Name
Abstract | ||
|---|---|---|
Game bots are programmed to automatically play games and illegally obtain profit, seriously affecting game experience of honest players and breaking the balance of game ecosystem. Therefore, bot detection needs to be addressed urgently, especially for MMORPGs, one of the most rapidly expanding genres of games. There have been some studies for bot detection, but the features they used are dependent on specific games and the methods cannot be generalized to other games. In this paper, we propose a trajectory based pre-trained model for game bot detection from game character trajectories and mouse trajectories, named T-Detector, which is independent to specific games and can be generalized to others. More specifically, we propose a pretrain method of LocationTime2Vec to learn representations of trajectories from huge unlabeled samples, which deeply embed spatial and temporal information hidden in trajectories. Moreover, we extract universal features based on behavioral differences in movement trajectories between human players and bots. We design an Angle Pretrain to extract features of turning angle, and propose an attention pooling module to extract features of moving speed and distance. Such features are not dependent on any specific game, enabling T-Detector to be generalized to many MMORPGs. Evaluated by two large-scale real-world datasets of 143,938 samples from two MMORPGs, T-Detector achieves the state-of-the-art performance in bot detection, and demonstrates powerful generalization ability. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ICDE53745.2022.00079 | 2022 IEEE 38th International Conference on Data Engineering (ICDE) |
Keywords | DocType | ISSN |
game bot detection,pretrain,feature extraction | Conference | 1063-6382 |
ISBN | Citations | PageRank |
978-1-6654-0884-4 | 0 | 0.34 |
References | Authors | |
18 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sha Zhao | 1 | 48 | 9.96 |
| Junwei Fang | 2 | 0 | 0.34 |
| Shiwei Zhao | 3 | 0 | 1.35 |
| Runze Wu | 4 | 0 | 0.68 |
| Jianrong Tao | 5 | 51 | 11.96 |
| Shijian Li | 6 | 1155 | 69.34 |
| Gang Pan | 7 | 1501 | 123.57 |