Show Me Your Account: Detecting MMORPG Game Bot Leveraging Financial Analysis with LSTM

Kyung Ho Park, Eunjo Lee, Huy Kang Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationInformation Security Applications - 20th International Conference, WISA 2019, Revised Selected Papers
EditorsIlsun You
Number of pages11
ISBN (Print)9783030393021
Publication statusPublished - 2020
Event20th World Conference on Information Security Applications, WISA 2019 - Jeju Island, Korea, Republic of
Duration: 2019 Aug 212019 Aug 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11897 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th World Conference on Information Security Applications, WISA 2019
Country/TerritoryKorea, Republic of
CityJeju Island

Bibliographical note

Funding Information:
This work was supported under the framework of international cooperation program managed by National Research Foundation of Korea (No. 2017K1A3A1A17092614).

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.


  • Game bot detection
  • LSTM neural networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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