Deep learning and multivariate time series for cheat detection in video games
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/78032 |
Resumo: | Online video games drive a multi-billion dollar industry dedicated to maintaining a competitive and enjoyable experience for players. Traditional cheat detection systems struggle when facing new exploits or sophisticated fraudsters. More advanced solutions based on machine learning are more adaptive but rely heavily on in-game data, which means that each game has to develop its own cheat detection system. In this work, we propose a novel approach to cheat detection that doesn't require in-game data. Firstly, we treat the multimodal interactions between the player and the platform as multivariate time series. We then use convolutional neural networks to classify these time series as corresponding to legitimate or fraudulent gameplay. Our models achieve an average accuracy of respectively 99.2% and 98.9% in triggerbot and aimbot (two widespread cheats), in an experiment to validate the system's ability to detect cheating in players never seen before. Because this approach is based solely on player behavior, it can be applied to any game or input method, and even various tasks related to modeling human activity. |
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Deep learning and multivariate time series for cheat detection in video gamesDeep learningMultivariate time seriesHuman-computer interactionVideo gamesScience & TechnologyOnline video games drive a multi-billion dollar industry dedicated to maintaining a competitive and enjoyable experience for players. Traditional cheat detection systems struggle when facing new exploits or sophisticated fraudsters. More advanced solutions based on machine learning are more adaptive but rely heavily on in-game data, which means that each game has to develop its own cheat detection system. In this work, we propose a novel approach to cheat detection that doesn't require in-game data. Firstly, we treat the multimodal interactions between the player and the platform as multivariate time series. We then use convolutional neural networks to classify these time series as corresponding to legitimate or fraudulent gameplay. Our models achieve an average accuracy of respectively 99.2% and 98.9% in triggerbot and aimbot (two widespread cheats), in an experiment to validate the system's ability to detect cheating in players never seen before. Because this approach is based solely on player behavior, it can be applied to any game or input method, and even various tasks related to modeling human activity.- (undefined)SpringerUniversidade do MinhoPinto, Jose PedroPimenta, AndreNovais, Paulo2021-10-142021-10-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/78032engPinto, J.P., Pimenta, A. & Novais, P. Deep learning and multivariate time series for cheat detection in video games. Mach Learn 110, 3037–3057 (2021). https://doi.org/10.1007/s10994-021-06055-x0885-612510.1007/s10994-021-06055-xhttps://link.springer.com/article/10.1007/s10994-021-06055-xinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T11:57:02Zoai:repositorium.sdum.uminho.pt:1822/78032Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:46:43.222214Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Deep learning and multivariate time series for cheat detection in video games |
title |
Deep learning and multivariate time series for cheat detection in video games |
spellingShingle |
Deep learning and multivariate time series for cheat detection in video games Pinto, Jose Pedro Deep learning Multivariate time series Human-computer interaction Video games Science & Technology |
title_short |
Deep learning and multivariate time series for cheat detection in video games |
title_full |
Deep learning and multivariate time series for cheat detection in video games |
title_fullStr |
Deep learning and multivariate time series for cheat detection in video games |
title_full_unstemmed |
Deep learning and multivariate time series for cheat detection in video games |
title_sort |
Deep learning and multivariate time series for cheat detection in video games |
author |
Pinto, Jose Pedro |
author_facet |
Pinto, Jose Pedro Pimenta, Andre Novais, Paulo |
author_role |
author |
author2 |
Pimenta, Andre Novais, Paulo |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Pinto, Jose Pedro Pimenta, Andre Novais, Paulo |
dc.subject.por.fl_str_mv |
Deep learning Multivariate time series Human-computer interaction Video games Science & Technology |
topic |
Deep learning Multivariate time series Human-computer interaction Video games Science & Technology |
description |
Online video games drive a multi-billion dollar industry dedicated to maintaining a competitive and enjoyable experience for players. Traditional cheat detection systems struggle when facing new exploits or sophisticated fraudsters. More advanced solutions based on machine learning are more adaptive but rely heavily on in-game data, which means that each game has to develop its own cheat detection system. In this work, we propose a novel approach to cheat detection that doesn't require in-game data. Firstly, we treat the multimodal interactions between the player and the platform as multivariate time series. We then use convolutional neural networks to classify these time series as corresponding to legitimate or fraudulent gameplay. Our models achieve an average accuracy of respectively 99.2% and 98.9% in triggerbot and aimbot (two widespread cheats), in an experiment to validate the system's ability to detect cheating in players never seen before. Because this approach is based solely on player behavior, it can be applied to any game or input method, and even various tasks related to modeling human activity. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-10-14 2021-10-14T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/1822/78032 |
url |
https://hdl.handle.net/1822/78032 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pinto, J.P., Pimenta, A. & Novais, P. Deep learning and multivariate time series for cheat detection in video games. Mach Learn 110, 3037–3057 (2021). https://doi.org/10.1007/s10994-021-06055-x 0885-6125 10.1007/s10994-021-06055-x https://link.springer.com/article/10.1007/s10994-021-06055-x |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132223234899968 |