Multimodal Game Bot Detection using User Behavioral Characteristics

TL;DR

This paper presents a novel method for detecting game bots in MMORPGs by analyzing user behavioral patterns, achieving over 96% accuracy in real-world data, thereby enhancing online game security.

Contribution

It introduces a behavioral characteristic-based detection methodology specifically designed for MMORPG game bots, validated on real data with high accuracy.

Findings

Detection accuracy reached 96.06% on banned accounts.

Behavioral analysis effectively distinguishes bots from human players.

Repetitive gold farming tasks are key indicators of bots.

Abstract

As the online service industry has continued to grow, illegal activities in the online world have drastically increased and become more diverse. Most illegal activities occur continuously because cyber assets, such as game items and cyber money in online games, can be monetized into real currency. The aim of this study is to detect game bots in a Massively Multiplayer Online Role Playing Game (MMORPG). We observed the behavioral characteristics of game bots and found that they execute repetitive tasks associated with gold farming and real money trading. We propose a game bot detection methodology based on user behavioral characteristics. The methodology of this paper was applied to real data provided by a major MMORPG company. Detection accuracy rate increased to 96.06% on the banned account list.