Quick and easy game bot detection based on action time interval estimation

Yong Goo Kang, Huy Kang Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.

Original languageEnglish
Pages (from-to)713-723
Number of pages11
JournalETRI Journal
Issue number4
Publication statusPublished - 2023 Aug

Bibliographical note

Funding Information:
This study is supported by Korea University Grant.

Publisher Copyright:
1225-6463/$ © 2022 ETRI.


  • consumer behavior
  • detection algorithms
  • machine learning
  • online game bot
  • predictive models

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • General Computer Science
  • Electrical and Electronic Engineering


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