Chatting Pattern Based Game BOT Detection: Do They Talk Like Us?
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Ah Reum | - |
dc.contributor.author | Kim, Huy Kang | - |
dc.contributor.author | Woo, Jiyoung | - |
dc.date.accessioned | 2021-09-06T12:56:21Z | - |
dc.date.available | 2021-09-06T12:56:21Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2012-11-30 | - |
dc.identifier.issn | 1976-7277 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/106906 | - |
dc.description.abstract | Among the various security threats in online games, the use of game bots is the most serious problem. Previous studies on game bot detection have proposed many methods to find out discriminable behaviors of bots from humans based on the fact that a bot's playing pattern is different from that of a human. In this paper, we look at the chatting data that reflects gamers' communication patterns and propose a communication pattern analysis framework for online game bot detection. In massive multi-user online role playing games (MMORPGs), game bots use chatting message in a different way from normal users. We derive four features; a network feature, a descriptive feature, a diversity feature and a text feature. To measure the diversity of communication patterns, we propose lightly summarized indices, which are computationally inexpensive and intuitive. For text features, we derive lexical, syntactic and semantic features from chatting contents using text mining techniques. To build the learning model for game bot detection, we test and compare three classification models: the random forest, logistic regression and lazy learning. We apply the proposed framework to AION operated by NCsoft, a leading online game company in Korea. As a result of our experiments, we found that the random forest outperforms the logistic regression and lazy learning. The model that employs the entire feature sets gives the highest performance with a precision value of 0.893 and a recall value of 0.965. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | KSII-KOR SOC INTERNET INFORMATION | - |
dc.subject | CENTRALITY | - |
dc.title | Chatting Pattern Based Game BOT Detection: Do They Talk Like Us? | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Huy Kang | - |
dc.contributor.affiliatedAuthor | Woo, Jiyoung | - |
dc.identifier.doi | 10.3837/tiis.2012.11.007 | - |
dc.identifier.scopusid | 2-s2.0-84870717762 | - |
dc.identifier.wosid | 000313526300007 | - |
dc.identifier.bibliographicCitation | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, v.6, no.11, pp.2866 - 2879 | - |
dc.relation.isPartOf | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | - |
dc.citation.title | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | - |
dc.citation.volume | 6 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 2866 | - |
dc.citation.endPage | 2879 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART001982445 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.description.journalRegisteredClass | other | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | CENTRALITY | - |
dc.subject.keywordAuthor | Online game security | - |
dc.subject.keywordAuthor | game bot | - |
dc.subject.keywordAuthor | MMORPG | - |
dc.subject.keywordAuthor | data mining | - |
dc.subject.keywordAuthor | text mining | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.