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.
|Title of host publication||Information Security Applications - 20th International Conference, WISA 2019, Revised Selected Papers|
|Number of pages||11|
|Publication status||Published - 2020|
|Event||20th World Conference on Information Security Applications, WISA 2019 - Jeju Island, Korea, Republic of|
Duration: 2019 Aug 21 → 2019 Aug 24
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||20th World Conference on Information Security Applications, WISA 2019|
|Country/Territory||Korea, Republic of|
|Period||19/8/21 → 19/8/24|
Bibliographical noteFunding Information:
This work was supported under the framework of international cooperation program managed by National Research Foundation of Korea (No. 2017K1A3A1A17092614).
© 2020, Springer Nature Switzerland AG.
- Game bot detection
- LSTM neural networks
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)