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

• Autor(a) principal: Silva, Valmiro Ribeiro da
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFRN
• Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/26974
• topic: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO; Biometrics; Keystroke dynamics; Mouse dynamics; Dimensionality reduction; User verification; League of legends; Insider treat

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.