An empirical biometric-based study for user identification with different neural networks in the online game League of Legends

The popularity of computer games has grown exponentially in the last years. 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 to make 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 neural networks has never been higher, the aim of this study is to investigate how different neural network algorithms behave when analysing a database of biometric information (keystroke and mouse dynamics) regarding the game League of Legends, and how those algorithms are affected by how frequently a sample is collected.