A Behavior Analysis-Based Game Bot Detection Approach Considering Various Play Styles
This addresses bot detection for MMORPG operators to reduce server workloads, but it is incremental as it builds on existing behavior analysis methods.
The paper tackles the problem of detecting game bots in MMORPGs by grouping players based on behavioral similarities and developing local detection models for each group, which improves accuracy as shown in experiments with real service data.
An approach for game bot detection in MMORPGs is proposed based on the analysis of game playing behavior. Since MMORPGs are large scale games, users can play in various ways. This variety in playing behavior makes it hard to detect game bots based on play behaviors. In order to cope with this problem, the proposed approach observes game playing behaviors of users and groups them by their behavioral similarities. Then, it develops a local bot detection model for each player group. Since the locally optimized models can more accurately detect game bots within each player group, the combination of those models brings about overall improvement. For a practical purpose of reducing the workloads of the game servers in service, the game data is collected at a low resolution in time. Behavioral features are selected and developed to accurately detect game bots with the low resolution data, considering common aspects of MMORPG playing. Through the experiment with the real data from a game currently in service, it is shown that the proposed local model approach yields more accurate results.