  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.
