Game bot detection using user behavioral characteristics
As the online service industry has continued to grow, illegal activities in the online world have drastically increased and become more diverse. Most illegal activities occur continuously because cyber assets, such as game items and cyber money in online games, can be monetized into real currency. The aim of this study is to detect game bots in a massively multiplayer online role playing game (MMORPG). We observed the behavioral characteristics of game bots and found that they execute repetitive tasks associated with gold farming and real money trading. We propose a game bot detection method based on user behavioral characteristics. The method of this paper was applied to real data provided by a major MMORPG company. Detection accuracy rate increased to 96.06 % on the banned account list.
To perform this study, we rely on a real-world dataset obtained from the operation of Aion, a popular game. Our Game Bot Detection dataset contains all features used in our paper, which were generated from in-game action logs for 88 days, between April 9th and July 5th of 2010. During this period, there were 49,739 characters that played more than 3 h. Among these players, 7702 characters were game bots, identified and labeled by the game company. The banned list was provided by the game company to serve as the ground truth, and each banned user has been vetted and verified by human labor and active monitoring.
1. Data Set Release
For academic purposes, we are happy to release our Dataset. If you use our dataset for your experiment, please cite our paper.
Dataset Download Link: Google Forms
The dataset contains the following feature and results.
1.1. Data Processing
Player information features
-> (Data aggregation) -> Results
Player actions features
-> (Frequency & ratio analysis) -> Results
Group activities features
-> (Data aggregation) -> Results
Social interaction diversity features
-> (Entropy analysis) -> Results
Network measures features
-> (Clustering) -> Results
1.2. Feature Selection
Best-first Search Weka Result
Greedy-stepwise Search Weka Result
Information Gain Ranking filter Weka Result
1.3. Classification & Evaluation
Decision tree with Feature_Set1 Weka Result
Random forest with Feature_Set1 Weka Result
Logistic regression with Feature_Set1 Weka Result
Naïve Bayes with Feature_Set1 Weka Result
Decision tree with Feature_Set2 Weka Result
Random forest with Feature_Set2 Weka Result
Logistic regression with Feature_Set2 Weka Result
Naïve Bayes with Feature_Set2 Weka Result
Decision tree with Feature_Set3 Weka Result
Random forest with Feature_Set3 Weka Result
Logistic regression with Feature_Set3 Weka Result
Naïve Bayes with Feature_Set3 Weka Result
Kang, A. R., Jeong, S. H., Mohaisen, A., & Kim, H. K. (2016). Multimodal game bot detection using user behavioral characteristics. SpringerPlus, 5(1), 1-19.
You may want to see the following papers that used our dataset!
 M. L. Bernardi, M. Cimitile, F. Martinelli, and F. Mercaldo, “An ensemble fuzzy logic approach to game bot detection through behavioural features,” IEEE Int. Conf. Fuzzy Syst., vol. 2018-July, pp. 1–9, 2018. DOI: 10.1109/FUZZ-IEEE.2018.8491615
 M. L. Bernardi, M. Cimitile, F. Martinelli, and F. Mercaldo, “A time series classification approach to game bot detection,” in Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics - WIMS ’17, 2017, pp. 1–11. DOI: 10.1145/3102254.3102263
Ah Reum Kang (armk at hksecurity.net ) or Huy Kang Kim (cenda at korea.ac.kr)