Crime Scene Reconstruction: Online Gold Farming Network Analysis

Hyukmin Kwon, Aziz Mohaisen, Jiyoung Woo, Yongdae Kim, Eunjo Lee, Huy Kang Kim

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or 'real money trading' through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.

Original languageEnglish
Article number7727944
Pages (from-to)544-556
Number of pages13
JournalIEEE Transactions on Information Forensics and Security
Volume12
Issue number3
DOIs
Publication statusPublished - 2017 Mar 1

Fingerprint

Crime
Electric network analysis
Gold
Ecosystems

Keywords

  • game bot
  • gold farming group
  • MMORPG
  • Online games

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

Cite this

Crime Scene Reconstruction : Online Gold Farming Network Analysis. / Kwon, Hyukmin; Mohaisen, Aziz; Woo, Jiyoung; Kim, Yongdae; Lee, Eunjo; Kim, Huy Kang.

In: IEEE Transactions on Information Forensics and Security, Vol. 12, No. 3, 7727944, 01.03.2017, p. 544-556.

Research output: Contribution to journalArticle

Kwon, Hyukmin ; Mohaisen, Aziz ; Woo, Jiyoung ; Kim, Yongdae ; Lee, Eunjo ; Kim, Huy Kang. / Crime Scene Reconstruction : Online Gold Farming Network Analysis. In: IEEE Transactions on Information Forensics and Security. 2017 ; Vol. 12, No. 3. pp. 544-556.
@article{873c42d698f44ac4ae545d0588c524c3,
title = "Crime Scene Reconstruction: Online Gold Farming Network Analysis",
abstract = "Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or 'real money trading' through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.",
keywords = "game bot, gold farming group, MMORPG, Online games",
author = "Hyukmin Kwon and Aziz Mohaisen and Jiyoung Woo and Yongdae Kim and Eunjo Lee and Kim, {Huy Kang}",
year = "2017",
month = "3",
day = "1",
doi = "10.1109/TIFS.2016.2623586",
language = "English",
volume = "12",
pages = "544--556",
journal = "IEEE Transactions on Information Forensics and Security",
issn = "1556-6013",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

TY - JOUR

T1 - Crime Scene Reconstruction

T2 - Online Gold Farming Network Analysis

AU - Kwon, Hyukmin

AU - Mohaisen, Aziz

AU - Woo, Jiyoung

AU - Kim, Yongdae

AU - Lee, Eunjo

AU - Kim, Huy Kang

PY - 2017/3/1

Y1 - 2017/3/1

N2 - Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or 'real money trading' through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.

AB - Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or 'real money trading' through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.

KW - game bot

KW - gold farming group

KW - MMORPG

KW - Online games

UR - http://www.scopus.com/inward/record.url?scp=85007042372&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85007042372&partnerID=8YFLogxK

U2 - 10.1109/TIFS.2016.2623586

DO - 10.1109/TIFS.2016.2623586

M3 - Article

AN - SCOPUS:85007042372

VL - 12

SP - 544

EP - 556

JO - IEEE Transactions on Information Forensics and Security

JF - IEEE Transactions on Information Forensics and Security

SN - 1556-6013

IS - 3

M1 - 7727944

ER -