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http://dx.doi.org/10.13089/JKIISC.2021.31.6.1097

Detecting Daily-Driven Game-Bot Based on Online Game Play Log Clustering  

Kim, Joo Hwan (Graduate School of Information Security, Korea University)
Choi, Jin-Young (Graduate School of Information Security, Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.31, no.6, 2021 , pp. 1097-1104 More about this Journal
Abstract
Online game-bots are already known for a lot of persons by various ways. It leads to problems such as declining game player's interest, in-game financial crisis, etc. Detecting and restricting of game-bot is now essential. Because both publishers and players get disadvantages from their long term abnormal working. But it is not easy to restrict, because of false restriction risks. Game publishers need to distinguish game-bot from server-side game logs. At last, it should can make reasons for game-bot restriction. In this paper, we classified game-bot users by using daily separated game logs for testing data. For daily-driven detection, we separated total dataset into one day logs. Preliminary detects game-bots with one day logs, and determines total results by using these data. Daily driven detection advantages on detection which contains combined game playing style. Which shows like normal user and game-bot. These methodology shows better F1-score, which one of indicator which demonstrate classification accuracy. It increases from 0.898 to 0.945 by using Random Forest classifier.
Keywords
Online-game security; Bot detection; Data clustering; Categorizing;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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