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http://dx.doi.org/10.13089/JKIISC.2017.27.6.1431

Using Image Visualization Based Malware Detection Techniques for Customer Churn Prediction in Online Games  

Yim, Ha-bin (Center for Information Security Technologies(CIST), Korea University)
Kim, Huy-kang (Center for Information Security Technologies(CIST), Korea University)
Kim, Seung-joo (Center for Information Security Technologies(CIST), Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.27, no.6, 2017 , pp. 1431-1439 More about this Journal
Abstract
In the security field, log analysis is important to detect malware or abnormal behavior. Recently, image visualization techniques for malware dectection becomes to a major part of security. These techniques can also be used in online games. Users can leave a game when they felt bad experience from game bot, automatic hunting programs, malicious code, etc. This churning can damage online game's profit and longevity of service if game operators cannot detect this kind of events in time. In this paper, we propose a new technique of PNG image conversion based churn prediction to improve the efficiency of data analysis for the first. By using this log compression technique, we can reduce the size of log files by 52,849 times smaller and increase the analysis speed without features analysis. Second, we apply data mining technique to predict user's churn with a real dataset from Blade & Soul developed by NCSoft. As a result, we can identify potential churners with a high accuracy of 97%.
Keywords
Log Analysis; Visualization; Online Game Data Mining; Churn Prediction;
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