Show Me Your Account: Detecting MMORPG Game Bot Leveraging Financial Analysis with LSTM
This addresses the issue of bot evasion in online games for game developers, though it is incremental as it builds on existing data mining approaches.
The paper tackled the problem of detecting game bots in MMORPGs by proposing a method based on financial analysis using LSTM, achieving meaningful detection performance on actual data from the game Aion.
With the rapid growth of MMORPG market, game bot detection has become an essential task for maintaining stable in-game ecosystem. To classify bots from normal users, detection methods are proposed in both game client and server-side. Among various classification methods, data mining method in server-side captured unique characteristics of bots efficiently. For features used in data mining, behavioral and social actions of character are analyzed with numerous algorithms. However, bot developers can evade the previous detection methods by changing bot's activities continuously. Eventually, overall maintenance cost increases because the selected features need to be updated along with the change of bot's behavior. To overcome this limitation, we propose improved bot detection method with financial analysis. As bot's activity absolutely necessitates the change of financial status, analyzing financial fluctuation effectively captures bots as a key feature. We trained and tested model with actual data of Aion, a leading MMORPG in Asia. Leveraging that LSTM efficiently recognizes time-series movement of data, we achieved meaningful detection performance. Further on this model, we expect sustainable bot detection system in the near future.