| Title |
Cumulative Log based Online Game Bot Detection System using Machine Learning Model |
| Authors |
최준영(Jun-Young Choi) ; 서창진(Chang-Jin Seo) |
| DOI |
https://doi.org/10.5370/KIEEP.2021.70.2.108 |
| Keywords |
Machine learning; Game bot; Log data cumulative; XgBoost |
| Abstract |
The online game market is steadily growing, and as such, there are active financial transactions to buy and sell game items. This paper focuses on developing an intelligent game bot detection system based on machine learning to detect game bots that collect game experience values and items illegally in online games. The proposed research method was constructed by reducing the learning data dimension using big data log information from RPG, AION games provided by KISA's “K-Cyber Security Challenge” for intelligent game bot detection, and experiments were conducted using XgBoost and Random Forest models. Experiments show that the game bot detection performance of the proposed method with 97.7% of the results using a total of 3,761 account logs through visualization through correlation coefficient analysis of the data set accumulated with log information. The proposed research is currently applicable to the RPG (Role Playing Game) game bot detection field as a game bot detection method using machine learning technology. |