TY - GEN
T1 - Show Me Your Account
T2 - 20th World Conference on Information Security Applications, WISA 2019
AU - Park, Kyung Ho
AU - Lee, Eunjo
AU - Kim, Huy Kang
N1 - 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.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Game bot detection
KW - LSTM neural networks
KW - MMORPG
UR - http://www.scopus.com/inward/record.url?scp=85079085390&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39303-8_1
DO - 10.1007/978-3-030-39303-8_1
M3 - Conference contribution
AN - SCOPUS:85079085390
SN - 9783030393021
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 13
BT - Information Security Applications - 20th International Conference, WISA 2019, Revised Selected Papers
A2 - You, Ilsun
PB - Springer
Y2 - 21 August 2019 through 24 August 2019
ER -