  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.
