Crime Scene Reconstruction: Online Gold Farming Network Analysis

Kwon, Hyukmin; Mohaisen, Aziz; Woo, Jiyoung; Kim, Yong‐Dae; Lee, Eunjo; Kim, Huy Kang

doi:10.1109/tifs.2016.2623586

Cited by 30 publications

(19 citation statements)

References 18 publications

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“…Compared to the changes and importance in the industry, however, it is quite difficult to find published research results on game and its churn analysis, especially for casual games. For sophisticated games, such as MMORPG (massively multiplayer online role-playing games), research results are available for churn causes including bots and cheating [ 12 ][ 13 ], account theft [ 14 ], and cyberbullying [ 15 ][ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Churn prediction of mobile and online casual games using play log data

et al. 2017

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Internet-connected devices, especially mobile devices such as smartphones, have become widely accessible in the past decade. Interaction with such devices has evolved into frequent and short-duration usage, and this phenomenon has resulted in a pervasive popularity of casual games in the game sector. On the other hand, development of casual games has become easier than ever as a result of the advancement of development tools. With the resulting fierce competition, now both acquisition and retention of users are the prime concerns in the field. In this study, we focus on churn prediction of mobile and online casual games. While churn prediction and analysis can provide important insights and action cues on retention, its application using play log data has been primitive or very limited in the casual game area. Most of the existing methods cannot be applied to casual games because casual game players tend to churn very quickly and they do not pay periodic subscription fees. Therefore, we focus on the new players and formally define churn using observation period (OP) and churn prediction period (CP). Using the definition, we develop a standard churn analysis process for casual games. We cover essential topics such as pre-processing of raw data, feature engineering including feature analysis, churn prediction modeling using traditional machine learning algorithms (logistic regression, gradient boosting, and random forests) and two deep learning algorithms (CNN and LSTM), and sensitivity analysis for OP and CP. Play log data of three different casual games are considered by analyzing a total of 193,443 unique player records and 10,874,958 play log records. While the analysis results provide useful insights, the overall results indicate that a small number of well-chosen features used as performance metrics might be sufficient for making important action decisions and that OP and CP should be properly chosen depending on the analysis goal.

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Section: Introductionmentioning

confidence: 99%

Churn prediction of mobile and online casual games using play log data

et al. 2017

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“…We interpret features used in our model are commonly found. Previous research on bot network also suggested financial features are common traces of modern MMORPGs [14]. Therefore, we expect our model is applicable in other games to be used in industry-level practices.…”

Section: Experiments Resultsmentioning

confidence: 85%

Show Me Your Account: Detecting MMORPG Game Bot Leveraging Financial Analysis with LSTM

Lee

Kim

2020

Information Security Applications

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With the rapid growth of MMORPG market, game bot detection has become an essential task for maintaining stable in-game ecosystem. To classify bots from normal users, detection methods are proposed in both game client and serverside. Among various classification methods, data mining method in server-side captured unique characteristics of bots efficiently. For features used in data mining, behavioral and social actions of character are analyzed with numerous algorithms. However, bot developers can evade the previous detection methods by changing bot's activities continuously. Eventually, overall maintenance cost increases because the selected features need to be updated along with the change of bot's behavior.To overcome this limitation, we propose improved bot detection method with financial analysis. As bot's activity absolutely necessitates the change of financial status, analyzing financial fluctuation effectively captures bots as a key feature. We trained and tested model with actual data of Aion, a leading MMORPG in Asia. Leveraging that LSTM efficiently recognizes timeseries movement of data, we achieved meaningful detection performance. Further on this model, we expect sustainable bot detection system in the near future.

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“…Kwon et al [99] focused on how players earn money in online games. In such games, an unofficial market sells recently raised money, creating gold farming groups (GFGs).…”

Section: F Cybercrime Detection Using Data Miningmentioning

confidence: 99%

Comprehensive Review of Cybercrime Detection Techniques

et al. 2020

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Cybercrimes describe cases of indictable offences and misdemeanours in which computer or any communication tools are involved as targets, commission instruments, incidental to, or that cases are associated with the prevalence of computer technology. Common forms of cybercrimes could be child pornography, cyberstalking, identity theft, cyber laundering, credit card theft, cyber terrorism, drug sale, data leakage, sexually explicit content, phishing and other cyber hacking. These kinds of cybercrimes are mostly leading to breaching users' privacy, security violation, business loss, financial fraud, or damage in public as well as government properties. Hence, this paper intensively reviews cybercrime detection and prevention methods. It first explores the different types of cybercrimes then discusses their threats against privacy and security in computer systems. It also describes the strategies that cybercriminals might utilize in committing these crimes against individuals, organizations, and societies. The paper then reviews the existing techniques of cybercrime detection and prevention. It objectively discusses the strengths and critically analyses the vulnerabilities of each technique. As a future study, the paper provides recommendations for the development of cybercrime detection model in which it is capable to effectively detect cybercrime in comparison to the existing techniques.

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Crime Scene Reconstruction: Online Gold Farming Network Analysis

Cited by 30 publications

References 18 publications

Churn prediction of mobile and online casual games using play log data

Churn prediction of mobile and online casual games using play log data

Show Me Your Account: Detecting MMORPG Game Bot Leveraging Financial Analysis with LSTM

Comprehensive Review of Cybercrime Detection Techniques

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