A Behavior Analysis-Based Game Bot Detection Approach Considering Various Play Styles

TL;DR

This paper presents a behavior analysis-based method for detecting game bots in MMORPGs by grouping players by behavior and developing local detection models, which improves accuracy using low-resolution data.

Contribution

It introduces a novel approach that groups players by behavior and creates local models for more precise bot detection in large-scale MMORPGs.

Findings

Local models improve detection accuracy

Behavioral grouping enhances model effectiveness

Low-resolution data suffices for accurate detection

Abstract

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.