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Multimodal game bot detection using user behavioral characteristics

Authors
Kang, Ah ReumJeong, Seong HoonMohaisen, AzizKim, Huy Kang
Issue Date
26-4월-2016
Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
Keywords
Online game security; Social network analysis; Behavior analysis; Data mining; MMORPG
Citation
SPRINGERPLUS, v.5
Indexed
SCIE
SCOPUS
Journal Title
SPRINGERPLUS
Volume
5
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/88892
DOI
10.1186/s40064-016-2122-8
ISSN
2193-1801
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 method based on user behavioral characteristics. The method 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.
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