An investigation of biometric-based user predictability in the online game League of Legends

Detalhes bibliográficos
Ano de defesa: 2019
Autor(a) principal: Silva, Valmiro Ribeiro da
Orientador(a): Abreu, Marjory Cristiany da Costa
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Não Informado pela instituição
Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM SISTEMAS E COMPUTAÇÃO
Departamento: Não Informado pela instituição
País: Brasil
Palavras-chave em Português:
Área do conhecimento CNPq:
Link de acesso: https://repositorio.ufrn.br/jspui/handle/123456789/26974
Resumo: Computer games have been consolidated as a favourite activity for years now. Although such games were created to promote competition and promote self-improvement, there are some recurrent issues. One that has received the least amount of attention so far is the problem of "account sharing" which is when a player shares his/her account with more experienced players in order to progress in the game. The companies running those games tend to punish this behaviour, but this specific case is hard to identify. Since, the popularity of machine learning techniques have never been higher, the aim of this study is to better understand how biometric data from online games behaves, to understand how the choice of character impacts a player and how different algorithms perform when we vary how frequently a sample is collected. The experiments showed through the use of statistic tests how consistent a player can be even when he/she changes characters or roles, what are the impacts of more training samples, how the tested machine learning algorithms results are affected by how often we collect our samples, and how dimensionality reduction techniques, such as Principal Component Analysis affect our data, all providing more information about how this state of art game database works.