A Behavior Analysis-Based Game Bot Detection Approach Considering Various Play Styles
Yeounoh Chung, Chang-yong Park, Noo-ri Kim, Hana Cho, Taebok Yoon,, Hunjoo Lee, Jee-Hyong Lee

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,…
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