cyber assets, such as game items and cyber money in online games, can be monetized into real currency. The aim of this study is to detect game bots in a Massively Multiplayer Online Role Playing Game (MMORPG). We observed the behavioral characteristics of game bots and found that they execute repetitive tasks associated with gold farming and real money trading. We propose a game bot detection methodology based on user behavioral characteristics. The methodology of this paper was applied to real data provided by a major MMORPG company. Detection accuracy rate increased to 96.06% on the banned account list.